Free Access
Issue
A&A
Volume 553, May 2013
Article Number A54
Number of page(s) 11
Section Extragalactic astronomy
DOI https://doi.org/10.1051/0004-6361/201220894
Published online 01 May 2013

© ESO, 2013

1. Introduction

The statistical properties of HII regions in galaxies have been extensively studied since the 1970s (see e.g., Hodge 1976; Kennicutt 1984; Kennicutt et al. 1989; Rozas et al. 1996). These studies, largely via observations of hydrogen recombination lines, provide important clues to the physics of massive star formation. The addition of infrared observations provides insight into those processes that connect star formation to the interstellar medium, and is an additional tool for studying star formation itself. Well-resolved infrared space observations of nearby galaxies are allowing us to apply data with excellent sensitivity and satisfactory angular resolution, which can be matched to their ground-based optical counterparts.

Following the pioneering space-based IRAS infrared survey (Beichman et al. 1988) and the second generation of measurements with ISO (Kessler et al. 1996), the Spitzer Space Telescope (Werner et al. 2004, hereinafter called simply Spitzer) has allowed, and continues to allow, astronomers to survey the thermal emission radiated individual infrared sources in nearby galaxies via its three operational instruments, the Infrared Array Camera (IRAC, Fazio et al. 2004), the Multiband Imaging Photometer for Spitzer (MIPS), and the Infrared Spectrometer, IRS. The Spitzer Infrared Nearby Galaxies Survey (SINGS, Kennicutt et al. 2003) is a comprehensive infrared imaging and spectroscopic survey of 75 galaxies within 30 Mpc, designed to probe the full range of star-forming environments and to provide a heritage archive. The pixel scale of the SINGS-IRAC mosaic images has been refined to 0.75 arcsec/pixel by the SINGS team, based on an original scale of 1.2 arsec/pixel.

The project, for which this is the pilot study, is designed to take advantage of the SINGS archive images. We chose M100 for this study because it has been deeply studied, and we were able to use a comprehensive survey of its HII regions as recorded in Knapen (1998), as well as an Hα image from (Knapen et al. 2004). M100 (NGC 4321) is a grand-design galaxy of class .SXS4 (De Vaucouleurs et al. 1991), with a fairly weak major bar. It is a very well defined spiral, class 12 in the scheme of (Elmegreen & Elmegreen 1987), with arms ranging between 15 arcsec and 150 arcsec from the nucleus. It has an inner bar and a star-forming ring in the circumnuclear region (Knapen et al. 1995a,b). We use the distance obtained in the HST key programme, using Cepheids, of 14.3 Mpc (Freedman et al. 2001).

One of the primary goals here is to compare the infrared luminosity of the HII regions of M100 with that in Hα emission. For this purpose we have used Spitzer IRAC data to complement the Hα data of Knapen (1998). In this paper we present an overview and some key initial results. The structure of the article is as follows. In Sect. 2 we describe the selection of the HII region sample, Section 3 describes the measurement and data analysis, and Sect. 4 gives the results of the analysis. The discussion and summary are in Sects. 5 and 6, respectively.

2. Data and sample

The photometric, image in Hα was taken at the 4.2 m William Herschel Telescope, La Palma (Knapen 1998). The set of HII regions, calibrated in Hα luminosity, and the image in Hα were obtained from the SIMBAD archive. The SINGS/IRAC images of M100 were obtained from the SINGS Public Data Set Archive1.

2.1. Image in Hα emission, and Hα catalogue

The disc of M100 was sampled with a spatial resolution of 1″ and a pixel scale of 0.24″. Knapen et al. (1998, 2003) selected the HII regions for measurement using the following criteria: a region detected with statistical significance needs to have at least nine contiguous pixels with a surface brightness at least three times greater than the noise level of the local background, and the uncertainty in the determination of the surface brightness should not be affected by more than 10% in the weakest region. Regions from the arms, the interarm region, and the circumnuclear region have all been included in the measurements. The sky background in the original image was very uniform, allowing a constant subtraction value for the whole galaxy (Knapen et al. 2003). The luminosities were obtained by integrating the counts within a circular aperture, whose radius was taken as the mean of the minimum and maximum radial extent of a region (regions in general are not perfectly circular), taken as the effective radius of a region. The estimated uncertainty in the luminosity of a given region is 10% (Knapen 1998).

thumbnail Fig. 1

a) Aligned images, left to right: Hα image, IRAC 3.6 μm image, IRAC 4.5 μm image.

