Issue 
A&A
Volume 593, September 2016



Article Number  A71  
Number of page(s)  25  
Section  Numerical methods and codes  
DOI  https://doi.org/10.1051/00046361/201628122  
Published online  23 September 2016 
Uncertainties and biases of source masses derived from fits of integrated fluxes or image intensities
Laboratoire AIM Paris–Saclay, CEA/DSM–CNRS–Université Paris Diderot, IRFU, Service d’Astrophysique, Centre d’Études de Saclay, Orme des Merisiers, 91191 GifsurYvette, France
email: alexander.menshchikov@cea.fr
Received: 13 January 2016
Accepted: 12 June 2016
Fitting spectral distributions of total fluxes or image intensities are two standard methods for estimating the masses of starless cores and protostellar envelopes. These mass estimates, which are the main source and basis of our knowledge of the origin and evolution of selfgravitating cores and protostars, are uncertain. It is important to clearly understand sources of statistical and systematic errors stemming from the methods and minimize the errors. In this modelbased study, a grid of radiative transfer models of starless cores and protostellar envelopes was computed and their total fluxes and image intensities were fitted to derive the model masses. To investigate intrinsic effects related to the physical objects, all observational complications were explicitly ignored. Known true values of the numerical models allow assessment of the qualities of the methods and fitting models, as well as the effects of nonuniform temperatures, farinfrared opacity slope, selected subsets of wavelengths, background subtraction, and angular resolutions. The method of fitting intensities gives more accurate masses for more resolved objects than the method of fitting fluxes. With the latter, a fitting model that assumes optically thin emission gives much better results than the one allowing substantial optical depths. Temperature excesses within the objects above the massaveraged values skew their spectral shapes towards shorter wavelengths, leading to masses underestimated typically by factors 2−5. With a fixed opacity slope deviating from the true value by a factor of 1.2, masses are inaccurate within a factor of 2. The most accurate masses are estimated by fitting just two or three of the longest wavelength measurements. Conventional algorithm of background subtraction is a likely source of large systematic errors. The absolute values of masses of the unresolved or poorly resolved objects in starforming regions are uncertain to within at least a factor of 2−3.
Key words: stars: formation / infrared: ISM / submillimeter: ISM / methods: data analysis / techniques: image processing / techniques: photometric
© ESO, 2016
1. Introduction
Significant technological advances in the astronomical instrumentation during the last four decades enabled measurements of the farinfrared thermal dust emission (usually optically thin in that wavelength range) and hence estimates of the masses of dusty objects. Fitting the farinfrared and submillimeter flux or intensity distributions of optically thin sources can give their average temperatures and masses (Hildebrand 1983). This simple method has become standard in studies of Galactic star formation and a major source of our knowledge of the physical properties and evolution of selfgravitating cores and protostars. Although there are more sophisticated approaches (e.g., Kelly et al. 2012), a simple fitting of the observed spectral shapes remains the most widely used method in the observational studies of star formation (e.g., Könyves et al. 2015). Its inaccuracies, biases, and limitations need to be carefully investigated before reliable conclusions can be made on the physical properties and evolution of the observed objects.
Mass derivation from fitting total fluxes or pixel intensities involves a strong assumption of a constant temperature within an object. In addition to such poorly known parameters as the distance, the farinfrared opacity and its powerlaw slope, and the dusttogas mass ratio, the most problematic assumption is that a single color temperature obtained from the fitting is a good approximation of the massaveraged physical dust temperatures. This may be true for only the simplest case of the lowest density starless cores, transparent in the visible wavelength range and thus practically isothermal, but it is clearly invalid for the protostellar envelopes that are centrally heated by accretion luminosity. Very sensitive dependence of the emission of dust grains on their temperature warrants careful investigation of the effects of nonuniform temperatures. There are papers that have investigated some aspects of the problem, notably the correlation between the estimated temperatures and powerlaw opacity slopes (e.g., Shetty et al. 2009a,b; Juvela & Ysard 2012, and references therein) and the inaccuracies of mass derivation and their effect on the resulting core mass function (Malinen et al. 2011).
The present purely modelbased study simplifies the problem by removing the “observational layer” between the physical reality and observers. To investigate the intrinsic effects related to the physical objects, all observational intricacies (the complex filamentary backgrounds, instrumental noise, calibration errors, different angular resolutions across wavebands, etc.) were explicitly ignored. Measurement errors in intensities and fluxes are assumed to be nonexistent and the radiation emitted by the model objects is known to a high precision, limited only by their numerical accuracy. A grid of radiative transfer models of starless cores and protostellar envelopes was computed and their total fluxes and image intensities were fitted to derive the model masses. Known true values of the numerical models allow us to assess the qualities of the methods and fitting models, as well as the effects of nonuniform temperatures, farinfrared opacity slope, selected subsets of wavelengths, background subtraction, and angular resolutions. The main goal was to quantify how much the mass derivation methods are affected, what the realistic uncertainties of the temperatures and masses are, and what one could possibly do to improve the estimates. Although this study is completely independent of the instruments and wavebands used in actual observations, it employs six Herschel wavebands (70, 100, 160, 250, 350, and 500 μm; Pilbratt et al. 2010), for which a wealth of recent results in star formation has been obtained (e.g., Könyves et al. 2015, and references therein).
Fig. 1 Opacities of grains (per gram of dust) and model radial optical depths. Subscripts on the curve labels (n_{30} to n_{0.03}) indicate the model mass M (in M_{⊙}). The wavelength dependence of κ_{sca}, κ_{abs}, and κ_{ext} is shown by thick solid lines (see Sect. 2.1 for details). The extinction optical depths τ_{ext} of the protostellar envelopes (L_{⋆} = 0.3 L_{⊙}) and starless cores are indicated respectively by the three sets of dashed blue and red lines. 

Open with DEXTER 
The radiative transfer models of starless cores and protostellar envelopes are presented in Sect. 2, the methods of mass derivation from fitting farinfrared and submillimeter observations are introduced in Sect. 3, the results of this work are presented in Sect. 4 and discussed in Sect. 5, the conclusions are outlined in Sect. 6, and further details are found in Appendices A–E.
2. Radiative transfer models
The models were computed with the 3D Monte Carlo radiative transfer code RADMC3D by C. Dullemond^{1}. Spherical model geometry was chosen to simplify the problem by reducing the number of free parameters involved in the study: asymmetries in model density distribution would introduce dependence on viewing angle (e.g., Men’shchikov & Henning 1997; Men’shchikov et al. 1999; Stamatellos et al. 2004) and hence increase the uncertainties of derived parameters. Isotropic scattering by dust grains was considered.
Grids of models for starless cores and protostellar envelopes were constructed, covering the ranges of masses M (0.03−30M_{⊙}) and luminosities L (0.03−30 L_{⊙}) relevant for both low and intermediatemass star formation. The masses and luminosities were sampled at the values of 0.0316, 0.1, 0.316, 1, 3.16, 10, 31.6 (separated by a factor of ); for simplicity, they will be referred to as 0.03,0.1,0.3,1,3,10,30 (M_{⊙}, L_{⊙}). Although the luminosity of an accreting protostar depends on its mass, the goal is to separate the effects of masses and luminosities.
In addition to isolated models, their embedded variants were constructed by implanting the isolated models into the centers of larger spherical background shells of uniform densities, in order to simulate the fact that stars form within their dense parental clouds that shield the embedded objects from the interstellar radiation field. All models were put at a distance D = 140 pc of the nearest starforming regions.
Fig. 2 Density structure of the model starless cores and protostellar envelopes. Subscripts on the curve labels (n_{30} to n_{0.03}) indicate the model mass M (in M_{⊙}). The dashed vertical line shows the outer boundary radius R = 10^{4} AU for all models. Embedded models are implanted in larger uniformdensity clouds with an outer boundary at 3 × 10^{4} AU. The dashed horizontal lines continue the densities of starless cores within the innermost radial zone. The dashed diagonal lines continue the densities of protostellar envelopes towards the radius of the inner dustfree cavity R_{0}, whose size depends on the model temperature profile T_{d}(r) (cf. Fig. 3) and adopted dust sublimation temperature (T_{S} = 10^{3} K). In other words, the dashed diagonal lines visualize the range of densities and radial distances over which the inner boundary R_{0} is located for L_{⋆} spanning the entire range 0.03 − 30 L_{⊙} (see Eq. (1)). 

Open with DEXTER 
2.1. Dust properties
Properties of the real astrophysical dust grains are poorly known and they are unlikely to be universal in the different starforming regions observed. The standard mass derivation methods ignore many complications related to the cosmic dust grains, assuming just a simple powerlaw opacity across all bands being fitted. For example, the presence of very small, stochastically heated grains is neglected (e.g., Desert et al. 1990); the contribution of these grains to the emission of starless cores and protostellar envelopes can become significant at λ ≲ 100 μm (e.g., Bernard et al. 1992; Siebenmorgen et al. 1992). For consistency with the mass derivation methods and previous studies of star formation, this model study adopts tabulated absorption opacities κ_{abs} for grains with thin ice mantles (Ossenkopf & Henning 1994), corresponding to coagulation time t = 10^{5} yr and number density n_{H} = 10^{6} cm^{3} (Fig. 2).
The opacity values at long wavelengths λ> 70 μm were replaced with a power law κ_{λ} ∝ λ^{2}; the modification aimed at testing the widely used assumption on the powerlaw farinfrared opacities . At short wavelengths (0.1<λ< 1 μm), the opacities were extrapolated with a power law κ_{λ} ∝ λ^{0.87} based on the last tabulated values. Although dust scattering is unimportant in the farinfrared, scattering opacities were constructed to resemble the values and wavelength dependence κ_{sca} ∝ λ^{4} of typical dust grains. The resulting dust opacity at λ> 70 μm was parameterized by κ_{0} = 9.31 cm^{2} g^{1} (per gram of dust), λ_{0} = 300 μm, and β = 2, with the maximum opacities limited by 10^{5} cm^{2} g^{1} (Fig. 1).
2.2. Density distributions
The density structure of starless cores was approximated by an isothermal BonnorEbert sphere (Bonnor 1956) with a temperature of 7 K and a central density of 5.2 × 10^{18} g cm^{3}. This somewhat arbitrary choice of ρ(r) gives just a simple and convenient functional form (Fig. 2) resembling the observed flattopped density profiles of starless cores (e.g., Alves et al. 2001; Evans et al. 2001). The issue of the gravitational instability (or stability) of the model cores is irrelevant for this study of the mass derivation methods. Protostellar envelopes were modeled as infalling spherical envelopes with the powerlaw densities ρ(r) ∝ r^{2} (e.g., Larson 1969; Shu 1977) around a central source of accretion energy (Fig. 2).
Model dust densities were scaled to obtain the desired grid of masses 0.03,0.1,0.3,1,3,10, and 30 M_{⊙} using the standard dusttogas mass ratio η = 0.01. The outer boundary of all the models was placed at the same distance of R = 10^{4} AU, beyond which their density either changed to zero (isolated models) or remained constant until R_{E} = 3 × R (embedded models). The embedding cloud density was set equal to ρ(R) (Fig. 2), which corresponds to the denser models (i.e., more massive) being formed in a denser environment. Most of the mass of the model starless cores and protostellar envelopes (96% and 90%, respectively) is contained in their outer parts (0.1 R<r<R).
For the starless cores, the inner boundary was arbitrarily set to R_{0} = 50 AU, as their densities are essentially constant and hence do not need to be resolved at smaller radii. The inner boundary of the dusty protostellar envelopes is defined by the dust sublimation temperature T_{S} ~ 10^{3} K. An exact value of T_{S} depends on the chemical composition and sizes of dust grains and so does the radius R_{0} of the inner dustfree cavity. For the purpose of this study, it is adequate to adopt a single value T_{S} = 10^{3} K. With the model κ_{ν} and ρ(r) (Sect. 2.1), the resulting radiativeequilibrium temperatures (Fig. 3) lead to the inner boundaries of the dusty protostellar envelopes that are fairly accurately described by a simple formula, (1)The model space between the inner and outer boundaries was discretized by nonuniform grids with the relative zone sizes δlog r that smoothly varied from 0.6 to 0.02 (100−150 zones) for starless cores and from 0.002 to 0.06 (200−300 zones) for protostellar envelopes.
Fig. 3 Radiativeequilibrium dust temperature profiles of starless cores (upper) and protostellar envelopes (lower) for the isolated models (left) and their embedded variants (right). Subscripts of the curve labels (T_{30} to T_{0.03}) indicate the model mass M (in M_{⊙}). The dashed horizontal lines in the upper panels continue the profiles of starless cores within the innermost radial zone. The dashed vertical line shows the outer boundary radius R = 10^{4} AU of all models. The maximum temperature and the inner boundary of the dusty protostellar envelopes are defined by the adopted dust sublimation temperature T_{S} = 10^{3} K. Three dashed horizontal lines in the lower panels indicate the range of radial distances over which the boundaries R_{0} of the dustfree cavities are located for the model luminosities in the entire range of 0.03 − 30 L_{⊙} (see Fig. 2). For protostellar envelopes with the same M, the “hotter” profiles correspond to higher L_{⋆}, larger R_{0} (cf. Eq. (1)), and lower radial optical depths . Two dashed lines bracketing the profiles of protostellar envelopes indicate the slopes T_{d}(r) ∝ r^{0.88} and ∝ r^{− 1/3}, the latter describing the temperatures of a transparent dusty envelope with the adopted grain properties (see also Appendix A). Vertically aligned filled circles indicate the massaveraged temperature T_{M} for each model, defined by Eq. (4). The slight wiggling of some profiles around their minima reflects the discrete nature of the Monte Carlo radiative transfer method. 

