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A&A
Volume 668, December 2022
Article Number A63
Number of page(s) 27
Section Atomic, molecular, and nuclear data
DOI https://doi.org/10.1051/0004-6361/202244032
Published online 06 December 2022

© W. R. M. Rocha et al. 2022

Licence Creative CommonsOpen Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

This article is published in open access under the Subscribe-to-Open model. Subscribe to A&A to support open access publication.

1 Introduction

Infrared (IR) spectroscopy is a diagnostic tool used to characterize chemical structures of molecules and distinguish their functional groups (e.g., Coblentz 1905; Balkanski 1989). For this reason, a number of laboratories around the world have been focusing on providing laboratory-based IR data of interstellar ice analogs for a range of different ice compositions and temperatures (e.g., Hagen et al. 1979; Strazzulla et al. 1984; Schmitt et al. 1989; Grim et al. 1989; Hudgins et al. 1993; Boudin et al. 1998; Palumbo et al. 1998; Schutte 1999; Muñoz Caro et al. 2002; Öberg et al. 2009; Pilling et al. 2010; Vinogradoff et al. 2015; Terwisscha van Scheltinga et al. 2018, 2021; Urso et al. 2020; Rachid et al. 2020, 2021; Potapov et al. 2021). IR spectra directly represent the molecular geometry of a molecule and as such can act as a molecular fingerprint. In the gas phase and at very high resolution, such rovibrationally resolved spectra are unique; although, overlap may still occur. In the solid state, however, interactions with the ice matrix prohibit molecules from (freely) rotating and cause spectra to broaden and shift with respect to the unperturbed gas-phase value. Additionally, spectral overlaps are more common. The amount of broadening and shifting depends on ice composition (both ice constituents and concentration) and ice temperature, as well as other parameters such as the level of ice porosity. In dedicated laboratory studies, all these parameters can be derived under fully controlled conditions. Examples can be found in Öberg et al. (2007).

IR spectroscopy is also the technique widely used to detect solid-phase molecules in the interstellar medium (ISM; e.g., Gillett & Forrest 1973; Schutte et al. 1996; Pontoppidan et al. 2003; Gibb et al. 2004; Boogert et al. 2008, 2013; Zasowski et al. 2009; Bottinelli et al. 2010; Penteado et al. 2015; Perotti et al. 2020; Rocha et al. 2021; Onaka et al. 2021). The light of a protostar, edge-on disk, or background star passes through the circumstellar material, and absorption features in the IR are seen in the protostellar spectral energy distribution (SED). The correct interpretation of those absorption bands is only possible upon comparison with the spectra of ice analogs measured in the laboratory. With this methodology, important discoveries have been made through observations of space- and ground-based telescopes such as the Infrared Space Observatory (ISO), the Spitzer Space Telescope/Infrared Spectrograph (IRS), and the Infrared Spectrometer And Array Camera mounted on the Very Large Telescope (VLT/ISAAC). To date, the molecules securely identified in ices are H2O, CO, CO2, NH3, CH4, and CH3OH (Öberg et al. 2011; Boogert et al. 2015), and the isotopologues are 13CO and 13CO2 (Boogert et al. 2002a). Except in the cases of CO and 13CO, which have only one vibrational mode, these molecules were identified in astrophysical ices by the detection of multiple absorption bands across the IR spectrum. These identifications in ices allowed us to study the solid-phase chemistry in different astrophysical environments. For example, amorphous water ice is predominantly found toward background stars and low-mass protostars (Smith et al. 1989; Boogert et al. 2008), whereas a certain fraction of crystalline water ice was found in the circumstellar material of high-mass protostars (Dartois et al. 2002). CO is also an important discriminator of the ice environment, and astronomical observations indicate that it does not only exist in the pure form, but it can also be mixed with CO2, H2O, or CH3OH (e.g., Pontoppidan et al. 2003; Cuppen et al. 2011). In the case of CO2 ice, the bending mode around 15 µm provides a diagnostic of heating and segregation of polar and apolar molecules in ices (e.g., Ehrenfreund et al. 1996; Pontoppidan et al. 2008; Isokoski et al. 2013). Among the list of molecules identified in ices, CH3OH (methanol) belongs to the group of so-called complex organic molecules (COMs), which, in astrochemistry, are defined as organic molecules containing six or more atoms (e.g., CxHyYz, with Y = O, N, P, S; Herbst & van Dishoeck 2009). A number of small molecules have been tentatively identified in ices for which only one vibrational mode could be assigned from astronomical observations. This list also includes sulfur-bearing molecules (notably, SO2; Boogert et al. 1997) and ions (notably OCN−; Schutte & Greenberg 1997).