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b) Aligned images, left to right: Hα image, IRAC 5.6 μm image, IRAC 8.0 μm image.

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2.2. Infrared Array Camera (IRAC) images

IRAC (Fazio et al. 2004) is an infrared camera operating simultaneously in four wavelength bands centred on 3.6 μm, 4.5 μm, 5.8 μm, and 8.0 μm. There are four detectors, each sized 256 × 256 pixels; InSb detectors cover the 3.6 μm and 4.5 μm bands, while SiAS IBC detectors cover the 5.8 μm and 8.0 μm bands. The sensitivity limits are 0.92, 1.22, 6, and 9 μJy (1σ, 200 s), respectively, in the four bands. The 1σ sensitivity in μJy is given for the low-background case (near the ecliptic poles). The values were calculated based on the sensitivity model given by (Hora et al. 2000). The reference unit for the IRAC images is MJy sr-1 and typical backgrounds in the four bands are 0.15 MJy sr-1, 0.44 MJy sr-1, 2.3 MJy sr-1, and 9.3 MJy sr-1, respectively. The details of the noise level can be seen in the IRAC Pocket Guide (Spitzer’s Science Support Team 2006).

2.3. Selection and location of the samples in the IRAC image frames

In the present pilot study we confined our attention to those HII regions with radii between 2.23 arcsec and 4.46 arcsec. We noticed that some of the HII regions with apparently larger radii than our selected upper limit were mergers of more than one region in the IRAC image frames, which have lower angular resolution than our optical data. We also noted that some regions with smaller radii than our lower limit did not have sufficiently well-defined limits. This effect can also be present in Hα and can cause systematic errors (Knapen et al. 2003), and although Zurita et al. (2000) evolved techniques to surmount this difficulty, we deemed it wiser to use the lower limit stated above. The total number of regions initially selected was 80, although we finally had to exclude two of these because they could not be fitted with a single black body temperature.

To locate the HII regions, registering their positions in the Hα image frame and also the IRAC image frames, we needed to transform the coordinate system of the Hα map (hereinafter we refer to the HII regions taken from this map as K-HII regions) from coordinates in arcsec related to the zero position on the nucleus to a pixel system, which we could then use to make the appropriate cross-reference to the IRAC image frames. We used the IRAF task image.tvmark to mark the location of each selected K-HII region in the Hα image.

3. Measurements and data analysis

3.1. Alignment of the IRAC images with the Hα image

The input unit of the IRAC images is MJy sr-1, and before we aligned the IRAC image frames with the Hα image frame, we transformed this to μJy pixel-1. The precise alignment process was needed to be sure that no data were lost or wrongly cross-correlated when making our comparison. The procedure used the IRAF task image.immatch.srgister. We used the Hα image as the fiducial frame because it has the highest intrinsic resolution. The IRAC images were regridded to give them the same pixel scale as the fiducial frame. They were then rotated, translated, and rescaled so that the final versions are correctly orientated and registered (see Figs. 1a,b).

3.2. Measurement of the K-HII regions in the IRAC bands

The measurement procedure was as follows:

  • (a)

    We displayed the Hα image frame and the four IRAC frames (one at a time) in Ximtool using the IRAF task image.display.

  • (b)

    We marked with an X each of the selected K-HII regions on the Hα frame and on the IRAC image frame. The pixel values of the K-HII regions are listed in Cols. 2 and 3 (xpos-img, ypos-img) in Table 1. This is done using the IRAF task image.tvmark.

  • (c)

    The fluxes of the K-HII regions in the IRAC images were measured using the IRAF task noao.digiphot.appphot.phot.

  • (d)

    The fluxes were determined by integrating in a circular aperture around the centres of the regions.

  • (e)

    The local sky background was subtracted from the measured fluxes to give the final values of the flux measurements. The median relative uncertainties of the measured fluxes were 4.5% in the 3.6 μm band, 5.1% in the 4.5 μm band, 2.2% in the 5.8 μm band, and 1.4% in the 8.0 μm band. These values are consistent with the reported values of about 5% for typical observers (see Spitzer Science Center 2006). In one outlying case (K-HII region 1008) we do find much higher uncertainties (19%, 21%, 10%, and 7% in the four bands, respectively).

Table 1

Measured IRAC fluexes of selected K-HII regions.

3.3. Colour and aperture corrections

According to the IRAC Data Handbook (Spitzer Science Center 2006), it is necessary to apply colour correction and aperture correction for better results, and we proceeded to apply both of these, as well as to perform the error tracking calculations related to the corrections.

thumbnail Fig. 2

a) Histograms of the IRAC fluxes for the K-HII regions of M100 in the four IRAC bands.