Open with DEXTER 
Fig. 4 Spectral energy distributions starless cores (upper) and protostellar envelopes (lower). Shown are the backgroundsubtracted fluxes for the isolated models (left) and their embedded variants (right). Subscripts of the curve labels (F_{30} to F_{0.03}) indicate the model mass (in M_{⊙}). For protostellar envelopes of the same mass, the SEDs with higher fluxes correspond to higher accretion luminosities L_{⋆} (0.03, 0.1, 0.3, 1, 3, 10, 30 L_{⊙}). Dashed lines indicate the fluxes of the ISRF that were integrated over the projected area of either the isolated models or the embedding clouds. 

Open with DEXTER 
2.3. Radiation sources and optical depths
All models were illuminated from the outside by an isotropic interstellar radiation field (Black 1994) with the “strength” parameter G_{0} = 1 (e.g., Parravano et al. 2003). The bolometric luminosity of the interstellar radiation field (ISRF) entering the isolated models at R amounted to L_{ISRF} = 1 L_{⊙}, whereas that crossing the boundary of embedding clouds at R_{E} was 9 L_{⊙}.
In addition to the external radiation field, the models of protostellar envelopes were assumed to be heated at their centers by a blackbody source of luminosity L_{⋆} of 0.03,0.1,0.3,1,3,10, and 30 L_{⊙} with an effective temperature of T_{⋆} = 5770 K. Actual values of T_{⋆} are unimportant, as the sources of accretion energy are surrounded by the completely opaque dusty envelopes reprocessing the hot radiation to T ≲ 10^{3} K very deep in their interiors.
Distribution of optical depths within dusty envelopes is one of the main parameters (along with the density structure) for the transfer of radiation and resulting radiativeequilibrium temperatures. All models are quite opaque at visible wavelengths, with radial optical depths τ_{V} ≈ 3−3 × 10^{3} for starless cores and τ_{V} ≈ 500−6 × 10^{5} for protostellar envelopes of different masses and luminosities (Fig. 1). At the farinfrared wavelength of 100 μm, starless cores with M< 3 M_{⊙} are transparent, whereas the ones with M ≳ 3 M_{⊙} are optically thick towards their centers. All protostellar envelopes are optically thick at 100 μm and some of them (with M ≳ 0.3 M_{⊙}) are even opaque at 500 μm towards their centers. Sizes of the dustfree cavities of protostellar envelopes increase with the luminosity of their central energy sources (cf. Eq. (1), Fig. 2), thus the optical depths of the envelopes decrease, approximately as .
High farinfrared optical depths of the model starless cores and protostellar envelopes are localized within relatively small spherical zones around their centers. Angular radii of the opaque dusty zones in the protostellar models can be described (at 70−500 μm) by a simple empirical expression (2)which can also be used (within a factor of 1.5−2) for the highmass models of starless cores of 3,10, and 30 M_{⊙}, in which the opaque zone exists only at λ ≤ 70,160, and 250 μm, respectively.
The density profiles ρ(r) ∝ r^{2} of the protostellar envelopes are similar to those of the starless cores for r ≳ 0.1 R (Fig. 2). Therefore, whenever an inner opaque zone exists in the objects, its mass obeys m(ϑ) ∝ ϑ, hence the fractional mass is the fractional radius and, using Eq. (2), can be written as (3)where ϑ, D, and R are in units of arcsec, pc, and AU, respectively. At λ ≲ 100 μm, the opaque zone of highmass objects extends over a large fraction of their mass. This means that the standard assumption of the farinfrared transparency is severely violated for massive objects. Protostellar envelopes with M< 3 M_{⊙} have small opaque zones that contain little mass and thus they cannot substantially affect the standard methods of mass derivation.
2.4. Temperature distributions
The models of starless cores and protostellar envelopes acquire radiativeequilibrium dust temperatures T_{d}(r) shown in Fig. 3. In the adopted isotropic ISRF, the radiativeequilibrium temperature of dust grains with the model opacities from Fig. 1 is T_{d} = 17.4 K, the value that the isolated models and embedding clouds acquire at their outer boundaries in the limit τ_{λ} → 0. The lower mass models of starless cores are transparent and thus almost isothermal. Their higher mass counterparts develop steeper temperature gradients under the outer boundaries of the isolated models and embedding clouds and lower temperatures in their interiors (Fig. 3).
Displaying the same behavior under their outer boundaries, protostellar envelopes of all masses develop steep temperature gradients towards the inner boundary (Fig. 3). Higher accretion luminosities make the dust hotter and thus, with the adopted dust sublimation temperature T_{S} = 10^{3} K, the boundary of the inner dustfree cavity shifts towards larger radial distances (cf. Eq. (1)). An analytical approximation of the profiles T_{d}(r) for protostellar envelopes can be found in Appendix A.
Differences between the isolated and embedded models are highlighted by their different temperature distributions at the outer model boundary (Fig. 3). The temperatures of embedded models at r = R are significantly lower than those of the isolated models, owing to the absorption of ISRF in the embedding clouds (R<r ≤ R_{E}). The denser the embedding cloud is, the lower T_{d}(R) is and the greater the contrast to the isolated model (Fig. 3). As the bulk of the mass of the models is contained in the outer parts, the differences in the temperature profiles between the isolated and embedded models can greatly affect their observational properties, such as the images and total (integrated) fluxes.
2.5. Spectral energy distributions
After computing the selfconsistent radiativeequilibrium dust temperature distributions T_{d}(r) from the radiative transfer models, observables – such as the intensity maps ℐ_{ν} and total fluxes F_{ν} – were obtained by a raytracing algorithm in separate runs of RADMC3D. Effects of the Monte Carlo noise on F_{ν}, evaluated from the standard deviations about the azimuthally averaged intensity profiles I_{ν}(ϑ), are below 0.003% and 3% for the starless cores and protostellar envelopes, respectively, in all models and wavebands.
To emulate the standard observational procedure of flux measurements, F_{ν} were integrated from the backgroundsubtracted model images ℐ_{ν}. The model background was evaluated as an average intensity within a δR′wide annulus placed just outside the outer model boundary (R′>R). In practice, the annulus was one pixel in width (δR′ = 0.47″) and it was detached from the boundary by one additional pixel. For the isolated models, is the intensity of the isotropic ISRF, whereas for the embedded models, is determined by both and the transfer of radiation in the background cloud (R′ ≤ r ≤ R_{E}) along the rays passing through the annulus. Inaccuracies inherent in the standard algorithm of background subtraction are discussed in Sect. 5.2 and Appendix B.
Spectral energy distributions (SEDs) of the models of starless cores and protostars are shown in Fig. 4. The SED shapes depend on the density and temperature distributions (Figs. 2 and 3). Large differences between the SEDs for the isolated and embedded cores are mainly caused by differences in their temperature profiles near the model boundary. The SEDs of protostellar envelopes are affected by the same effects to a much lesser degree as their density profiles are centrally peaked and their temperature profiles are dominated by the internal radiation source. The SEDs of the models of different masses and luminosities show a large variety of shapes in the farinfrared domain (Fig. 4) due to varying optical depths and temperatures.
Providing a useful reference in our analysis, additional raytracing runs of RADMC3D computed the fluxes of isothermal models. These are the same models described above (Fig. 2), in which selfconsistent temperature profiles (Fig. 3) have been replaced with their massaveraged values: (4)The resulting total fluxes of the isothermal models are denoted F_{ν}(T_{M}).
3. Fitting source fluxes and intensities
In observational studies, after obtaining multiwavelength images ℐ_{ν} and integrating backgroundsubtracted (and deblended) fluxes F_{ν} of extracted sources, their spectral distributions need to be fitted to derive fundamental physical parameters, such as the source mass and luminosity.
The standard technique uses the wellknown formal solution of the radiative transfer equation that can be written as (5)where I_{ν} is the observed specific intensity, T is the homogeneous temperature of an object, B_{ν}(T) is the blackbody intensity, and τ_{ν} is the optical depth of the object. After obtaining an image ℐ_{ν} ≡ I_{νij}, the total flux F_{ν} = ^{∫}ℐ_{ν} dΩ can be integrated over the solid angle Ω subtended by the object. For constant intensity, it reduces to F_{ν} = I_{ν} Ω. A critical assumption used in the derivation of Eq. (5) is that the object is homogeneous in temperature, whereas the temperatures of the astrophysical objects are actually nonuniform (cf. Fig. 3).
Two methods and two fitting models were explored in this work that have been used in observational studies of star formation to estimate source temperatures and masses.
3.1. Fitting total fluxes F_{ν}
In this method, the total fluxes F_{ν} are integrated from backgroundsubtracted and deblended images ℐ_{ν} of source intensities and then are fitted to estimate source mass as one of the fitting parameters. With the adopted parameterization of the powerlaw opacity κ_{ν} = κ_{0}^{(}ν/ν_{0}^{)}^{β}, it is possible to write Eq. (5) in the form (6)where η is the dusttogas mass ratio and D is the source distance. The fitting model of Eq. (6) with five parameters (T, M, β, D, Ω) is referred to as modbody in this paper. After fitting F_{ν} and estimating the model parameters, the average column density N_{H2} can be obtained from M = μm_{H}N_{H2}D^{2}Ω, where μ = 2.8 is the mean molecular weight per H_{2} molecule and m_{H} is the hydrogen mass.
With an additional assumption that measured fluxes F_{ν} represent optically thin emission^{2}, Eq. (6) can be written as (7)The fitting model of Eq. (7) with four parameters (T, M, β, D) is referred to as thinbody in this paper. By the definition (τ_{ν} ≪ 1), it produces only fits with the modified blackbody shapes κ_{ν}B_{ν}(T) that are scaled up or down, depending on M. Obviously, the modbody fits with τ_{ν} ≪ 1 produce the same shapes as the thinbody model does, whereas the modbody fits with τ_{ν} ≫ 1 resemble a blackbody B_{ν}(T). In the intermediate (semiopaque) cases, the shortwavelength parts of the fitted curves can be described by B_{ν}(T) while morphing into κ_{ν}B_{ν}(T) at long wavelengths where the radiation becomes optically thin. With more flexible shapes, modbody can give better fits of the data, but it does not necessarily lead to good estimates of temperatures and masses.
After fitting fluxes with a modbody or thinbody model, an estimate of T_{F} and the corresponding mass M_{F} are obtained. For the realistic objects with strongly nonuniform temperatures T_{d}(r) (Fig. 3), emerging fluxes F_{ν} are heavily distorted from the simple shapes of the fitting models, hence these models are inadequate and an estimate of T_{F} does not guarantee that M_{F} is close to the true mass M. For the purpose of obtaining accurate M_{F} ≈ M, it is necessary (but not sufficient) to have T_{F} ≈ T_{M}, i.e., it is possible to interpret T in Eq. (7) as the massaveraged T_{M} from Eq. (4). In fact, assuming τ_{ν} ≪ 1 in the farinfrared, the observed fluxes contain emission of all dust grains, which is proportional to the mass of dust at different T_{d} in the entire volume of an object: (8)Equations (7) and (8) can immediately be combined into a definition of the massaveraged intensity B_{νM}. Since B_{ν}(T) ∝ T in the RayleighJeans domain, the equations are also readily converted into T_{M} from Eq. (4). In the model objects studied here, differences between B_{νM} and B_{ν}(T_{M}) quickly become negligible beyond the peak wavelength of the latter (λ≳2 λ_{peak}). Therefore, T_{M} is fully consistent with the fitting models at long wavelengths.
3.2. Fitting image intensities I_{ν}
In this method, it is possible to fit pixel intensity distributions I_{νij} of the backgroundsubtracted and deblended images ℐ_{ν} of a source^{3}, to derive a map of its column densities and then the source mass . It is convenient to express the modbody and thinbody models from Eqs. (6) and (7) as functions of the pixel column density N_{H2}: In this formulation, both models have only three fitting parameters (T, N_{H2}, β) in contrast to the case where total fluxes are fitted (five parameters for modbody in Eq. (6) and four parameters for thinbody in Eq. (7); see Sect. 3.1). Furthermore, limited angular resolutions of real images makes the results of fitting I_{ν} depend sensitively on the degree to which a source is resolved.
For fully resolved sources, such as the model objects used in this work, relatively small pixels sample completely independent intensities from different rays. For progressively lower angular resolutions, intensities within a beam become increasingly blended together. Radiation with different temperatures gets mixed not only along the line of sight, but also in the transverse directions, in the plane of the sky. For unresolved objects with intrinsic temperature gradients, radiation from the entire object becomes heavily blended, leading to strong distortions of their spectral intensity distributions.
An important assumption used in the derivation of N_{H2ij} is a constant temperature T_{d}(x,y,z) along the lines of sight within a certain radial distance from a pixel (i,j). The distance depends on the angular resolution of images: for less resolved sources, temperatures from a larger environment of the pixel contribute to its intensity. With low optical depths τ_{ν} ≪ 1 in the farinfrared, emission is observed from the entire column of dust grains at (i,j) with different temperatures T_{d}(z) along the line of sight. The reasoning associated with Eq. (8) can be applied to show that T in Eq. (10) is consistent with the columnaveraged temperature (11)A massaveraged temperature, equivalent to that from Eq. (4), can be obtained as .
3.3. Variable and fixed parameters
In most studies, the opacity slope β has been kept fixed in the fitting process to reduce the number of free parameters and improve the robustness of derived parameters. Following this practice, Sect. 4 presents and discusses only the results of fitting with a fixed opacity slope. When fitting intensities I_{ν} with β fixed, the number of free variable parameters becomes γ = 2 for both thinbody and modbody models (T, N_{H2}). When fitting fluxes F_{ν}, distance D is also assigned a fixed value to further reduce the degrees of freedom, although astronomical distances are poorly known. The number of free variable parameters is thus γ = 2 for thinbody (T, M) and γ = 3 for modbody (T, M, Ω).
In practice, after measuring F_{ν} = ^{∫}I_{ν} dΩ, the solid angle Ω over which I_{ν} were integrated is known^{4} and its value can be fixed, reducing γ for modbody to two free variables (T, M). In this model study, one could also keep Ω = π (R/D)^{2} constant, as the true values of R and D are known; however, modbody would then become completely equivalent to thinbody. Indeed, fixing Ω of transparent objects at accurate (or even overestimated) values means that the optical depths in Eq. (6) are very small (τ_{ν} = κ_{ν}ηMD^{2}Ω^{1} ≪ 1), which effectively converts modbody into thinbody. Only when fixing strongly underestimated values Ω ≪ π (R/D)^{2}, the farinfrared τ_{ν} become large enough to produce any noticeable differences between modbody and thinbody. This work investigates qualities of two different models, hence Ω was allowed to vary in all modbody fits of F_{ν}.
When fitting pixel intensities I_{ν} instead of F_{ν}, the farinfrared τ_{νij} within most of the image pixels (i,j) are small, even for perfectly resolved sources. For poorly resolved or unresolved sources, radiation within the beams gets diluted and maximum values of τ_{νij} in the images become smaller. All models of starless cores and protostellar envelopes contain 96% and 90% of their masses, respectively, in their outer parts (r ≳ 0.1 R, Fig. 2). Intensities in the outer parts of the source images come mostly from the pixel columns of dust with τ_{νij} ≪ 1 in the farinfrared. Only in the models with M ≳ 3 M_{⊙} do they become substantially affected by the radiation from the central opaque zone (Sect. 2.3). As a result, the masses derived from fitting I_{ν} are almost the same (within ~20%) for both fitting models and hence only the thinbody results are presented for this method.
3.4. Data points and their subsets
Fitting was executed for a set of the total model fluxes {F_{λi}} or pixel intensities {I_{λi}}(i = 1,2,...,6) at the Herschel wavelengths λ_{i} of 70, 100, 160, 250, 350, and 500 μm. In this modelbased study, the intensities and fluxes of numerical models have essentially no measurement errors. It makes sense, however, to make their uncertainties resemble typical observational values, hence to get an idea of realistic inaccuracies of the estimated parameters (masses, temperatures). Before the fitting, the model intensities and fluxes were assigned an additional (optimistic) uncertainty of 15%, a value similar to the levels of calibration errors in real observations (e.g., with Herschel). The above uncertainties were associated with the exact data points to see how typical data uncertainties translate into the resulting error bars of the derived parameters. Extra uncertainties come from the fact that the dusttogas ratio η, reference opacity κ_{0}, and distance D, which are used in the fitting models (Sects. 3.1, 3.2) but held constant, are actually poorly known. Conservatively assuming that the quantities have random and independent uncertainties of 20%, the latter were added in quadrature to those of the derived masses, for the same purpose of obtaining the total resulting mass uncertainties.
To isolate the effects of temperature gradients in starless cores and protostellar envelopes (Fig. 3), the fitting was done for several subsets of data, removing some (or none) of the shortestwavelength points from the fitting process. The data subsets are denoted Φ_{n} = {Y_{λi}}, where Y_{λi} is either F_{λi} or I_{λi} and n is the number of the longest wavelengths used in the fitting^{5}. Fits of total fluxes were considered successful (acceptable) and their results are shown below, if χ^{2} ≤ n−γ + 1, with the last term added to allow testing χ^{2} for zero degrees of freedom (n = γ). Fits of image intensities were considered successful, if the same goodness condition was fulfilled in all pixels within an object. These results, as well as the somewhat less reliable results with n−γ + 1 <χ^{2}< 10 in some pixels are presented below. Details of the fitting algorithm can be found in Appendix C.
Fig. 5 Fluxes of the isolated starless cores (upper) and protostellar envelopes (lower) fitted with the thinbody (left) and modbody (right) models. The fluxes for the 0.03, 0.3, and 3 M_{⊙} cores and envelopes (with L_{⋆} = 1 L_{⊙}) from Fig. 4 are shown as black triangles, squares, and circles, respectively. Successful fits (Sect. 3.4) using different subsets Φ_{n} of fluxes are indicated by the solid and dashed lines of different widths and colors. The thin black curves show the flux distributions for the isothermal models where the actual model temperatures (Fig. 3) were replaced with their massaveraged values: T_{d}(r) = T_{M} (16.3,13.2,9.2 K for starless cores and 18.1,15.6,12.3 K for protostellar envelopes). 