Many different COMs have been identified in the gas phase through radio and submillimeter surveys (e.g., Blake et al. 1987; Jørgensen et al. 2012, 2020; McGuire et al. 2016; Belloche et al. 2020; van Gelder et al. 2020; McGuire 2022; Nazari et al. 2021; Rivilla et al. 2021; Brunken et al. 2022), but astronomical observations have not been able to unambiguously identify frozen COMs larger than CH3OH due to low spectral resolution or sensitivity. Nevertheless, tentative detections of CH3CHO (acetaldehyde) and CH3CH2OH (ethanol) ice have been reported in the literature (Schutte et al. 1999; Öberg et al. 2011; Terwisscha van Scheltinga et al. 2018; Rocha & Pilling 2015; Rocha et al. 2021). Consistently with these tentative detections, several laboratory experiments have shown that such molecules can be formed in ices. Some examples are interstellar ice analogs processed by UV radiation (e.g., Bernstein et al. 1995; Muñoz Caro & Schutte 2003; Öberg et al. 2009; Meinert et al. 2016; Öberg 2016; Nuevo et al. 2018; Ishibashi et al. 2021; Bulak et al. 2021), electron bombardment (e.g., Brown et al. 1982; Materese et al. 2015; Mifsud et al. 2021), X-rays (e.g., Pilling & Bergantini 2015; Ciaravella et al. 2019), cosmic rays (e.g., Hudson et al. 2001; Domaracka et al. 2010; Pilling et al. 2010), and via thermal processing (e.g., Danger et al. 2011; Theulé et al. 2013). Other mechanisms excluding the presence of energetic triggers, such as atom addition reactions that are more representative of dark cloud conditions, have also been shown to result in the formation of COMs (Watanabe & Kouchi 2002; Fuchs et al. 2009; Theulé et al. 2013; Linnartz et al. 2015; Fedoseev et al. 2017; Ioppolo et al. 2021).

Apart from IR spectroscopy, the complex refractive index (CRI) of ice samples is important for the interpretation of astronomical observations. CRI is given by a complex number, , where n and k are the real and imaginary parts and are associated with scattering and absorption effects, respectively. In protostellar environments, CRI has been used to evaluate the effect of icy grain sizes and shapes in the spectral features of ices (e.g., Ehrenfreund et al. 1997; Boogert et al. 2002b, 2008; Pontoppidan et al. 2005; Rocha & Pilling 2015; Perotti et al. 2020; Dartois et al. 2022). For example, Boogert et al. (2008) observed a dependence of the libration mode of H2O ice peak position on the size of spherical grains. Better fits of this band are obtained when small grains are adopted in the models. Similarly, CRI values have been used to interpret the absorption band at 3 µm, which is associated with the O–H stretching mode of H2O (e.g., Smith et al. 1989; Dartois & d’Hendecourt 2001). In the Solar System, the CRI also plays a crucial role in the simulation of reflected light due to icy surfaces to interpret spectral observations. (e.g., Clark et al. 2012; Dalle Ore et al. 2015). Finally, the CRI may be used to construct opacities for a dust grain size distribution model (Weingartner & Draine 2001), which can be used with a radiative transfer code to self-consistently calculate the temperature and density distributions of dusty astronomical objects, for example, protoplanetary disks (D’Alessio et al. 2006).