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b) Histograms of the colour+aperture-corrected IRAC fluxes in the four IRAC bands.

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3.3.1. Colour correction

The IRAC Data Handbook (Spitzer Science Center 2006) gives a recipe for interpreting the data from sources with spectral shapes different from the nominal shape assumed in the standard calibration procedure. If the energy distribution in flux per octave is not constant, it is advisable to apply the colour correction to the quoted flux densities. The convention used for IRAC is the same as for IRAS (Beichman et al. 1988) and ISO (Blommaert et al. 2003). We applied the colour correction as prescribed in the IRAC Data Handbook.

3.3.2. Aperture correction

The IRAC data are calibrated using aperture photometry on a set of selected stars. The calibration aperture has a radius of ten original (“native”) pixels, 12.2 arcsec, in all four channels. A much smaller on-source aperture is needed for crowded fields. In the presence of extended emission, a small off-source annulus is normally used. The calibration aperture does not take in all the light from the calibration sources, so the extended emission appears too bright in the data products delivered initially (Spitzer Science Center 2006). We first applied the aperture correction to the colour-corrected fluxes using the method described in the IRAC Data Handbook (Spitzer Science Center 2006, see Table 5.7). We then went on to compare the results to the uncorrected values to test whether there was a significant difference. Figure 2 shows the histogram of the IRAC measured fluxes, both corrected and uncorrected, in all four bands.

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a) The IRAC fluxes of a representative HII region (K-HII 1195) for the four bands, with a best black body fit to the three longer wavelength bands. The point at the maximum of the curve is not an observed flux, but the estimated peak flux with its error bar. Also the 3.6 μm flux shows a marked excess over the black body curve.

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b) The same as Fig 3a, but with colour+aperture-corrected fluxes.

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4. Results

4.1. Estimation of colour temperatures Tcol(IRAC) using three bands

When we examined the spectral energy distribution (SED) of a typical K-HII region in the four IRAC bands, we noted that it is possible to fit a black body curve well to the fluxes in the 4.5 μm, 5.8 μm, and 8.0 μm bands (hereinafter the upper three IRAC bands), while the 3.6 μm flux is clearly present in excess. We refer to the literature in Sect. 5.3 to identify the source of this excess. We could infer a colour temperature, Tcol(IRAC), from the fluxes in the three upper bands, which we measured for each region. We made this estimate using a chi-squared black body fit to the measured fluxes and colour+aperture-corrected fluxes.

Figure 3 shows the black body fit to a representative K-HII region (K-HII 1195), using both the uncorrected and colour+aperture-corrected fluxes (herineafter called corrected fluxes). The best fits yield temperatures, Tcol(IRAC), of 284 K without correction, and 264 K with corrections. We found by inspection that the fits to our spectra made using corrected fluxes are slightly better than those made using the uncorrected fluxes. We also present the black body fits using corrected fluxes for a set of sampled K-HII regions in Fig. 42.

One of the main results of the present article is the set of Tcol(IRAC) values for the selected set of K-HII regions (see Sect. 2.3 for the criteria), and in Fig. 5 we present these results as a histogram. We can see that the mode value is 310 K, with a range from 260 to 360 K for the measurements made with uncorrected fluxes (Fig. 5a), while for the measurements with corrected fluxes the mode is 290 K, with a range from 240 K to 340 K (Fig. 5b). The difference in Tcol(IRAC) for the two cases is of order 20 K, with the uncorrected fluxes giving the higher set of values (see Figs. 3 and 5). This range of colour temperatures, which can also be seen in Fig. 6, is surprisingly restricted given the range of luminosities of the regions: some two orders of magnitude in either Hα or IR luminosity (see also Figs. 6 and 7).

thumbnail Fig. 4

The same as Fig. 3b, but with a sample of selected HII regions.

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thumbnail Fig. 5

a) Histogram of colour temperature Tcol(IRAC) for the K-HII regions with measured fluxes in the IRAC bands.

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thumbnail Fig. 5

b) As Fig. 5a but the fluxes here have been subjected to colour+aperture-correction.

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thumbnail Fig. 6

Colour+aperture-corrected values of the colour temperature Tcol(IRAC) against L(Hα) for the K-HII regions sampled with IRAC.

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thumbnail Fig. 7

Colour temperature, Tcol(IRAC) as a function of the infrared luminosity L(IRAC) for the HII regions measured, with colour+aperture-corrected values for L(IRAC) and Tcol(IRAC).