Open with DEXTER 
4. Results
This section describes derived parameters for both starless cores and protostellar envelopes, obtained from acceptable fits for all subsets Φ_{n} (Sect. 3.4) for both modbody and thinbody (Sect. 3.1). Results for the isothermal models are presented in Appendix D. To evaluate the effects of the uncertain farinfrared opacity slope, results are shown for β = 2 used in the radiative transfer modeling and for two other β values (1.67, 2.4), differing from the true value by a factor of 1.2. Results obtained with variable fitting parameter β are described in Appendix E.
Masses derived from fitting images ℐ_{ν} of objects with temperature gradients must depend on their angular resolutions (Sect. 3.2). To investigate this effect, the model images with pixels of 0.47″ were convolved with Gaussian beams of 1, 36, and 144″ (FWHM) and then resampled to 1, 12, and 48″ pixels, respectively. For the objects with diameters of 142″ (2 × 10^{4} AU, Fig. 2), the three variants represent resolved, partially resolved, and unresolved cases.
In this paper, the term uncertainties refers to the error bars of measured or derived quantities, the term inaccuracies (sometimes simply errors) refers to the deviations of the derived quantities from their model values, and the term biases denotes variable systematic dependences of inaccuracies across the ranges of model parameters (M, L, T_{M}).
4.1. Selected examples
Examples of the fits of F_{ν} for the isolated starless cores and protostellar envelopes with masses of 0.03, 0.3, and 3 M_{⊙} are shown in Fig. 5. Although the qualitatively similar plots for embedded models are not presented, their derived parameters and uncertainties are described in Sect. 4.2.
Flux distributions of the isolated starless cores (Fig. 4) are similar to those of the modified blackbodies κ_{ν}B_{ν}(T_{M}). The fits for a lowmass core with M = 0.03 M_{⊙} shown in Fig. 5 are identical for all subsets Φ_{n} since the core is nearly isothermal, with T_{d}(r) very similar to its T_{M} = 16.3 K. Fluxes of the highermass cores of 0.3 and 3 M_{⊙} display larger deviations from the fluxes F_{ν}(T_{M}) of isothermal models for larger subsets Φ_{n} (n = 3 → 6). The shapes of F_{ν} become “hotter” because of the steeper temperature profiles (Sect. 2.4) at the outer boundary (Fig. 3). For a massive core of 3 M_{⊙}, discrepancies between F_{ν} and F_{ν}(T_{M}) at λ ≲ 160 μm reach factors ≳ 5.
Fig. 6 Temperatures T_{F} and masses M_{F} derived from fitting F_{ν} of both isolated and embedded starless cores vs. the true model values of T_{M} and M for three β values (1.67, 2, 2.4). For various subsets Φ_{n} of fluxes, results from successful thinbody and modbody fits (Sect. 3.4) are displayed by the colored and gray lines, respectively. Error bars represent the 1 × σ uncertainties of the derived parameters returned by the fitting routine combined with the assumed ± 20% uncertainties of η, κ_{0}, and D (Sect. 3.4). The black solid lines show the locations where T_{F} and M_{F} are equal to the true values. To preserve clarity of the plots, much less accurate modbody results are displayed only for correct β = 2 and without error bars. 

Open with DEXTER 
Fig. 7 Temperatures T_{F} and masses M_{F} derived from fitting F_{ν} of both isolated and embedded protostellar envelopes (with the true masses M of 0.03, 0.3, and 3M_{⊙}) vs. the true model values of T_{M} and L for three β values (1.67, 2, 2.4). Results from successful thinbody and modbody fits for various subsets Φ_{n} of fluxes (Sect. 3.4) are displayed by the colored and gray lines, respectively. See Fig. 6 for more details. 