The advances in the identification of molecules in both the gas and solid phases have been strongly supported by atomic and molecular data in open-access databases. In fact, electronic databases have become an essential tool in the context of astrochemistry, given the large amount of data that are produced by laboratory experiments. In particular, the astrochemical community targeting gas-phase chemical species is well served with multiple databases. For example, the Cologne Database for Molecular Spectroscopy1 (CDMS; Müller et al. 2001, 2005; Endres et al. 2016) and the Jet Propulsion Laboratory2 (JPL; Pickett et al. 1998; Pearson et al. 2010) databases provide catalogs with transition frequencies, energy levels, and line strengths for atoms and molecules in the gas-phase of astrophysical and atmospheric interest. Collisional rate coefficients are available through the Leiden Atomic and Molecular Database (LAMDA)3 for non-LTE excitation (Schöier et al. 2005; van der Tak et al. 2020). Similarly, BASECOL contains a repository of collisional ro-vibrational excitation data of molecules by colliding with different agents such as atoms, ions, molecules, or electrons (Dubernet et al. 2006, 2013). More oriented to chemical reactions, the UMIST Database for Astrochemistry4 (UDfA; McElroy et al. 2013) contains the reaction rates of more than 6000 gas-phase reactions. In a similar vein, the Kinetic Database for Astrochemistry5 (KIDA; Wakelam et al. 2012) has provided reaction rate coefficients for a massive number of chemical species for astrochemical studies. The photodissociation and photoionization values of gas-phase molecules relevant for astrophysics are available online6 and described by Heays et al. (2017), van Dishoeck et al. (2006), and van Dishoeck (1988). The properties of gas-phase polycyclic aromatic hydrocarbons (PAHs) are widely available through the NASA Ames PAH IR Spectroscopy Database7 (Bauschlicher et al. 2010; Boersma et al. 2014; Mattioda et al. 2020).

The astrochemistry community working with solid-phase materials has also been served with databases. The refractive index of refractory materials is available via the Database of Optical Constants for Cosmic Dust8 (Henning et al. 1999; Jäger et al. 2003). Likewise, IR spectra of binary ice mixtures and refractive indexes of pure ices can be found on the web page of the Cosmic Ice Laboratory9 from NASA (e.g., Moore et al. 2010; Knez et al. 2012; Gerakines & Hudson 2020) and at Databases of the Astrophysics & Astrochemistry Laboratory10, which contains measurements by Hudgins et al. (1993). A database of refractive indices of ice samples irradiated by heavy ions is also available on the Laboratório de Astroquímica e Astrobiologia da Univap (LASA) webpage11 with calculations performed by Rocha & Pilling (2014, 2018), and Rocha et al. (2020). Infrared refractive indices of CO and CO2 ices are available from the Experimental Astrophysics Laboratory on the Catania Astrophysical Observatory website12 (Baratta & Palumbo 1998). Finally, we also mention the Solid Spectroscopy Hosting Architecture of Databases and Expertise13 (SSHADE; Schmitt et al. 2018), which contains a compilation of spectral and photometric data obtained by various spectroscopic techniques over the whole electromagnetic spectrum from gamma to radio wavelengths, through X-rays, UV, vis, IR, and millimeter ranges. The data are not limited to ices, but also contain measurements of liquids, minerals, rocks, and organic and carbonaceous materials.

Similarly to many of the databases mentioned above, the Leiden Database for Ices has served the astronomical community since the 1990s, but until recently no COM spectra were included, and the spectral resolution of the data was around 1–2 cm−1 (e.g., Gerakines et al. 1996; Ehrenfreund et al. 1996, 1997). Additionally, the data were fragmented into several databases targeting specific ice samples. To continue supporting the interpretation of ice observations with current and future telescopes, in particular the James Webb Space Telescope (JWST), we have fully upgraded the Leiden Ice Database for Astrochemistry (LIDA14). In particular, LIDA is a deliverable of the Early Release Science program ICE AGE15 (PI: Melissa McClure; Co-PIs: Adwin Boogert, Harold Linnartz). In LIDA, all data are now available in one central location, and appealing features are included such as a search capability and dynamic data visualization. Additionally, online tools are included in LIDA to support JWST data analysis or to prepare observing blocks, by deriving integration times based on expected column densities. LIDA covers the most abundant solid-phase species observed in the ISM, which are listed in Table 1, along with information about their secure, tentative, or non-identification in the solid phase in astrophysical environments from previous observations. The JWST has the technical potential to enlarge the inventory of ice identifications in space, and several programs (ERS, Guaranteed Time Observations – GTO and General Observer - GO) will search for new ice features toward protostars and background stars using high spatial- and spectral-resolution observing modes. Moreover, the JWST will shed light on the mystery surrounding the formation of COMs in ices. For this purpose, comparison with spectra of COMs in astrophysically relevant ice matrices at high spectral resolution is needed. Such data are required for a range of different physical conditions, such as mixing ratios, temperatures and porosity levels, as these differences affect the spectral appearance of the ice absorption bands.