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4.2. Comparison between the IRAC luminosity, L(IRAC), and the Hα luminosity L(Hα)

4.2.1. Values of L(IRAC), L(Hα) and Tcol(IRAC)

We estimate the IRAC luminosity, L(IRAC), from the angular size of the HII region, its colour temperature and the distance of M100, for which we took 14.3 Mpc (Freedman et al. 2001). In Fig. 6 we plot the Hα luminosity, L(Hα) v. Tcol(IRAC), using corrected values, while in Fig. 7 we show the equivalent plot of L(IRAC) v. Tcol(IRAC) (see Table 2). It is clear from Figs. 6 and 7 that there is no functional relationship between Tcol(IRAC) and either L(IRAC) or L(Hα). The positions of the HII regions within the galaxy have been indicated by using separate symbols (see Fig. 6). There is no evidence in Figs. 6 and 7, of any differences in the relations between the luminosities and the IR temperature, of any kind, between these differently placed HII regions. Although there appears to be a convergence towards high luminosities on the mode value of 310 K for the uncorrected graphs or 290 K for the corrected graphs, this could well be a statistical effect, as there are increasingly fewer HII regions as the luminosity increases.

Table 2

Estimated Tcol and L(IRAC) of selected K-HII regions (with corrections).

4.2.2. The correlation between L(Hα) and L(IRAC)

The other main result presented here is the strong positive correlation between the luminosity in Hα and the IR luminosity as measured with IRAC. In Fig. 8 we have plotted these quantities against one another. The linear relationship is strong, with a rank correlation coefficient of 0.79 (see Press et al. 1986), and the two-sided significance level of its deviation from zero is 7.73 × 10-18. In Table 3 we show the values of the slopes and the intercept constants, using two versions of weighting: uniform weighting (weighting factor = 1) and statistical fluctuation weighting (weighting factor = 1/yi, where yi is one of the dependent variables, see Bevington 1969). It is of interest that the slopes of both graphs are unity within the measurement errors. The IR and Hα luminosities are clearly linearly proportional for our sample. This proportionality holds for regions within the arms and in the interarm zone, with an rms scatter of around 0.2 dex. One of the circumnuclear HII regions lies well above the general trend, indicating, in all probability, enhanced conversion of higher energy photons to IR owning to a much higher dust concentration.

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Using colour+aperture-corrected fluxes to derive L(IRAC) v. Hα luminosity L(Hα) for our HII region sample.

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Table 3

The slopes and y-intercepts of L(IRAC) and L(Hα).

4.3. Radial distribution of colour temperatures Tcol(IRAC) in the disc of M100

Most of the regions (60 out of 78) measured here are in the spiral arms of the galaxy, sixteen are in the interarm zones, and only two are in the circumnuclear region. We have plotted the values of the colour temperature, Tcol(IRAC), in the regions against galactocentric radius, in Fig. 9, and it is clear that there is no trend at all with radius. We note this because the electron temperatures in HII regions have been measured to increase with galactocentric radius. This was first found within the Galaxy, using radio recombination lines, by Churchwell & Walmsley (1975), and confirmed by Talent & Dufour (1979). A refined set of observations by Afflerbach et al. (1996) found that if DG is the galactocentric radius of an HII region in the disc of the Galaxy, its electron temperature, Te, can be expressed as Te = 5537( ± 387) + 320DG where Te is in K and DG in kpc. The important point here is the gradient, which has more recently been determined by Quireza et al. (2006) as 287 (±46) K kpc-1. Although measurements with this degree of accuracy are not generally available for external galaxies, a number of authors have used emission line ratios to measure positive gradients in the electron temperature with increasing galactocentric radius dating from the results of Rayo et al. (1982). Our IR measurements span a reasonably wide range of galactocentric radius so that if there were a significant gradient in the temperature Tcol(IRAC) we should expect to detect it. It is apparent from Fig. 9 that there is no significant dependence of Tcol(IRAC) on galactocentric radius, in contrast to the behaviour of the electron temperature.

thumbnail Fig. 9

The radial distribution of colour temperature, Tcol(IRAC) for the K-HII regions in the disc of M100.

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5. Discussion

5.1. Star formation rate

There is a linear relation with high rank coefficient in the log-log plot between L(IRAC) and L(Hα) (see Table 3). The slope of the relation is indistinguishable from unity, and there is a measurable offset. This linear relation should allow us to use the near-IR luminosity of an HII region as a proxy for Hα when estimating the star formation rates associated with HII regions, so that a good estimate of the star formation rate could be derived by combining near-IR with mid-IR observations.