Open with DEXTER 
Flux distributions of an isolated protostellar envelope with M = 0.03 M_{⊙} (Fig. 4) display various shapes that are quite different from those of κ_{ν}B_{ν}(T_{M}), whereas for a more opaque envelope of 3 M_{⊙} they become similar to the modified blackbody shapes. The protostellar fits (Fig. 5) show greater deviations for larger subsets Φ_{n} (n = 3 → 6), much larger than those of starless cores. Differences between F_{ν} and F_{ν}(T_{M}) reach orders of magnitude at λ ≲ 100 μm. The shapes appear much “hotter” owing to T_{d} ~ 100−10^{3} K (Sect. 2.4) deep inside the envelopes (Fig. 3). The lowermass protostellar envelopes are more transparent and the hot emission greatly distorts F_{ν} at λ ≲ 250 μm.
4.2. Properties derived from fitting fluxes F_{ν}
Isolated starless cores, β = 2 (Fig. 6). For the lowmass, transparent cores (M → 0.03 M_{⊙}), quite accurate values T_{F} ≈ T_{M} and M_{F} ≈ M are derived for all subsets Φ_{n}. For the denser, more opaque cores (M → 30 M_{⊙}), derived T_{F} and M_{F} become more over and underestimated, respectively, as the spectral shapes of F_{ν} become much wider and distorted towards shorter wavelengths (Fig. 4). The biases and inaccuracy of the estimates depend on the subset Φ_{n}, with the least inaccurate T_{F} and M_{F} obtained for the thinbody fits of Φ_{2}. However, the biases of the parameters across the entire mass range remains fairly strong. Derived masses of the starless cores are underestimated within a factor of 2 for 1 <M ≤ 3 M_{⊙} and factor of 5 for 3 <M ≤ 30 M_{⊙}.
Embedded starless cores, β = 2 (Fig. 6). For the lowmass, transparent cores (M → 0.03 M_{⊙}), M_{F} are underestimated by a factor of 1.35 for all subsets Φ_{n}, although T_{F} are quite accurate because the standard observational procedure of background subtraction ignores the fact that embedding backgrounds tend to be rimbrightened at their outer boundary R (Appendix B, Sect. 5.2). The embedded cores have T_{d}(r) that are quite flat across their boundary for all masses (Fig. 3). Having no flux distortions caused by nonuniform temperatures (Fig. 4), the F_{ν} peaks of the most massive cores (M → 30 M_{⊙}) move towards the longest wavelength (λ_{6} = 500 μm), which leads to T_{F} and M_{F} that are under and overestimated, respectively.
Isolated protostellar envelopes, β = 2 (Fig. 7). Emission of the hot dust with T_{d} ~ 100−10^{3} K greatly skews their F_{ν} towards shorter wavelengths (Fig. 4). This becomes especially significant for the lower mass, more transparent envelopes (M → 0.03 M_{⊙}, L_{⋆} → 30 L_{⊙}) that produce hotter dust over a much larger volume (Fig. 3). The thinbody fits of larger subsets Φ_{n} (n = 3 → 6), lead to errors in T_{F} and M_{F} that reach factors of 1.6 and 3, respectively. The smallest subset Φ_{2} is unaffected by the hot emission and it produces fairly accurate thinbody estimates of T_{F} and M_{F} (for all M and L_{⋆}) within factors of 1.1 and 1.3, respectively.
Embedded protostellar envelopes, β = 2 (Fig. 7). Results are qualitatively similar to those of the isolated envelopes, although with larger inaccuracies. Derived M_{F} are underestimated by at least a factor of 1.5, mostly due to oversubtraction of the rimbrightened embedding background (Appendix B, Sect. 5.2). Although the envelopes have T_{d}(r) that are quite flat across their boundaries (Fig. 3), their derived parameters are greatly affected by the skewed F_{ν} owing to the hot dust deep in their interiors. The thinbody fits of large subsets Φ_{n} (n = 3 → 6) lead to inaccuracies in T_{F} and M_{F} as large as factors of 1.4−1.8 and 3−5, respectively. The most accurate T_{F} and M_{F}, obtained for the smallest subset Φ_{2}, are underestimated within factors of 1.2 and 2.
Effects of the adopted opacity slope β on the estimated parameters are similar for both starless cores (Fig. 6) and protostellar envelopes (Fig. 7). Although detailed behavior of the differences with respect to the above results for true β = 2 depends on the subset Φ_{n}, clear general trends can be seen. Shallower slopes (β = 1.67) lead to an increase in T_{F} and thus M_{F} becomes smaller, whereas steeper slopes (β = 2.4) lead to a decrease in T_{F} and hence M_{F} becomes larger, in both cases by a factor of approximately 2.
The thinbody fitting model produces much better overall results than modbody does. Parameters estimated with modbody become so incorrect that they may be considered completely unusable. The importance of estimating accurate massaveraged temperatures T_{M} for deriving correct masses M_{F} is illustrated by the isothermal models presented in Appendix D.
4.3. Properties derived from fitting images ℐ_{ν}
This section presents results for both starless cores and protostellar envelopes, obtained from successful fits of the backgroundsubtracted ℐ_{ν} for all subsets Φ_{n}, for only the thinbody fitting model. Derived modbody masses are practically the same as the thinbody masses, because the bulk of the model mass is in optically thin regions (Sect. 3.3). Effects of the adopted farinfrared opacity slopes are the same as when fitting F_{ν} (Sect. 4.2): under or overestimating β by a factor of 1.2 gives masses M_{ℐ} that are systematically under or overestimated by a factor of 2. The method of fitting images ℐ_{ν}, thereby deriving , and afterwards integrating source mass M_{ℐ} brings clear benefits for wellresolved starless cores with nonuniform temperatures, compared to the other method (Sect. 4.2) of first integrating total fluxes F_{ν} from ℐ_{ν} (losing all spatial information) and then estimating M_{F} from the fitting model.
Isolated starless cores, β = 2 (Fig. 8). For the fully resolved models, derived T_{ℐ} and M_{ℐ} have fairly good accuracy and little bias for acceptable fits, although the range of the latter for larger Φ_{n} (n = 3 → 6) shrinks to the lowest masses. As the transparent lowmass cores (M → 0.03 M_{⊙}) are almost isothermal, derived T_{ℐ} and M_{ℐ} perfectly agree with T_{M} and M for any subset Φ_{n}. Massive cores with more variable T_{d}(r) (Fig. 3) also have significant variations of T_{d}(z) along the line of sight at pixel (i,j). Emission of hot dust skews the spectral shapes of I_{νij} towards shorter wavelengths, even more so at the highmass end. The most accurate masses are obtained for Φ_{2}, whereas larger Φ_{n} (n = 3 → 6) give increasingly incorrect T_{ℐ} and M_{ℐ}. With degrading angular resolutions, the inaccuracies and biases increase, especially for M → 30 M_{⊙} and larger Φ_{n} (n = 3 → 6). As expected, in the limiting case of unresolved objects the results approach those obtained with the method of fitting fluxes F_{ν} (Fig. 6).
Embedded starless cores, β = 2 (Fig. 8). For the fully resolved models, derived T_{ℐ} have fairly good accuracy and little bias for the acceptable fits, although the range of the latter for larger Φ_{n} (n = 3 → 6) shrinks to even lower masses than for the isolated models. Showing no particularly large bias over almost the entire range of model masses, M_{ℐ} are underestimated by a factor of 1.3 owing to the standard observational procedure of background subtraction (Appendix B, Sect. 5.2). Derived parameters of the models do not depend on angular resolutions, as they have relatively flat T_{d}(r) across their boundaries (Fig. 3), hence the spectral distortions of I_{νij} are negligible.
Fig. 8 Temperatures T_{ℐ} and masses M_{ℐ} derived from fitting images ℐ_{ν} of both isolated and embedded starless cores vs. the true model values of T_{M} and M for correct β = 2. The three columns of panels present results for three angular resolutions (resolved, partially resolved, and unresolved cases) and for various subsets Φ_{n} of pixel intensities. Error bars represent the 1 × σ uncertainties of the derived T_{ℐ} and M_{ℐ} (computed over all pixels as the N_{H2}averaged errors of T_{Nij} and integrated errors of N_{H2}, correspondingly), combined with the assumed ± 20% uncertainties of η, κ_{0}, and D (Sect. 3.4). Less reliable results (n−γ + 1 <χ^{2}< 10 in some pixels) are shown without error bars. See Fig. 6 for more details. 

Open with DEXTER 
Fig. 9 Temperatures T_{ℐ} and masses M_{ℐ} derived from fitting images ℐ_{ν} of both isolated and embedded protostellar envelopes (with the true masses M of 0.03,0.3, and 3 M_{⊙}) vs. the true model values of T_{M} and L for correct β = 2. The three columns of panels present results for three angular resolutions indicated (resolved, partially resolved, and unresolved cases). See Fig. 8 for more details. 