This manuscript systematically guides (new) users through LIDA. Section 2 and the appendix give an overview of the data available in the 2022 version of LIDA and describe the type of data and how they were obtained. Section 3 provides information about the database structure, namely, the relational design, web interface, and visualization tools. In Sect. 4, we introduce the computational online tools dedicated to supporting JWST data analysis. An application illustrating the potential of LIDA is also shown. Section 5 points out the upgrades on LIDA that are intended for the coming years. A summary of this work is provided in Sect. 6.

2 Data in the database

In this section, we provide details on the experimental techniques used to measure the data available through LIDA. In summary, LIDA contains mid-infrared (mid-IR) spectra of ice samples (~4000-500 cm−1; 2.5–20 µm) and ultraviolet-visible (UV/vis) to mid-IR refractive indices of water ice in the 0.25–20 µm range. The IR ice spectra are available for pure and mixed ices for different settings. The amorphous and crystalline H2O ice refractive indices (n and k) are available for ices deposited at different temperatures.

2.1 Experimental setups and ice growth techniques

Between 1990 and 2020, the majority of the IR data in Leiden were recorded with our HV setup, a regular high vacuum setup (10−7 mbar) in which the broadband light of a Fourier Transform IR spectrometer (best resolution 0.1 cm−1) is transmitted through a cryogenically cooled substrate covered with ice that is grown under fully controlled laboratory conditions (Fig. 1a). The transmitted beam is focused into a detector and processed with a Fourier transform to provide the transmitted light per wavelength. In the 1990s, this setup was also equipped with a microwave discharge hydrogen flow lamp that was used to irradiate ice samples with a flux of ~ 1015 photons cm−2 s−1 dominated by Lyman-α emission in order to study radical or ionic ice constituents or species formed upon irradiation (Gerakines et al. 1996). The HV setup has been regularly upgraded and details are available in Öberg et al. (2009). A new setup has been used since 2020: infrared absorption setup for ice spectroscopy (IRASIS) uses the same measurement principle but operates at substantially lower pressures (10−9 mbar) to minimize contaminants. Moreover, laser interferometry has been incorporated to perform thickness measurements in order to derive experimental absorption cross-sections. All these data are recorded in transmission. Details concerning IRASIS are available in Rachid et al. (2021). In the near future, a quartz crystal micro-balance will also be incorporated. A few spectra available from LIDA have been recorded using reflection absorption IR (RAIRS) spectroscopy (Fig. 1b), but they are not included in the collection of ice spectra presented in this paper. The reader can find more details about those experiments in van Broekhuizen (2005), Fuchs et al. (2006), Öberg et al. (2009), Fayolle et al. (2011) and Ligterink et al. (2018); moreover, the CRYOgenic Photoproduct Analysis Device (CRYOPAD) is a setup that uses RAIRS and is dedicated to study the impact of vacuum UV irradiation on ice samples.

Apart from ice spectra, we also present real UV/vis refractive index measurements at cryogenic temperatures using our optical absorption setup for ice spectroscopy (OASIS; Kofman et al. 2019; He et al. 2022). The base pressure on OASIS is around 6×10−8 mbar. In this setup, a light source impinges on the growing ice and the reflected beam creates an interference pattern (Fig. 1c). Specifically, an Xe arc lamp and an HeNe laser (632 um) are the light sources for the interference technique (Fig. 1d). The light from the Xe arc lamp strikes the growing ice at 45 degrees and is reflected toward the aperture of an Andor 303i Shamrock spectrometer. In the spectrometer, the light is dispersed and collected on a charge-coupled device (CCD; Andor iDus DV420 OE), which allows us to record the interference pattern at different wavelengths in the 250 and 750 nm region. The HeNe beam strikes the ice at a low angle (~3°) and is recorded by a photodetector. The interference pattern is later used to derive the refractive index of the ice (see Sect. 2.3). So far, the OASIS experiments have targeted measurements of pure ices. In the near future, this setup will be used to measure the refractive index of binary ice mixtures as well.