5.2. Dust emission shells around hot star clusters

It is very interesting to note that there is no correlation between L(Hα) and Tcol(IRAC), although there is a tendency for the value of the latter to cluster around 300 K for the high-luminosity regions. This could be accounted for in a scenario where the dust in the ISM surrounding an ionizing star cluster is swept out to beyond a radial distance from the cluster, which depends on the cluster luminosity. In any HII region the dust emission would then come from a thick shell whose inner radius is larger the higher the stellar luminosity. A zero-order approximation gives a baseline result for modelling, that the inner radius of the emitting dust shell should vary as where Lc is the luminosity of the cluster. One possible way of approaching this would be to use a stellar wind model of the type first developed by Castor et al. (1975), which has been extended and used by many subsequent authors. It is generally conceded that the driving mechanism for the wind is the radiation field of the star absorbed directly by the atoms of the circumstellar gas. These winds, which for early-type stars imply mass loss rates of order 10-6M yr-1 and velocities in the range 1000−2000 km s-1, are a key energy source for the ISM. In addition to the wind we should add the possibility that the major photon fluxes from the OB stars can couple directly to the dust and drive it from proximity to the stars. Another mechanism that might lead to the near invariance of the temperatures we measure could be the phenomenon of dust destruction. A number of mechanisms capable of destroying dust grains can act in the neighbourhood of hot stars, among them sputtering or sublimation. (Morales et al. 2011) invoke dust destruction to explain their observations of dust temperatures around main sequence stars, ranging from B8 to K0. Using IRS/MIPS data from Spitzer, in the spectral range from 5 μm to 35 μm they find two distinct dust components with temperatures of ~190 K and ~60 K, with little variation along the sequence. They explain the inner warmer dust as due to small grains released from icy planetesimals, released at a temperature horizon that varies according to the stellar type, yielding an approximately invariant temperature. It is also interesting to note that one proposed heating mechanism for small grains is stochastic heating (Draine & Li 2007), in which a grain is heated by absorption of a single photon. This form of heating implies that the temperatures of the grains do not respond in the same way as they would if they were in thermodynamic equilibrium, so that their mean temperature does not depend on the luminosities of the stars supplying the photons, and depends only slightly on the temperatures of those stars. Although we have outlined here some conceptual models and mechanisms that could, in principle, account for our results, our aim is to present the overview and the key initial results of the new observational studies, and although the models demand detailed exploration, this is beyond the scope of our work in the present article. We intend to turn our attention to them in a future article, based not only on these results but on more extensive statistical/observational studies now under way.

5.3. The origin of the 3.6 μm excess

The origin of the infrared emission in the IRAC bands is thermal emission from heated dust. However, the three longer wavelength bands can be reasonably well fitted by a single black body curve, while the 3.6 μm emission is clearly in excess of the flux extrapolated to shorter wavelengths from this curve. Following what is by now a very extensive literature on the subject, we identify this excess as an emission feature from polycyclic aromatic hydrocarbon molecules, commonly referred to by the initials PAH. They have been attributed to the C-C and C-H stretching and bending vibrations in the PAH molecules (Puget & Léger 1989), while an alternative model, proposed by Duley & Williams (1988) is that they are due to hydrogenated amorphous graphite particles. They have been observed systematically in many types of interstellar IR sources: HII regions, planetary nebulae, and reflection nebulae around early-type stars (see Glass 1999, p. 102). They have also been observed in nearby infrared galaxies, (see e.g., Imanishi et al. 2006, 2008), to choose a couple of examples from numerous published works. While a series of bands in the near-IR appear in a wide variety of dusty interstellar environments and they are now conventionally referred to as PAH features, and although we feel safe in attributing the 3.6 μm excess observed in all the K-HII regions of M100 to a PAH feature, this does not imply that the details of the origin of the feature are fully understood.

6. Summary

We have analysed the near-IR emission from a set of the most luminous HII regions in M100 (referred to here as K-HII regions) as measured from Spitzer with IRAC in the four detection bands of the instrument, with the following results:

  • (a)

    With a chi-squared fitting technique, we could find veryreasonable single-temperature black body fits to the emissionfrom the 4.5 μm, 5.8 μm, and 8.0 μm bands, giving values for the colour temperature of the dust Tcol(IRAC) in the range 250 K−350 K. The difference between the colour+aperture-corrected and the uncorrected temperatures is only some 20 K, and based on this initial study, we can see that colour+aperture correction produces a barely statistically significant difference both to the temperatures and the luminosities obtained.