Open with DEXTER 
Isolated protostellar envelopes, β = 2 (Fig. 9). For the fully resolved models, derived T_{ℐ} and M_{ℐ} are very accurate across all masses and luminosities. With degrading angular resolutions and for larger Φ_{n} (n = 3 → 6) the inaccuracies and biases increase quite considerably. The accretion energy released in the envelopes centers heats the dust to T_{S} ~ 10^{3} K, making T_{d}(r) strongly nonuniform. For the lines of sight passing through the inner radial zones, the hot emission skews the I_{νij} shapes towards shorter wavelengths. For the unresolved envelopes, the results become similar to those obtained with the method of fitting fluxes F_{ν} (Fig. 7).
Embedded protostellar envelopes, β = 2 (Fig. 9). For the fullyresolved models, derived T_{ℐ} are slightly less accurate for the acceptable fits than in the case of the isolated envelopes. The range of the latter in more massive models for larger Φ_{n} (n = 3 → 6) shrinks towards higher L_{⋆}. The most accurate M_{ℐ}, obtained for Φ_{2}, are underestimated by a factor of 1.45, mostly because of the oversubtraction of the rimbrightened background (Appendix B, Sect. 5.2). For the partially resolved and unresolved envelopes, the most accurate M_{ℐ} (for Φ_{2}) are underestimated by factors of 1.5−2, whereas fitting larger Φ_{n} (n = 3 → 6) leads to errors by factors of 4−5.
5. Discussion
Spectral flux and intensity distributions of the radiative transfer models of the starless cores and protostellar envelopes (0.03−30 M_{⊙},L_{⊙}) were fitted using the modbody and thinbody models. Derived values of the fitting parameters were then compared to their true values to quantify the qualities of the mass derivation methods, fitting models, and various sources of errors.
As shown in Sect. 4, large intrinsic inaccuracies and biases need to be taken into account when applying the methods of mass derivation to the observed sources. In addition to being affected by nonuniform temperatures, estimated masses are also affected by the adopted value of β and subset of data points Φ_{n}, as well as by the removal algorithm of the background emission of an embedding cloud. In the method of fitting fluxes F_{ν}, the masses depend on the fitting model, whereas in the method of fitting images ℐ_{ν}, they depend on the angular resolution.
The results of this purely modelbased work discussed below may be directly applicable only to sources with very accurate measurements (with negligible errors). Real observations deal with images of relatively faint, crowded sources on strong and variable backgrounds, obtained with quite different angular resolutions, and thus they carry much larger measurement errors. Observations are substantially affected by various statistical and systematic errors, depending on the adopted source extraction method (e.g., Men’shchikov et al. 2012; Men’shchikov 2013) and especially the treatment of background subtraction and deblending. Implications for the reallife studies are considered below, whenever possible.
5.1. Mass derivation methods
In the first method, source fluxes F_{ν} are integrated from the images ℐ_{ν}, their spectral distribution is fitted, and source mass M_{F} is estimated from the fitting model. In the second method, the pixel spectral shapes I_{ν} of the images ℐ_{ν} are fitted and the source mass M_{ℐ} is integrated from the resulting image of column densities. For unresolved sources and the thinbody fitting model, the methods give very similar levels of inaccuracy, whereas for resolved images, the methods differ quite substantially.
When fitting F_{ν}, the observed source emission from its entire volume is blended in the spatially integrated fluxes that retain no spatial information. For the models with strongly nonuniform T_{d}(r) (Fig. 3), resulting heavy distortions of the spectral shapes of F_{ν} (Fig. 4) from those of the fitting models lead to large systematic errors in estimated parameters (Figs. 6 and 7).
When fitting ℐ_{ν}, it is very beneficial to have a higher angular resolution. For fully resolved objects, pixels (i,j) sample independent I_{νij} from different columns of dust. For the transparent lower mass models (M ≲ 0.3 M_{⊙}), derived M_{ℐ} are quite accurate (Figs. 8, 9). For lower resolutions, the intensity of each pixel (i,j) blends with that of its larger surroundings within the beam, not only along the line of sight. The contamination of I_{νij} by the more distant areas, leads to a substantial degradation of T_{ℐ} and M_{ℐ}, especially when fitting large Φ_{n} (n = 3 → 6). Thus, the benefits of this method are vanishing with decreasing angular resolutions.
Multiwavelength Herschel images have been used to reconstruct radial temperature and density profiles of wellresolved sources (Roy et al. 2014). Whenever such reconstructed densities are accurate enough, they can be used to obtain masses of the nearby sources. Results of this study demonstrate, however, that the simple method of fitting images ℐ_{ν} is able to deliver accurate masses for spatially resolved sources (Sect. 4.3).
5.2. Background subtraction
Stars form in the densest parts of interstellar clouds, hence the embedded models of starless cores and protostellar envelopes must be more realistic than the isolated models. Although the spherical uniformdensity embedding clouds are idealized, in a first approximation they account for the absorption and reemission of ISRF, leading to realistic temperature profiles within the model objects. However, the presence of surrounding material makes it necessary to subtract its contribution to study the properties of the starless cores and protostellar envelopes alone. In observational practice, backgrounds are estimated by an average intensity in a narrow annulus placed just outside a source (cf. Sect. 2.5). Subtraction of such a flat background is not quite accurate as a transparent embedding cloud around any object always tends to be rimbrightened and resembles a crater, in contrast to a distant, physically unrelated back or foreground. This effect is discussed in detail in Appendix B.
The actual observable depths of the background craters may be shallower, when the local (filamentary) background itself is embedded in a less dense but more extended cloud or is seen in projection onto a distant, physically unrelated back or foreground. The rimbrightening effect gets diluted, if the column densities of the sourceembedding background and of the other unrelated clouds are similar. Poorer angular resolutions also tend to smear out the effect for less resolved sources. Realistic temperature gradients within the embedding backgrounds (Fig. 3) can either reduce or increase the crater depths by ~ 10% for starless cores and protostellar envelopes, respectively (Appendix B).
For unresolved sources, the observational algorithm of background subtraction is likely to overestimate fluxes as stars are born within the gravitationally unstable densest peaks of the parent clouds. Large beams blend the object’s emission with that of its mountainlike environment, spreading the mix downhill, towards the valleys of lower cloud densities. The real background under an unresolved source must be hilllike, whereas the background values from an annulus tend to come from more distant valleys. The problem is aggravated in crowded regions, where no local sourcefree annuli around overlapping sources can be found and where one needs to deblend sources. Angular resolution degrades with wavelengths, hence the degree of flux overestimation becomes strongly biased towards longer wavelengths.
5.3. Nonuniform temperatures
Both fitting models make a sensitive assumption that the objects have a uniform temperature T, which seems to make them inadequate for the applications to starless cores and protostellar envelopes with nonuniform T_{d}(r). For the purpose of the derivation of accurate masses, however, the uniform T can be interpreted as an appropriate average quantity. In the methods of fitting F_{ν} and ℐ_{ν}, the temperature is consistent with T_{M} and T_{Nij} from Eqs. (4) and (11), respectively (cf. Sects. 3.1 and 3.2). In other words, to estimate masses M_{F} or M_{ℐ} that are accurate (≈M), it is necessary that the fitting returns T_{F} or T_{ℐij} as close as possible to the average values T_{M} or T_{Nij}, respectively. This is clearly demonstrated in Appendix D by the accurate masses obtained for the isothermal models with T_{d}(r) = T_{M}.
The inhomogeneous temperatures tend to distort the spectral shapes of F_{ν} and I_{νij} of the objects towards shorter wavelengths (Figs. 4 and 5). With a strong dependence of the dust emission peak on temperature (κ_{νP}B_{νP}(T) ∝ T^{5}), the radial zones with higher T_{d}(r) make a much greater contribution to the observed spectral shapes. Therefore, the shapes are skewed mainly owing to the emission of those parts of the objects that have T_{d}(r) >T_{M} or T_{d}(r) >T_{Nij}. In other words, distortions of the spectral shapes are caused by the dust with excess temperatures above the average values.
This is further demonstrated by additional raytracing observations of the models, in which the excess temperatures were removed: T_{d}(r) → min{T_{d}(r),T_{M}}. Derived masses of these mostly isothermal models (not shown) are almost as accurate as those of the fully isothermal models (Appendix D), only within a few percent lower. As is expected, there is almost no dependence on the subsets Φ_{n} (n = 2 → 6), which indicates that the spectral shapes are indeed not distorted.
5.4. Fitting models
When fitting images ℐ_{ν}, both fitting models are equivalent and estimated parameters are indistinguishable (Sect. 3.3). When fitting fluxes F_{ν}, the results of this work show that thinbody generally returns far more accurate masses than modbody does for both isolated and embedded variants of starless cores and protostellar envelopes (Figs. 6 and 7).
Although the modbody fits often look better (i.e., they have smaller χ^{2} values), they generally bring parameters that are much more inaccurate. Indeed, the spectral shapes of F_{ν} are skewed towards short wavelengths by emission from their hotter parts. With more free parameters, modbody describes more flexible shapes, between B_{ν}(T) and κ_{ν}B_{ν}(T). It is able to produce better fits of the distorted spectral shapes of objects with nonuniform T_{d}(r) and hence it always tends to produce significantly over and underestimated T_{F} and M_{F}, respectively (Sect. 4.2). Furthermore, most of the modbody fits have τ_{ν} ~ 1 even in the farinfrared, which is fundamentally inconsistent with the radiative transfer models whose fluxes F_{ν} represent optically thin emission (τ_{ν} ≪ 1).
The thinbody model produces the best overall results and smallest biases and inaccuracies in derived T_{F} and M_{F} for the isolated and embedded starless cores and protostellar envelopes (Figs. 6−9). The thinbody fits are, by definition, optically thin in the farinfrared and thus consistent with the radiative transfer models. Only two variable fitting parameters of thinbody contribute to better robustness of T_{F} and M_{F}, compared to modbody with one extra free parameter.
Contrary to what is usually assumed in observational studies, the results show that it must be counterproductive to aim at precise fitting of the peaks and shorter wavelength parts of I_{ν} and F_{ν}. When the distorted shapes are reproduced more accurately, the estimates of the temperatures and masses are less accurate.
5.5. Opacity slopes
The standard methods of mass derivation ignore the presence of very small stochastically heated dust particles, assuming just a simple powerlaw opacity across all bands, and so do the radiative transfer models in this study. Emission of such very small grains within the real objects could enhance fluxes at 70 and 100 μm and, in effect, skew their spectral shapes farther towards short wavelengths, leading to more heavily overestimated temperatures and underestimated masses.
Various compositional and structural properties of real cosmic dust grains in different environments may lead to farinfrared opacity slopes that are different from β ≈ 2 (expected for small compact spherical grains) and even to wavelengthdependent β_{λ}. This study explored three constant values (1.67, 2.0, 2.4) to probe their influence on the accuracy of derived masses. Fixing β in the fitting process reduces the number of free parameters and improves the consistency (reduces biases) of derived parameters for objects with different physical properties (M, L_{⋆}).
With the correct β value, masses derived with the thinbody fits are generally off the true mass M, the magnitude of discrepancy depending on how much and in what direction derived temperature deviates from T_{M}. When β is over or underestimated by a factor of 1.2, derived masses become over or underestimated within a factor of 2 with respect to the masses obtained using the true value β = 2. This is a direct consequence of the temperatures being under or overestimated, correspondingly, a behavior that is easy to understand. In contrast to the thinbody fits, no clear trends with respect to the inaccuracies in the adopted β value can be found for modbody, except that it generally returns greatly over and underestimated T_{F} and M_{F}.
To quantify the effects of freedom in this fitting parameter, additional fits with variable β were performed (Appendix E). As is expected, they showed much greater biases and inaccuracies in derived parameters (Figs. E.1 and E.3), as the extra degree of freedom also makes the resulting β values incorrect (Fig. E.2), the magnitude of error depending on the true values of M and L_{⋆}. It is possible to compare these results with those obtained in previous studies focused on the relationships between the derived β_{F} and T_{F} (Shetty et al. 2009a,b; Juvela & Ysard 2012, and references therein). The present models of starless cores and protostellar envelopes show that the correlations of the two quantities may be both positive and negative (Fig. E.4), with almost no correlation in the case of isothermal models. They must be induced by deviations of the spectral shapes of F_{ν} (Fig. 4) from F_{ν}(T_{M}) (Fig. D.1), caused by the nonuniform T_{d}(r) (Fig. 3). For the protostellar envelopes, the correlations are nonmonotonic and they may either be strongly negative or positive, depending on the luminosity.
5.6. Data subsets
For nonuniform profiles T_{d}(r) of starless cores and protostellar envelopes (Fig. 3), better parameters are estimated with thinbody when using smaller subsets of data (n = 6 → 2) as the latter are less affected by the skewed spectral shapes. The most accurate masses are obtained by fitting just two of the longest wavelength data points; in most cases, however, a subset Φ_{3} produces very similar results. Larger subsets Φ_{n} (n = 2 → 6) may give slightly better M_{F} only when fixing an incorrect β value for the lowermass starless cores (M ≲ 1 M_{⊙}, Fig. 6). Using the inadequate fitting model with an incorrect β, larger Φ_{n} can constrain T_{F} to better resemble T_{M}. For overestimated β, derived M_{F} always shift to higher values (Fig. 7), which offsets the general opposite trend to underestimate M_{F} and thus may give more accurate results.
Inaccuracies of the data points in real observations are usually more substantial than those assumed in this work, aggravated by the systematic uncertainties that may lead to both over and underestimated F_{ν} (Sect. 5.2). Backgroundsubtracted and deblended I_{ν} at each wavelength with different angular resolutions have independent and different systematic errors. The latter must be large and uncertain on the bright and structured backgrounds in starforming regions. Moreover, unresolved sources are likely to include emission from clusters of objects.
Results of this model study are directly relevant to real observations only in the simplest case (which is rare) of accurate measurements with negligible errors. A blind application of the findings to real complex images may lead to incorrect results if the above caution is ignored and small subsets Φ_{2} of data points with large and independent measurement errors are fitted. For such data, it would be safer and more appropriate to fit a larger subset of the longest wavelength data (Φ_{3} or Φ_{4}, depending on the quality of measurements). Distortions of the observed spectral shapes towards shorter wavelengths is an intrinsic property of both starless cores and protostellar envelopes, affecting all sources in starforming regions, independently of the level of measurement errors.
It beyond the scope of this modelbased work to give general recipes to observers on how to select data points to fit. This study highlights the intrinsic behavior of the mass derivation methods by eliminating the “observational layer” (with all its complications and uncertainties) between the objects and the observer. It is important to realize that the peak and shorter wavelength shapes of I_{ν} and F_{ν} are most skewed by the temperature excesses within objects (Sect. 5.3) and their influence has to be minimized to obtain accurate results. In view of the strong dependence of the results on Φ_{n}, it is advisable to examine fits of all subsets of data points for each observed source to estimate the robustness of the results and to possibly choose the fits giving the best mass estimate.
5.7. Mass uncertainties
To make a bridge between this purely modelbased study with no measurement errors and actual observational studies and see how typical statistical errors in the input data would translate into those of the derived masses, this work assigned (fairly optimistically) ±15% errors to the model intensities and fluxes, and adopted ±20% errors in η, κ_{0}, and D.
The uncertainties in derived masses returned by the fitting algorithm are 40−70%, depending on the subset Φ_{n} of fluxes (Figs. 6−9, D.2, D.3). For the acceptable fits of larger subsets (n = 2 → 6), the derived mass uncertainty is dominated by the 20% errors of the parameters η, κ_{0}, and D, because the effect of the 15% measurement errors becomes smaller for the fits constrained by a larger number of independent data points. For smaller subsets (n = 6 → 2), the fits are less constrained, hence the contribution of the 15% error bars to the derived mass uncertainty becomes larger. Different subsets Φ_{n} give very similar results only for fully resolved sources with the method of fitting images ℐ_{ν} (Figs. 8 and 9).
In real observations, statistical measurement uncertainties in I_{ν} and F_{ν} are larger than the ±15% errors assumed in this study. Furthermore, it would be more realistic to adopt uncertainties of η, κ_{0}, and D of at least ±50%, which would raise the derived mass uncertainties well beyond 100%. By including the mass inaccuracies (of a factor of 2) induced by a 20% uncertainty in β and systematic errors (of factors of at least 2) caused by the nonuniform temperatures within the observed sources, it is clear that the absolute values of masses derived from fitting are inaccurate and uncertain (within a factor of at least 2−3). It is possible to neglect the uncertainties in η, κ_{0}, and D, if the focus is on studying relative properties of a population of objects all at roughly the same distance within a certain starforming cloud with homogeneous dust properties. Apart from this, however, one has to derive accurate absolute values of the most fundamental parameters to make correct and physically meaningful conclusions.
It is quite important to carefully estimate mass uncertainties: without realistic error bars, derived masses are meaningless and correct conclusions are unlikely. To go one step further and obtain an idea of the actual errors of derived masses, it is possible to construct radiative transfer models of the observed population of sources, distribute the model sources over the observed images and extract them, and finally derive their masses. Comparing derived masses with the fully known model properties, reasonable estimates of the actual errors in derived masses are obtained.
6. Conclusions
This paper presented a modelbased study of the uncertainties and biases of the standard methods of mass derivation (fitting fluxes F_{ν} and images ℐ_{ν}), widely applied in observational studies of the low and intermediate star formation. To focus on the intrinsic effects related to the physical objects, all observational complications leading to additional flux or intensity errors (filamentary and fluctuating backgrounds, instrumental noise, calibration errors, different resolutions, blending with nearby sources, etc.) were assumed to be nonexistent. As a consequence, results of this work are directly relevant only for the simplest case of bright isolated sources on faint backgrounds with negligible measurement errors. The real mass uncertainties for starless cores and protostellar envelopes are likely to be larger than those found in this work.
Embedding backgrounds of physical objects tend to be rimbrightened (i.e., resembling craters), their depths depend on the sizes of the object and embedding cloud. The standard observational procedure of flat background subtraction may give systematically underestimated I_{ν} and F_{ν}, and hence masses for resolved sources. Poorer angular resolutions at longer wavelengths tend to systematically overestimate I_{ν} and F_{ν}, and hence masses for unresolved objects, as their emission gets blended with that of the mountainlike background and possibly with other objects within the same beam.
Temperature excesses above average values T_{M} and T_{Nij} are the primary reason for the skewness of the spectral shapes of F_{ν} and I_{νij} towards shorter wavelengths. Depending on M, L_{⋆}, β, Φ_{n}, fitting model, and angular resolution, they lead to overestimated temperatures and various biases. With the method of fitting F_{ν}, masses become underestimated by factors 2−5. When fitting ℐ_{ν}, similarly large inaccuracies are found only for unresolved objects, whereas with better angular resolutions they decrease and become very small for wellresolved objects.
When fitting ℐ_{ν}, both fitting models are equivalent and estimated M_{ℐ} are indistinguishable. When fitting F_{ν}, thinbody gives far more accurate M_{F} than modbody does. The latter causes such great biases and inaccuracies in T_{F} and M_{F} that modbody must be considered unusable.
Fixing opacity slope β reduces biases in derived parameters. When β is too high or low by a factor of 1.2, derived masses become over or underestimated by a factor of 2 with respect to those obtained using the true β = 2. Qualitatively, this behavior is caused by the natural tendency of steeper β to produce lower temperatures, hence higher masses. Quantitatively, the factors are approximate and they may depend on some of the assumptions used in this study. Mass derivation with a free variable β should be avoided, as it tends to lead to very strong biases and erroneous masses.
Subsets Φ_{n} of data points strongly affect derived masses, except when fitting images ℐ_{ν} of fully resolved sources. Given the nonuniform T_{d}(r) of the model objects, the most accurate masses are estimated with thinbody using subsets that are as small as possible (n = 6 → 2). In real observations with substantial independent errors in different wavebands, it should be much safer and more accurate to fit slightly larger subsets (Φ_{3} or even Φ_{4}). Those data points that are on the peak of their spectral distribution or on the shortwavelength side should be ignored, whenever possible, to improve the accuracy of derived masses. In practice, it is advisable to investigate fits of all subsets of data for each observed source, to verify robustness of the results and to possibly choose the best mass estimate.
Inaccuracies of the derived masses depend on the mass range considered. Dividing the range of 0.03−30 M_{⊙} at 1 M_{⊙} into the low and highmass objects and considering unresolved or poorly resolved sources with β = 2, the following conclusions drawn. Masses of the isolated low and highmass starless cores are underestimated by factors 1−1.3 and 1.3−4, respectively. The mass inaccuracies increase towards the highmass end and for larger subsets Φ_{n} (n = 2 → 6). Masses of the embedded lowmass cores are underestimated by a factor of 1.4. They are more biased towards the highmass end, changing from under to overestimated within a similar factor. Masses of the protostellar envelopes are considerably biased over the range of 0.03−30 L_{⊙} and their inaccuracies strongly increase for larger subsets of Φ_{n} (n = 2 → 6). Masses of the isolated and embedded envelopes become underestimated by factors 2−3 and 3−5, respectively. Masses of the lowmass starless cores are likely to be determined much more accurately than those of protostellar envelopes.
Typical mass uncertainties returned by the fitting algorithm are 40−70%, adopting statistical errors of 15% for model intensities (fluxes) and optimistically assuming that η, κ_{0}, and D were known to within 20%. If more realistic statistical errors in the measurements and parameters of at least 50% are adopted, the mass uncertainties increase well beyond 100%. Larger subsets Φ_{n} (n = 2 → 6) of independent data points are beneficial in somewhat reducing the resulting mass uncertainties. On the other hand, the larger subsets are also highly undesirable, because they escalate the systematic mass inaccuracies by at least a factor of 2 as a result of nonuniform temperatures. Smaller subsets Φ_{n} (n = 6 → 2) are able to minimize the systematic errors caused by the temperature variations, but they increase the chances of getting incorrect masses in the case of inaccurate data measurements in real observations.
Without extremely accurate flux measurements and knowledge of the free parameters (η,κ_{0},β,D), and without radiative transfer simulations to have an idea of the actual mass errors, it would be reasonable to assume that the absolute values of masses of the unresolved or poorly resolved objects are inaccurate to within at least a factor of 2−3. This may be less problematic, if the relative properties are studied of a population of objects within a starforming cloud, hopefully with the same distance and dust opacities. Ultimately, however, accurate absolute masses are necessary to make correct, physically meaningful conclusions.
There are several ways to improve mass estimates: (1) using a multiwavelength source extraction method measuring the most accurate, least biased backgroundsubtracted and deblended fluxes across all wavebands; (2) selecting the best sources from the extraction catalogs, with the most accurately and consistently measured fluxes over at least three longest wavelengths; (3) using the thinbody fitting model for the purposes of temperature or mass derivation; (4) estimating the model parameters η, κ_{0}, β, and D as accurately as possible and always performing fitting with β fixed; (5) for resolved sources, fitting their backgroundsubtracted (and deblended) images and integrating source masses from column densities; (6) fitting all subsets of data points for each source and choosing the smallest possible subset (Φ_{3} or Φ_{4}) that gives the most accurate temperatures and masses; (7) using radiative transfer models to simulate observed images, extracting the model sources, deriving their masses, and comparing them to the true model values to have an idea of the actual errors for the derived masses of observed sources.
Farinfrared transparency is an important assumption that is wrong for highmass objects (Sect. 2.3).
Acknowledgments
This study employed SAOImage DS9 (by William Joye) developed at the Smithsonian Astrophysical Observatory (USA), the CFITSIO library (by William D. Pence) developed at HEASARC NASA (USA), and SWarp (by Emmanuel Bertin) developed at Institut d’Astrophysique de Paris (France). Radiative transfer code MC3Dsph version 3.12 (by Sebastian Wolf, Wolf 2003) was used to compute the first generation of the models in this work. The plot utility and ps12d library used in this work to draw figures directly in the PostScript language were written by the author using the PSPLOT library (by Kevin E. Kohler) developed at Nova Southeastern University Oceanographic Center (USA) and the plotting subroutines from the AZEuS MHD code (by David A. Clarke and the author) developed at Saint Mary’s University (Canada). Collaborative work within the Herschel Gould Belt and HOBYS key projects was very beneficial. Useful comments on a draft made by Pierre Didelon, Arabindo Roy, Alana RiveraIngraham, Sarah Sadavoy, Philippe André, and by the anonymous referee helped improve this paper.
References
 Alves, J. F., Lada, C. J., & Lada, E. A. 2001, Nature, 409, 159 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
 André, P., Di Francesco, J., WardThompson, D., et al. 2014, in Protostars and Planets VI, eds. H. Beuther, R. S. Klessen, C. P. Dullemond, & Th. Henning, Space Sci. Ser., 27 [Google Scholar]
 Bernard, J. P., Boulanger, F., Desert, F. X., & Puget, J. L. 1992, A&A, 263, 258 [NASA ADS] [Google Scholar]
 Black, J. H. 1994, in The First Symposium on the Infrared Cirrus and Diffuse Interstellar Clouds, eds. R. M. Cutri, & W. B. Latter, ASP Conf. Ser., 58, 355 [Google Scholar]
 Bonnor, W. B. 1956, MNRAS, 116, 351 [NASA ADS] [CrossRef] [Google Scholar]
 Desert, F.X., Boulanger, F., & Puget, J. L. 1990, A&A, 237, 215 [NASA ADS] [Google Scholar]
 Evans, II, N. J., Rawlings, J. M. C., Shirley, Y. L., & Mundy, L. G. 2001, ApJ, 557, 193 [NASA ADS] [CrossRef] [Google Scholar]
 Hildebrand, R. H. 1983, QJRAS, 24, 267 [NASA ADS] [Google Scholar]
 Juvela, M., & Ysard, N. 2012, A&A, 539, A71 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Kelly, B. C., Shetty, R., Stutz, A. M., et al. 2012, ApJ, 752, 55 [NASA ADS] [CrossRef] [Google Scholar]
 Könyves, V., André, P., Men’shchikov, A., et al. 2015, A&A, 584, A91 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Larson, R. B. 1969, MNRAS, 145, 271 [NASA ADS] [CrossRef] [Google Scholar]
 Malinen, J., Juvela, M., Collins, D. C., Lunttila, T., & Padoan, P. 2011, A&A, 530, A101 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Men’shchikov, A. 2013, A&A, 560, A63 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Men’shchikov, A. B., & Henning, T. 1997, A&A, 318, 879 [NASA ADS] [Google Scholar]
 Men’shchikov, A. B., Henning, T., & Fischer, O. 1999, ApJ, 519, 257 [NASA ADS] [CrossRef] [Google Scholar]
 Men’shchikov, A., André, P., Didelon, P., et al. 2010, A&A, 518, L103 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Men’shchikov, A., André, P., Didelon, P., et al. 2012, A&A, 542, A81 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Ossenkopf, V., & Henning, T. 1994, A&A, 291, 943 [NASA ADS] [Google Scholar]
 Parravano, A., Hollenbach, D. J., & McKee, C. F. 2003, ApJ, 584, 797 [NASA ADS] [CrossRef] [Google Scholar]
 Pilbratt, G. L., Riedinger, J. R., Passvogel, T., et al. 2010, A&A, 518, L1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. 1992, Numerical recipes in FORTRAN. The art of scientific computing, 2nd edn. (Cambridge University Press) [Google Scholar]
 Roy, A., André, P., Palmeirim, P., et al. 2014, A&A, 562, A138 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Shetty, R., Kauffmann, J., Schnee, S., & Goodman, A. A. 2009a, ApJ, 696, 676 [NASA ADS] [CrossRef] [Google Scholar]
 Shetty, R., Kauffmann, J., Schnee, S., Goodman, A. A., & Ercolano, B. 2009b,ApJ, 696, 2234 [Google Scholar]
 Shu, F. H. 1977, ApJ, 214, 488 [NASA ADS] [CrossRef] [Google Scholar]
 Siebenmorgen, R., Kruegel, E., & Mathis, J. S. 1992, A&A, 266, 501 [NASA ADS] [Google Scholar]
 Stamatellos, D., Whitworth, A. P., André, P., & WardThompson, D. 2004, A&A, 420, 1009 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Wolf, S. 2003, Comput. Phys. Comm., 150, 99 [NASA ADS] [CrossRef] [Google Scholar]
Appendix A: Protostellar envelopes temperatures
With the adopted κ_{ν} and ρ(r) (Sects. 2.1 and 2.2), the radial temperature profiles of protostellar envelopes (Fig. 3) can be approximated by a combination of two power laws: (A.1)where r is in AU and parameters A and B depend on mass and accretion luminosity: Equation (A.1) describes the temperatures induced by the central accretion energy source (ignoring ISRF), valid for T_{d} ≲ 300 K. The first term in Eq. (A.1) approximates the steepest profiles in the inner semiopaque region, the second term represents temperatures in the transparent outer part of the envelopes. An approximate borderline between the two regimes can be estimated directly from Fig. 3 as (A.2)
Appendix B: Rimbrightened backgrounds
Fig. B.1 Background rim brightening in the spherical uniformdensity embedding shells, either isothermal (left) or with the radiativeequilibrium T_{d}(r) from the models (Sect. 2) of embedded starless cores (middle) and protostellar envelopes with L_{⋆} = 30 L_{⊙} (right). The intensity profiles on the left are labeled by the size ratios R_{E}/R = { 30,3,1.3 } of the embedding cloud and the central cavity, whereas the intensity profiles at 500 μm in the middle and on the right indicate the mass (in M_{⊙}) of the embedded models. For uniform temperatures (left), the brightening factors f_{S} are 1.03,1.41, and 2.77 are the values given by Eq. (B.1). For nonuniform temperatures (and R_{E}/R = 3, R = 10^{4} AU), they range from 1.27 to 1.40 (middle) and from 1.44 to 1.53 (right). 