In IRASIS, OASIS, and CRYOPAD, a single gas/vapour component or a gaseous mixture is introduced into the chamber through a controllable leak valve and deposited onto a cold substrate. Usually, the substrate used in transmission spectroscopy is one of the following materials: potassium bromide – KBr, zinc selenide – ZnSe, or germanium – Ge; whereas, gold (Au) is used for RAIRS. A UV-enhanced aluminium mirror is used as a substrate in refractive index experiments. In most of the data available from LΓDA, the ices are background-deposited, which means that the gas inlet does not point toward the sample, allowing the molecules to impinge onto the substrate coming from random directions and to adhere to both sides of it. This is more representative of the way molecules interact with an icy dust grain in space and generally causes the ice to be somewhat more porous. In the case of mixed ices, the samples can be prepared in a separate mixing system or by admitting the individual gas/vapor components in the chamber through different dosing lines. In either case, the molecules are considered to be homogeneously mixed before freezing out onto the cooled substrate. The ice thickness is often given as the number of monolayers or as Langmuir, where one monolayer (1L) corresponds to 1015 cm−2 (Langmuir 1938). In IR spectroscopy experiments, the ice thickness can be as thin as a few monolayers. In some cases, when ice mixtures are used, the ice has to be thicker (~3000 ML) in order to allow the detection of the less abundant molecular component in the sample or guarantee that the deposition of background gases during the measurement is negligible (see Terwisscha van Scheltinga et al. 2018). In experiments to measure the ice refractive index, the ice thickness is generally much thicker (~45 000 ML) because the technique requires us to record several fringes in the interference pattern. It is worth mentioning that the shape and position of the IR bands are not affected by the ice thickness or the underlying substrate used in the experiments performing transmission spectroscopy.

Table 1

List of molecules with relevant data on LIDA and their solid-phase (tentative or non-) detection in the ISM.

thumbnail Fig. 1

Overview of experimental techniques used to obtain data hosted in LIDA. The left panel shows two infrared spectroscopic techniques used to record the spectrum of ices; namely, transmission mode (a) and reflection-absorption mode (b). The right panel illustrates the technique used to measure UV/vis refractive index values. Cartoon c provides details of the diffraction and reflection phenomena that generate an interference pattern during ice growth. Cartoon d shows the incidence and reflection of the monochromatic and broadband light beams when interacting with the ice.

2.2 Absorbance spectrum

The majority of the IR absorbance spectra in LIDA have been measured using transmission spectroscopy. The basic principle to measure the absorbance spectrum is that the incident radiation is attenuated when crossing the ice sample due to the intrinsic properties of the material. The intensity of the transmitted light at each wavelength is calculated with Lambert-Beer’s law, which is given by (1)

where is the incident light intensity, αλ is the wavelength-dependent absorption coefficient, r is the concentration in the sample, and ℓ is the effective radiation path within the ice. The absorbance is derived from Eq. (1) as shown below: (2)

where the absorbance is directly proportional to the molecular concentration and the radiation path in the ice. In transmission spectroscopy, the substrate is transparent to the IR light, and the absorption bands observed in the IR spectra are due to the molecules in the ice sample.

When RAIRS is used, the absorbance is no longer obtained from Eq. (2). The absorbance spectrum is calculated as a function of the reflected light, and depending on the ice thickness, the geometry of the light path, and the setup itself, the spectrum can change substantially. Briefly, in RAIRS, the IR light shines onto a reflective gold (Au) surface at grazing angles (~90° with regard to surface normal) and is reflected toward the detector (Fig. 1b). Upon specular reflection, the s-polarized light becomes negligible, and only the p-polarized component interacts with the molecules. In this way, RAIRS has an additional selection rule for absorption, which imposes that the vibrational motions have a component orthogonal to the reflection surface (Palumbo et al. 2006). RAIRS comes with the disadvantage that spectra cannot be directly compared with astronomical data, as in the case of spectra recorded in transmission. On the other hand, RAIRS has the advantage of increasing the signal-to-noise ratio of the data.