  • (b)

    There is a clear excess in the 3.6 μm band for all the regions, which we attribute to PAH emission.

  • (c)

    There is a strong correlation between the luminosity, L(Hα), in the Hα emission line and L(IRAC), the total near-IR luminosity for the regions measured. This could be useful. Calibrating the L(Hα) – L(IRAC) relation and combining the IRAC luminosity with the mid-IR luminosity should allow a practical derivation of the star formation rates without direct recourse to Hα observations.

  • (d)

    However, Tcol(IRAC) does not show a correlation with the HII region luminosity, a result that is less intuitively obvious than the correlation between the two luminosities in (c). This could be explained in a scenario in which the mean distance of the emitting dust from the central star cluster is greater for regions of higher luminosity, which harbour ionizing stars of higher luminosity and temperature. A detailed mechanism for this, which could involve stellar winds sweeping out the dust, and/or ablation of dust particles close to the star cluster, will need further exploration. There is a reduction in the scatter of Tcol(IRAC) for regions of higher luminosity, but this might be a statistical effect of the decline in the luminosity function.


2

All the fitting plots are available from the authors on request.

Acknowledgments

We thank the anonymous referee for the suggestions that helped us to make significant improvements to the article. We also thank Dr. Aigen Li for helpful discussions. This research has made use of the SIMBAD data base, operated at the CDS, Strasbourg, France. The work is based partially on observations made with the Spitzer Space Telescope, which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA. S.J.C. thanks the Instituto de Astrofisica de Canarias for support during a working visit. The research was partly supported by projects P3/86 of the Instituto de Astrofisica de Canarias, and AYA2007-67625-CO2-01 of the Spanish Ministry of Science and Innovations.

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All Tables

Table 1

Measured IRAC fluexes of selected K-HII regions.

Table 2

Estimated Tcol and L(IRAC) of selected K-HII regions (with corrections).

Table 3

The slopes and y-intercepts of L(IRAC) and L(Hα).

All Figures

thumbnail Fig. 1

a) Aligned images, left to right: Hα image, IRAC 3.6 μm image, IRAC 4.5 μm image.

Open with DEXTER
In the text
thumbnail Fig. 1

b) Aligned images, left to right: Hα image, IRAC 5.6 μm image, IRAC 8.0 μm image.

Open with DEXTER
In the text
thumbnail Fig. 2

a) Histograms of the IRAC fluxes for the K-HII regions of M100 in the four IRAC bands.

Open with DEXTER
In the text
thumbnail Fig. 2

b) Histograms of the colour+aperture-corrected IRAC fluxes in the four IRAC bands.

Open with DEXTER
In the text
thumbnail Fig. 3

a) The IRAC fluxes of a representative HII region (K-HII 1195) for the four bands, with a best black body fit to the three longer wavelength bands. The point at the maximum of the curve is not an observed flux, but the estimated peak flux with its error bar. Also the 3.6 μm flux shows a marked excess over the black body curve.

Open with DEXTER
In the text
thumbnail Fig. 3

b) The same as Fig 3a, but with colour+aperture-corrected fluxes.

Open with DEXTER
In the text
thumbnail Fig. 4

The same as Fig. 3b, but with a sample of selected HII regions.

Open with DEXTER
In the text
thumbnail Fig. 5

a) Histogram of colour temperature Tcol(IRAC) for the K-HII regions with measured fluxes in the IRAC bands.

Open with DEXTER
In the text
thumbnail Fig. 5

b) As Fig. 5a but the fluxes here have been subjected to colour+aperture-correction.

Open with DEXTER
In the text
thumbnail Fig. 6

Colour+aperture-corrected values of the colour temperature Tcol(IRAC) against L(Hα) for the K-HII regions sampled with IRAC.

Open with DEXTER
In the text
thumbnail Fig. 7

Colour temperature, Tcol(IRAC) as a function of the infrared luminosity L(IRAC) for the HII regions measured, with colour+aperture-corrected values for L(IRAC) and Tcol(IRAC).

Open with DEXTER
In the text
thumbnail Fig. 8

Using colour+aperture-corrected fluxes to derive L(IRAC) v. Hα luminosity L(Hα) for our HII region sample.

Open with DEXTER
In the text
thumbnail Fig. 9

The radial distribution of colour temperature, Tcol(IRAC) for the K-HII regions in the disc of M100.

Open with DEXTER
In the text

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