Open with DEXTER 
In contrast to the emission of the distant and physically unrelated backgrounds or foregrounds, embedding backgrounds resemble craters. The central spherical region occupied by an object (r ≤ R) does not belong to the embedding cloud (R<r ≤ R_{E}) and thus must be considered empty when determining the object’s background. Rim brightening for uniformdensity transparent isothermal clouds with such a cavity depends only on their relative radial thickness (R_{E}−R) /R. It can be quantified by the ratio f_{S} of intensities (or column densities) along the lines of sight passing through the rim and the center of the cavity: (B.1)According to Eq. (B.1), the background under embedded objects can be overestimated from a few percent to a factor of several, hence backgroundsubtracted values and masses may become substantially underestimated. For the present models with R = 10^{4}AU and R_{E}/R = 3, the background and masses are over and underestimated by f_{S} = 1.41, respectively. The value is the discrepancy of derived masses seen for the embedded starless cores and protostellar envelopes (Figs. 6−9, D.2, D.3). The effect becomes much stronger for very thin shelllike embedding clouds, whereas it vanishes for extended background clouds. For the size ratios R_{E}/R of 1.3 and 30, the factor f_{S} takes the values of 2.77 and 1.03, respectively. Numerical examples of the rimbrightened backgrounds for both isothermal and nonisothermal spherical embedding clouds are shown in Fig. B.1.
Realistic temperature profiles in the embedding clouds bring only minor quantitative changes, not altering qualitatively the rim brightening effect (Fig. B.1). Steep positive or negative temperature gradients in the dense shells around embedded starless cores and protostellar envelopes (Fig. 3) tend to slightly reduce or increase the brightening effect (by ~ 10%), respectively.
Actual geometry of the real background clouds is of minor importance, the only relevant assumption is that the embedded object (hence, its background cavity) has a convex shape. For instance, assuming a planeparallel embedding cloud with thickness 2 R_{E} along the line of sight, it is possible to obtain a slightly different expression than Eq. (B.1) for the rim brightening factor: (B.2)Planeparallel geometry makes the brightening factors f_{P} somewhat larger than f_{S} from Eq. (B.1), with the difference being stronger for thinner shelllike clouds. For instance, the size ratios R_{E}/R of 30,3, and 1.3, correspond to the brightening factors f_{P} of 1.03,1.46, and 3.55, respectively.
Depths of the background craters may be quite dissimilar for different objects in real observations. The observations show that the interstellar medium is strongly filamentary and that stars tend to form in narrow, very dense filaments (e.g., Men’shchikov et al. 2010; André et al. 2014). For an object embedded in a cylindrical filament of radius R_{E} in the plane of the sky, the brightening factor f_{C} is intermediate between f_{S} and f_{P}, depending on the position angle of the radiusvector from the center of the object to its outer boundary R. It is easy to see that f_{C} = f_{P} along the filament’s axis, whereas f_{C} = f_{S} in the orthogonal direction, across the filament.
The widths of the embedding filaments appear to be similar to the sizes of embedded objects (Men’shchikov et al. 2010). Assuming their cylindrical geometry, the embedding filaments of starless cores and protostellar envelopes are likely to have R_{E} only a factor of about 2−3 larger than R. For such narrow filamentary backgrounds of resolved objects, the rim brightening effect is quantified by factors f_{C} ≈ 1.8−1.4.
Background rim brightening may be observable only when imaging the nearby resolved sources unaffected by other distant back or foregrounds. With this effect in action, the standard observational algorithm of background subtraction underestimates F_{ν} by factors similar to f_{S} or f_{P}. With poorer angular resolutions, the rim of the background crater gets smeared out and thus the brightening effect eventually vanishes for unresolved sources which also have an opposite trend to produce underestimated background and hence overestimated I_{ν} and F_{ν} (cf. Sect. 5.2).
Appendix C: Fitting procedure
The nonlinear leastsquares fitting algorithm used in this work employs the LevenbergMarquardt method (Press et al. 1992) that minimizes χ^{2} residuals between the model and data. The method requires a user to provide initial guesses for model parameters. Tests have shown that an arbitrary choice of the initial values of the fitting models (Sects. 3.1, 3.2) does not guarantee convergence to the global χ^{2} minimum. A fully automated fitting procedure has been designed to overcome this problem and avoid any need to make arbitrary initial guesses.
The algorithm explores the multidimensional parameter space of the model with a large number of trial fittings of the spectral distributions of data points. The parameter space is discretized in logarithmically equidistant steps δlog p, covering all relevant initial values of temperature T (4−10^{3} K), mass M (3 × 10^{3}−3 × 10^{2}M_{⊙}), column density N_{H2} (10^{17}−10^{27} cm^{2}), and solid angle Ω (1.85 × 10^{13}−1.85 × 10^{5} sr). Large initial discretization steps δlog p ~ 10 and the above ranges of parameters are adaptively refined in an iterative binary search down to δlog p ≈ 0.1, accelerating the algorithm in finding the global minimum. Data points are fitted using all combinations of the model parameters^{6} in the adaptively refined parameter space and their initial values are found that converge to the globally smallest χ^{2} in a fully automated procedure.
Algorithms described in this paper were written as a versatile and robust FORTRAN utility fitfluxes that efficiently estimates modbody or thinbody parameters from fitting either total fluxes of cataloged sources or intensities of multiwavelength images. The code is easy to install and use and it is freely available from the author upon request.
Appendix D: Results for isothermal models
Fig. D.1 Spectral energy distributions of the isothermal models of starless cores (upper) and protostellar envelopes (lower). Shown are the backgroundsubtracted fluxes of the isolated models (left) and of their embedded variants (right). For the cores of increasing masses, T_{M} = {16.3, 15.0, 13.2, 11.2, 9.25, 7.66, 6.42 K}. For the envelopes of increasing luminosities and masses, T_{M} = {16.6, 16.8, 17.3, 18.1, 19.5, 21.9, 25.9 K}, T_{M} = {13.8, 14.1, 14.7, 15.6, 17.1, 19.4, 23.0 K}, T_{M} = {10.0, 10.4, 11.1, 12.3, 14.1, 16.8, 20.7 K}. See Fig. 4 for more details. 