Either transmission or RAIRS can record data of pure or mixed ices before and after warm-up, or processed by UV radiation. The ice spectrum is taken by averaging a certain number of scans, allowing a higher signal-to-noise ratio, and typical spectral resolutions are within 0.5 cm−1 and 2.0 cm−1, whereas 0.1 cm−1 spectra can be recorded if needed. Likewise, the absorbance accuracy is a characteristic of the IR spectrometer and is around 1%. Warm-up experiments are performed by depositing the ice at a low temperature (~10 K), followed by a slow increase of the substrate temperature (e.g., 25 K h−1) while IR spectra are continuously taken. In the cases where experiments with UV ice processing are performed, the absorbance spectrum is taken after the irradiation process. The recorded IR absorbance spectrum often shows a curved baseline, which needs to be corrected. Typically, a low-order polynomial function is used to flatten the ice spectrum and perform corrections to remove artifacts from the IR spectra. The baseline correction is made by interpolating a function for wavelengths where there is no absorption and subtracting it from the original signal. The spectra contained in the database have previously been baseline-corrected using a polynomial or linear function to set the data to zero absorbance where there are no absorption features. When available, the non-baseline-corrected spectrum is downloadable. The original spectra (raw data) are not downloadable; this is to avoid the publication of data in the literature that have not been treated correctly. This requires appropriate knowledge of how to deal with these datasets.

For astrochemical applications, the absorbance spectrum is often converted to an optical-depth scale. The optical depth of experimental data is given by D’Hendecourt & Allamandola (1986): (3)

Ultimately, is converted to the wave number domain (), and used to calculate the column density of the ice sample from the equation below: (4)

in which A is the band strength of the vibrational modes associated with the absorption features. Most of the A values in the literature have been derived for pure ice samples (e.g., Gerakines et al. 1995, 1996; Kerkhof et al. 1999; Bouilloud et al. 2015; Hudson et al. 2017). However, Öberg et al. (2007) and Bouwman et al. (2007) showed that a variation in the chemical composition of the ice leads to changes in the band strength of solid-phase molecules. These changes are often reported as relative values with respect to the pure ice, because information such as the ice density is unknown when the molecular concentrations change within the ice. In Table 2, we compile A values from the literature for pure ices, which were then used to derive the column densities of the ices in LIDA. These values were also used to derive the column densities of most of the ice mixtures. Otherwise, we used tabulated values from Öberg et al. (2007) to derive the column densities of H2O:CO2, and the values from Terwisscha van Scheltinga et al. (2018, 2021), Rachid et al. (2020, 2021) to derive the column densities of ice-containing COMs.

When an ice column density is derived from RAIRS data, a correction must be performed on the band strength values from the literature. For spectra measured with RAIRS, the ice column density is given by (5)

where R is the correction factor. Specifically, in RAIRS experiments the path length of the light beam is longer across the ice than in transmission IR spectroscopy. Consequently, band strengths measured with RAIRS are no longer the same as those measured in transmission. This correction depends on the molecule and individual calibration experiments performed. Since this paper only presents results from transmission spectroscopy, the R values are not provided. Future upgrades of LIDA will include RAIRS data and their respective correction factors for the ice column density calculation.

Spectra of mixed ices are shown in the database, with the ratio between the molecules given in the label. For example, in the H2O:CO2 (10:1) ice there are ten molecules of H2O for each molecule of CO2 in the ice. Layered ices can also be made by depositing a certain number of monolayers (1 monolayer - ML ~ 1015 molecules cm−2) of a pure molecule on the substrate followed by a number of ML of another pure or mixed ice. When this is the case, the mixture is named “CO over CO2”, which means that pure CO was deposited on top of a pure CO2 ice. Similarly, “CO under CO2” means that CO was deposited in the bottom layer, followed by CO2 deposition on the top layer.

In Fig. 2, we show the fraction of pure and mixed samples hosted in LΓDA. The majority of the IR spectra are of binary ice samples accounting for 52.4%. Most of these samples are mixtures of simple molecules (e.g., H2O, CO, CO2). Recently, binary ices containing COMs (e.g., CH3CHO, CH3CH2OH, CH3OCH3, CH3NH2, CH3OCHO, CH3COCH3, CH3CN) have been included in the database. The second biggest group (25.2%) is of ice samples containing three compounds, which may include a COM. Pure ice samples make up the third group (17.5%) and contain simple and complex molecules, as well as ions ( and OCN−). We note, however, that these ions are formed via the heating of HNCO:NH3 ice (Novozamsky et al. 2001). Moreover, some of the pure ice samples were exposed to UV radiation with experimental details given in Gerakines et al. (1996). Finally, the groups of quaternary and five-component ice samples are combined and account for 4.9% of all spectra in the database. A full list with all ice analogs in the database is presented in Table A.1.

Table 2

List of vibrational transitions and band strengths of the molecules in pure ices as included here and presented in the literature.

thumbnail Fig. 2

Pie chart displaying the percentage division of ice analogs in LIDA from pure samples to five-component mixtures.

2.3 UV/visible and mid-IR refractive index

When light shines upon the ice surface, part of it refracts into the ice, and part of it is specularly reflected by the surface (Figs, 1c and d). The refracted beam is reflected in the ice-substrate interface and eventually emerges back into the vacuum. The phase difference (Δ) between the light rays that pass through the ice and the ones reflected by the surface is related to their optical path difference (δ), which is given by (6a) (6b)

where λ is the wavelength of the incoming light, n is the real part of the ice refractive index at wavelength λ, d is the ice thickness, and θ2 is the refraction angle (see Fig. 1c), that is the angle between the refracted light and the normal plane perpendicular to the ice. The incident angle θ1 is related to the refraction angle θ2 by Snell’s law. When δ/λ is an even number, Δ is a multiple of 2π, resulting in constructive interference of the light beams. Conversely, when δ/λ is an odd number, the interference is destructive. Consequently, the intensity of the resulting beam reflected by the ice surface is an oscillation pattern with the following form: (7)

where A and B are constants. Thus, the intensity of the interference pattern carries information about both the refractive index and the rate at which the ice thickness increases during deposition. Since each of these parameters is unknown, they cannot be derived from a single interference measurement. However, by recording the interference pattern of growing ice employing two different incident angles or wavelengths and employing Eq. (7), both the ice refractive index and the growth rate can be derived. By recording the interference pattern of growing ice using two light beams of the same wavelength (λ) but different angles (α, β), the refractive index expression can be derived from the frequency of the oscillations (Eq. (7)) and Snell’s law: (8)

where Pα and Pβ are the periods of the interference patterns generated by the light beams striking the ice at angles α and β, respectively. For more details about the derivation of Eq. (8), see, for example, Tempelmeyer & Mills Jr (1968), Beltrán et al. (2015), and He et al. (2022).

While Eq. (8) provides the ice refractive index in the UV/vis range, the refractive index in the mid-IR can be calculated using the Kramers-Kronig relations (Kronig 1926; Kramers 1927), which are given by (9)

where n670 nm is the refractive index of the sample at 670 nm and is within the UV/vis range for which the refractive index was derived (through Eq. (8)), v is the wave number corresponding to the peak of the band, and v′ is the wave number before and after the v value. The Cauchy principal value P is used to overcome the singularity when v = v′. The term “k” corresponds to the imaginary part of CRI and is given by (10)

where Absv is the absorbance spectrum value (Eq. (2)), d is the thickness of the ice sample, and , and are the Fresnel coefficients. The sub-labels 0, 1, and 2 refer to vacuum, ice sample, and substrate regions, respectively. The refractive index of the substrate is implicit in the terms and . Finally, the term is given by ; is the CRI.

To determine the real and imaginary refractive index in the mid-IR, LIDA provides tools to solve Eqs. (9) and (10) numerically via an iterative procedure. Specifically, LΓDA uses the Maclaurin formula described in Ohta & Ishida (1988) to obtain the real refractive index, and, subsequently, the imaginary refractive index is derived. This methodology was also employed in other computational codes dedicated to calculating the CRI values of ice samples (Rocha & Pilling 2014; Gerakines & Hudson 2020).