Open with DEXTER 
Fig. D.2 Masses M_{F} derived from fitting F_{ν} for the isothermal models of both isolated and embedded starless cores and protostellar envelopes. Compared to the results for isolated models, all derived masses of embedded models are underestimated by approximately a factor of 1.3, owing to background oversubtraction (Sect. 5.2). See Fig. D.1 for SEDs and Fig. 6 for more details. 

Open with DEXTER 
The importance of estimating T_{F} that approaches the massaveraged temperature T_{M} from Eq. (4) for deriving accurate masses is shown by isothermal models, those described in Sect. 2 and used throughout this paper where selfconsistent (radiativeequilibrium) profiles T_{d}(r) were replaced with T_{M}. The isothermal models were then observed and imaged in a raytracing run of the radiative transfer code. The resulting SEDs (Fig. D.1) are essentially the modified blackbody shapes κ_{ν}B_{ν}(T_{M}) ν and the same is true for the spectral shapes of image pixels. The temperature excesses above T_{M} (Sect. 5.3) that greatly distorted the model SEDs (Fig. 4) towards shorter wavelengths do not exist in the isothermal models. Consequently, the isothermal shapes of F_{ν} and I_{νij} bring much more expected and accurate results.
Fig. D.3 Masses M_{ℐ} derived from fitting images ℐ_{ν} of the isothermal models of both isolated and embedded starless cores and protostellar envelopes for the correct value of β = 2. Two columns of panels (left, middle) display the masses derived for resolved and unresolved images of the isolated models, whereas the third column of panels (right) present results for unresolved embedded models. Angular resolutions do not make any difference for the isothermal models. See Fig. D.2 and Fig. 8 for more details. 

Open with DEXTER 
With the correct value β = 2, derived M_{F} of the isolated starless cores and protostellar envelopes derived with thinbody agree with the true masses M (Fig. D.2). The same results are obtained for modbody, with the exception of the minimal subset Φ_{3} for some models. An inspection of the problematic fits show that T_{F} is somewhat overestimated because of an additional degree of freedom in modbody and a formal search for a globally best fit in its parameter space. The fits in question have the globally lowest χ^{2} value, but they correspond to small values of Ω and, therefore, to high optical depth τ_{ν} ~ 1. However, there are also other very good fits with somewhat larger χ^{2} that do produce accurate T_{F} = T_{M} with τ_{ν} ≪ 1, consistent with the models. The problem seems to be just a simple consequence of the finite accuracy of the numerical models and their fluxes.
With the same β = 2, the derived masses of the embedded models (Fig. D.2) are almost uniformly underestimated by a factor of approximately 1.3. The reason for the difference with respect to the isolated models is the conventional approach to background subtraction. Emission of a transparent cloud embedding a physical object tends to be rim brightened (Appendix B, Sect. 5.2). An average intensity in an annulus may overestimate background, from a few percent to a factor of several, hence may underestimate the backgroundsubtracted intensities I_{ν}, fluxes F_{ν}, and derived masses (see Sects. 4.2 and 4.3).
For an inadequate fitting model, skewed by β values fixed below or above its correct value, the fits are obviously biased to over or underestimate T_{F} and hence to under or overestimate M_{F}, correspondingly (Fig. D.2). The inaccuracy is within a factor of 2, independently of whether β is a factor of 1.2 lower or higher. Fitting larger subsets Φ_{n} (n = 2 → 6) for the isothermal models may give somewhat better derived parameters, as they better constrain T_{F} with an incorrect slope β.
Mass derivation from images ℐ_{ν} of the isothermal models delivers results that are similar to those described above for both isolated and embedded variants (Fig. D.3). Dependence on the adopted β is the same as described above, hence only the results with correct β = 2 are presented. With no temperature deviations from T_{M} in the isothermal models, derived M_{ℐ} are very accurate for all angular resolutions, in contrast to the results with the selfconsistent T_{d}(r) (Figs. 8 and 9). Derived M_{ℐ} for the embedded models are practically identical to M_{F}, being underestimated by a factor of 1.3 owing to the background rimbrightening effect (Appendix B, Sect. 5.2).
Appendix E: Results for free variable β
Fig. E.1 Temperatures T_{F} and masses M_{F} derived from fitting F_{ν} of isolated, embedded, and isothermal models (fits with free variable β) for starless cores (upper) and protostellar envelopes (lower). See Fig. 6 for more details. 

Open with DEXTER 
Fig. E.2 Opacity slope β_{F} derived from fitting F_{ν} of isolated, embedded, and isothermal models (fits with free variable β) for starless cores (upper) and protostellar envelopes of selected masses (3, 0.3, and 0.03 M_{⊙}, lower). See Fig. 6 for more details. 

Open with DEXTER 
Fig. E.3 Masses M_{ℐ} derived from fitting images ℐ_{ν} of the isolated and embedded starless cores and protostellar envelopes (fits with free variable β). Two columns of panels (left, middle) display the masses derived for the resolved and unresolved images of the isolated models, whereas the third column of panels (right) presents results for the unresolved embedded models. Derived masses of the latter are underestimated by a factor of approximately 1.3 as a result of the conventional procedure of background subtraction (Sect. 5.2). See Fig. 8 for more details. 

Open with DEXTER 
Fig. E.4 Relationships between T_{F} and β_{F} derived from fitting fluxes F_{ν} of starless cores and protostellar envelopes (fits with free variable β). Curves plotted with squares (dark colors) and circles (bright colors) in the left and middle panels correspond to the isolated and embedded models, whereas in the right panel they correspond to the isothermal versions of the isolated starless cores and protostellar envelopes, respectively. Results for the starless cores are plotted for all masses 0.03−30 M_{⊙}. Results for the protostellar envelopes, shown for only selected masses (0.03,0.3, and 3 M_{⊙}, three sets of identical lines), span the entire range of luminosities 0.03−30 L_{⊙}. Thick horizontal lines indicate the true value β = 2. For clarity of the plots, error bars are not shown. 

Open with DEXTER 
In some applications of the mass derivation methods, the opacity slope β has been allowed to vary along with the other fitting parameters (T, M, Ω). To quantify effects of the extra degree of freedom, additional fits with variable β were performed in this study. Although the parameters for both fitting models were derived and analyzed, only the much less incorrect thinbody results are presented and discussed here.
Figure E.1 compares derived T_{F} and M_{F} of the isolated, embedded, and isothermal variants of starless cores and protostellar envelopes with the true model values (T_{M}, M). Although the isolated cores and envelopes display behavior that is qualitatively similar to the β = 2 case (Sect. 4.2), the biases in T_{F} and M_{F} towards denser (more massive) cores and envelopes, as well as over L_{⋆} for the latter, become much stronger. For example, for the isolated starless cores with M ≳ 10 M_{⊙}, derived T_{F} are overestimated by a factor of 2, whereas M_{F} are underestimated by a factor of 15. For the embedded protostellar envelopes of M = 3 M_{⊙} with L_{⋆} ≲ 1 L_{⊙}, temperatures are overestimated by a similarly large factor and M_{F} underestimated by a factor of 8.
Such errors are caused by the derived β_{F} whose values for starless cores are systematically lowered towards higher mass models, and are underestimated by a factor of 4 (Fig. E.2). In the case of the embedded protostellar envelopes, the values of β_{F} are progressively underestimated towards lower luminosities, up to a factor of 2 (Fig. E.2). The very large errors in β_{F} are, in turn, caused by the temperature excesses over T_{M} (Sect. 5.3), which is highlighted in Fig. E.1 by the accurate results for the isothermal models. As in the fixed β case, errors in derived parameters for the embedded starless cores are smaller than those for the isolated cores, whereas the behavior is opposite for the embedded protostellar envelopes (cf. Figs. 6, 7). However, the biases over the masses and luminosities in Fig. E.1 become stronger than in the fixed β case, which again is attributed to the additional biases in the derived β_{F} values (Fig. E.2).
The method of fitting images ℐ_{ν} delivers results that are similar to those described above, for both isolated and embedded variants (Fig. E.3) of partially resolved and unresolved objects. Derived masses for the fully resolved objects are much more accurate and they do not depend on the subset of data points Φ_{n}, for the reasons discussed in Sects. 4.3 and 5.1. With degrading angular resolutions, accuracy of the estimated parameters deteriorates to the levels obtained from the method of fitting total fluxes F_{ν} (Fig. E.1). Larger beams heavily blend emission with nonuniform temperatures from different pixels, distorting their spectral distribution towards shorter wavelengths. In both methods, derived masses become systematically much less accurate (greatly underestimated) when fitting larger subsets Φ_{n} (n = 3 → 6). However, even the smallest subsets Φ_{3} show very significant inaccuracies and different biases that depend on the mass and luminosity of an object.
The above results obtained with free fitting parameter β can be compared with those from the previous studies focused on the relationship between derived temperature and opacity slope (cf. Shetty et al. 2009a,b; Juvela & Ysard 2012, and references therein). Figure E.4 shows the intrinsic dependencies between β_{F} and T_{F} for the isolated and embedded starless cores and protostellar envelopes, and for the isothermal versions of the isolated models. The correlations between β_{F} and T_{F} take variety of shapes, from a strongly positive to a strongly negative correlation, with practically no correlation for the isothermal models. This suggests that they must be caused by different kinds of deviations of the spectral shapes of F_{ν} (Fig. 4) produced by the nonuniform T_{d}(r) (Fig. 3) from F_{ν}(T_{M}) (Fig. D.1). Smaller subsets Φ_{n} (n = 6 → 3) bring less correlated β_{F} and T_{F} than the large subsets do. For the protostellar envelopes, the correlations are nonmonotonic and they may either be strongly negative or positive, depending on the luminosity of the central energy source.
All Figures
Fig. 1 Opacities of grains (per gram of dust) and model radial optical depths. Subscripts on the curve labels (n_{30} to n_{0.03}) indicate the model mass M (in M_{⊙}). The wavelength dependence of κ_{sca}, κ_{abs}, and κ_{ext} is shown by thick solid lines (see Sect. 2.1 for details). The extinction optical depths τ_{ext} of the protostellar envelopes (L_{⋆} = 0.3 L_{⊙}) and starless cores are indicated respectively by the three sets of dashed blue and red lines. 

Open with DEXTER  
In the text 
Fig. 2 Density structure of the model starless cores and protostellar envelopes. Subscripts on the curve labels (n_{30} to n_{0.03}) indicate the model mass M (in M_{⊙}). The dashed vertical line shows the outer boundary radius R = 10^{4} AU for all models. Embedded models are implanted in larger uniformdensity clouds with an outer boundary at 3 × 10^{4} AU. The dashed horizontal lines continue the densities of starless cores within the innermost radial zone. The dashed diagonal lines continue the densities of protostellar envelopes towards the radius of the inner dustfree cavity R_{0}, whose size depends on the model temperature profile T_{d}(r) (cf. Fig. 3) and adopted dust sublimation temperature (T_{S} = 10^{3} K). In other words, the dashed diagonal lines visualize the range of densities and radial distances over which the inner boundary R_{0} is located for L_{⋆} spanning the entire range 0.03 − 30 L_{⊙} (see Eq. (1)). 

Open with DEXTER  
In the text 
Fig. 3 Radiativeequilibrium dust temperature profiles of starless cores (upper) and protostellar envelopes (lower) for the isolated models (left) and their embedded variants (right). Subscripts of the curve labels (T_{30} to T_{0.03}) indicate the model mass M (in M_{⊙}). The dashed horizontal lines in the upper panels continue the profiles of starless cores within the innermost radial zone. The dashed vertical line shows the outer boundary radius R = 10^{4} AU of all models. The maximum temperature and the inner boundary of the dusty protostellar envelopes are defined by the adopted dust sublimation temperature T_{S} = 10^{3} K. Three dashed horizontal lines in the lower panels indicate the range of radial distances over which the boundaries R_{0} of the dustfree cavities are located for the model luminosities in the entire range of 0.03 − 30 L_{⊙} (see Fig. 2). For protostellar envelopes with the same M, the “hotter” profiles correspond to higher L_{⋆}, larger R_{0} (cf. Eq. (1)), and lower radial optical depths . Two dashed lines bracketing the profiles of protostellar envelopes indicate the slopes T_{d}(r) ∝ r^{0.88} and ∝ r^{− 1/3}, the latter describing the temperatures of a transparent dusty envelope with the adopted grain properties (see also Appendix A). Vertically aligned filled circles indicate the massaveraged temperature T_{M} for each model, defined by Eq. (4). The slight wiggling of some profiles around their minima reflects the discrete nature of the Monte Carlo radiative transfer method. 

Open with DEXTER  
In the text 
Fig. 4 Spectral energy distributions starless cores (upper) and protostellar envelopes (lower). Shown are the backgroundsubtracted fluxes for the isolated models (left) and their embedded variants (right). Subscripts of the curve labels (F_{30} to F_{0.03}) indicate the model mass (in M_{⊙}). For protostellar envelopes of the same mass, the SEDs with higher fluxes correspond to higher accretion luminosities L_{⋆} (0.03, 0.1, 0.3, 1, 3, 10, 30 L_{⊙}). Dashed lines indicate the fluxes of the ISRF that were integrated over the projected area of either the isolated models or the embedding clouds. 

Open with DEXTER  
In the text 
Fig. 5 Fluxes of the isolated starless cores (upper) and protostellar envelopes (lower) fitted with the thinbody (left) and modbody (right) models. The fluxes for the 0.03, 0.3, and 3 M_{⊙} cores and envelopes (with L_{⋆} = 1 L_{⊙}) from Fig. 4 are shown as black triangles, squares, and circles, respectively. Successful fits (Sect. 3.4) using different subsets Φ_{n} of fluxes are indicated by the solid and dashed lines of different widths and colors. The thin black curves show the flux distributions for the isothermal models where the actual model temperatures (Fig. 3) were replaced with their massaveraged values: T_{d}(r) = T_{M} (16.3,13.2,9.2 K for starless cores and 18.1,15.6,12.3 K for protostellar envelopes). 

Open with DEXTER  
In the text 
Fig. 6 Temperatures T_{F} and masses M_{F} derived from fitting F_{ν} of both isolated and embedded starless cores vs. the true model values of T_{M} and M for three β values (1.67, 2, 2.4). For various subsets Φ_{n} of fluxes, results from successful thinbody and modbody fits (Sect. 3.4) are displayed by the colored and gray lines, respectively. Error bars represent the 1 × σ uncertainties of the derived parameters returned by the fitting routine combined with the assumed ± 20% uncertainties of η, κ_{0}, and D (Sect. 3.4). The black solid lines show the locations where T_{F} and M_{F} are equal to the true values. To preserve clarity of the plots, much less accurate modbody results are displayed only for correct β = 2 and without error bars. 

Open with DEXTER  
In the text 
Fig. 7 Temperatures T_{F} and masses M_{F} derived from fitting F_{ν} of both isolated and embedded protostellar envelopes (with the true masses M of 0.03, 0.3, and 3M_{⊙}) vs. the true model values of T_{M} and L for three β values (1.67, 2, 2.4). Results from successful thinbody and modbody fits for various subsets Φ_{n} of fluxes (Sect. 3.4) are displayed by the colored and gray lines, respectively. See Fig. 6 for more details. 

Open with DEXTER  
In the text 
Fig. 8 Temperatures T_{ℐ} and masses M_{ℐ} derived from fitting images ℐ_{ν} of both isolated and embedded starless cores vs. the true model values of T_{M} and M for correct β = 2. The three columns of panels present results for three angular resolutions (resolved, partially resolved, and unresolved cases) and for various subsets Φ_{n} of pixel intensities. Error bars represent the 1 × σ uncertainties of the derived T_{ℐ} and M_{ℐ} (computed over all pixels as the N_{H2}averaged errors of T_{Nij} and integrated errors of N_{H2}, correspondingly), combined with the assumed ± 20% uncertainties of η, κ_{0}, and D (Sect. 3.4). Less reliable results (n−γ + 1 <χ^{2}< 10 in some pixels) are shown without error bars. See Fig. 6 for more details. 

Open with DEXTER  
In the text 
Fig. 9 Temperatures T_{ℐ} and masses M_{ℐ} derived from fitting images ℐ_{ν} of both isolated and embedded protostellar envelopes (with the true masses M of 0.03,0.3, and 3 M_{⊙}) vs. the true model values of T_{M} and L for correct β = 2. The three columns of panels present results for three angular resolutions indicated (resolved, partially resolved, and unresolved cases). See Fig. 8 for more details. 

Open with DEXTER  
In the text 
Fig. B.1 Background rim brightening in the spherical uniformdensity embedding shells, either isothermal (left) or with the radiativeequilibrium T_{d}(r) from the models (Sect. 2) of embedded starless cores (middle) and protostellar envelopes with L_{⋆} = 30 L_{⊙} (right). The intensity profiles on the left are labeled by the size ratios R_{E}/R = { 30,3,1.3 } of the embedding cloud and the central cavity, whereas the intensity profiles at 500 μm in the middle and on the right indicate the mass (in M_{⊙}) of the embedded models. For uniform temperatures (left), the brightening factors f_{S} are 1.03,1.41, and 2.77 are the values given by Eq. (B.1). For nonuniform temperatures (and R_{E}/R = 3, R = 10^{4} AU), they range from 1.27 to 1.40 (middle) and from 1.44 to 1.53 (right). 

Open with DEXTER  
In the text 
Fig. D.1 Spectral energy distributions of the isothermal models of starless cores (upper) and protostellar envelopes (lower). Shown are the backgroundsubtracted fluxes of the isolated models (left) and of their embedded variants (right). For the cores of increasing masses, T_{M} = {16.3, 15.0, 13.2, 11.2, 9.25, 7.66, 6.42 K}. For the envelopes of increasing luminosities and masses, T_{M} = {16.6, 16.8, 17.3, 18.1, 19.5, 21.9, 25.9 K}, T_{M} = {13.8, 14.1, 14.7, 15.6, 17.1, 19.4, 23.0 K}, T_{M} = {10.0, 10.4, 11.1, 12.3, 14.1, 16.8, 20.7 K}. See Fig. 4 for more details. 

Open with DEXTER  
In the text 
Fig. D.2 Masses M_{F} derived from fitting F_{ν} for the isothermal models of both isolated and embedded starless cores and protostellar envelopes. Compared to the results for isolated models, all derived masses of embedded models are underestimated by approximately a factor of 1.3, owing to background oversubtraction (Sect. 5.2). See Fig. D.1 for SEDs and Fig. 6 for more details. 

Open with DEXTER  
In the text 
Fig. D.3 Masses M_{ℐ} derived from fitting images ℐ_{ν} of the isothermal models of both isolated and embedded starless cores and protostellar envelopes for the correct value of β = 2. Two columns of panels (left, middle) display the masses derived for resolved and unresolved images of the isolated models, whereas the third column of panels (right) present results for unresolved embedded models. Angular resolutions do not make any difference for the isothermal models. See Fig. D.2 and Fig. 8 for more details. 

Open with DEXTER  
In the text 
Fig. E.1 Temperatures T_{F} and masses M_{F} derived from fitting F_{ν} of isolated, embedded, and isothermal models (fits with free variable β) for starless cores (upper) and protostellar envelopes (lower). See Fig. 6 for more details. 

Open with DEXTER  
In the text 
Fig. E.2 Opacity slope β_{F} derived from fitting F_{ν} of isolated, embedded, and isothermal models (fits with free variable β) for starless cores (upper) and protostellar envelopes of selected masses (3, 0.3, and 0.03 M_{⊙}, lower). See Fig. 6 for more details. 

Open with DEXTER  
In the text 
Fig. E.3 Masses M_{ℐ} derived from fitting images ℐ_{ν} of the isolated and embedded starless cores and protostellar envelopes (fits with free variable β). Two columns of panels (left, middle) display the masses derived for the resolved and unresolved images of the isolated models, whereas the third column of panels (right) presents results for the unresolved embedded models. Derived masses of the latter are underestimated by a factor of approximately 1.3 as a result of the conventional procedure of background subtraction (Sect. 5.2). See Fig. 8 for more details. 

Open with DEXTER  
In the text 
Fig. E.4 Relationships between T_{F} and β_{F} derived from fitting fluxes F_{ν} of starless cores and protostellar envelopes (fits with free variable β). Curves plotted with squares (dark colors) and circles (bright colors) in the left and middle panels correspond to the isolated and embedded models, whereas in the right panel they correspond to the isothermal versions of the isolated starless cores and protostellar envelopes, respectively. Results for the starless cores are plotted for all masses 0.03−30 M_{⊙}. Results for the protostellar envelopes, shown for only selected masses (0.03,0.3, and 3 M_{⊙}, three sets of identical lines), span the entire range of luminosities 0.03−30 L_{⊙}. Thick horizontal lines indicate the true value β = 2. For clarity of the plots, error bars are not shown. 

Open with DEXTER  
In the text 
Current usage metrics show cumulative count of Article Views (fulltext article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 4896 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.