In the current version of LIDA, we present the H2O ice refractive index in the UV/vis, measured on the OASIS setup, and mid-IR optical constants calculated with the online tool available in LIDA (see Sect. 4.2). In a follow-up paper, the refractive indexes of pure H2O shown in this database will be systematically compared with the literature values (Rocha et al., in prep.). The refractive index values of other molecules (e.g., CO, CO2, N2, CH4, CH3OH) have in part already been measured and will be included in future LIDA upgrades. These will also include astronomically relevant ice mixtures.

3 Features of the database

The upgraded LIDA is an extendable platform designed to host IR spectra and UV/vis refractive indices of ice samples, as well as to support the uploading of new datasets that will be obtained in future experiments. Access to these data is obtained with dynamical and interactive visualization software that is also linked to online tools to perform astronomy-oriented calculations. Additionally, all data are available for download in a standard ascii format. In the next subsections, we provide details about different aspects of the database. More information describing the software and approaches used to construct the database are given in Appendix B and interactive documentation is available in the online documentation16.

3.1 User interface

The user interface of LIDA shows four sub-modules, namely, (i) spectral data, (ii) optical constants, (iii) online tools, and (iv) further information and a contact form. Access to these sub-modules is obtained via the navigation bar at the top of the web interface. All IR ice spectra are available in the submodule named spectral data, which currently counts for more than 1100 ice spectra related to over 150 different ice samples. In the future, new data will be added, and the option exists to add previously recorded data that are currently scattered over the literature. The “optical constants” section only contains the real refractive index of H2O ice at different temperatures for now. However, more data from ongoing experiments will be added, which includes measurements of N2, CO, CO2, CH4, and CH3OH. LIDA is also equipped with online tools focused on astronomy-oriented calculations. Finally, the user can visualize the credits, and contact the developers and scientific managers of the database. To render the database user interface, we used common web technologies such as HyperText Markup Language (HTML), Cascading Style Sheets (CSS), and JavaScript (JS). A list of all software used to develop LIDA is available at the “credit” section17 of LIDA.

3.2 Search capability and metadata

The IR spectra and the UV/vis optical constants of the ice analogs in LIDA can be searched via a search box by accessing the spectral data and optical constants tabs, respectively, in the navigation bar. The search capability uses SQLAlchemy18, a python SQL (Structured Query Language) toolkit, and Object Relational Mapper to enable searches in Flask applications.

To find a specific analog in the database, either the chemical formula or the molecule name can be used. For example, the user can type water or H2O to search for water ice spectra in the database. Searching for ice mixtures is possible by providing a list of the chemical formulas separated by a space (e.g., H2O CO2 CH3OH). LIDA can also be used to search for molecules sharing common chemical structures. For example, when the query is CO, a list of all the molecules containing a carbon-oxygen bond (both simple and double) will be displayed on the web interface (e.g., CH3OH, CH3CHO, HCOOH). Searching for more specific structures, such as functional groups, is also possible. As an example, if the query is COOH, a list with samples containing molecules that carry a carboxylic acid functional group will be returned (e.g., HCOOH). LIDA also supports searching by the type of ice processing. For example, thermally processed water ice can be searched for with H2O category=warm-up. Similarly, an energetically processed ice can be searched for with H2O category=irradiation. Finally, the user can also search for a spectrum by the author who published the data with the command H2O author=Öberg.

By searching for a specific ice sample, the user can also visualize the metadata. For example, information such as spectral resolution, deposition temperature, ice thickness, and publication are visible. All spectra hosted in LIDA are available for download in ascii format (e.g., .txt extension), which is a standard format that can be imported to several software and computational codes. This feature enables the downloading of a single spectrum or all spectra related to an ice analog.

3.3 Data visualization

The data in LIDA are plotted interactively with Bokeh19, a Python library for interactive visualization (Bokeh Development Team 2018). This software provides several control buttons by default to support the interactive inspection of the plots. More details can be accessed via the Bokeh documentation.

As an example of the data visualization in LIDA, Fig. 3 shows the IR absorbance spectrum of pure H2O ice at temperatures of 15, 45, 75, 105, and 135 K (Öberg et al. 2007). The color of each spectrum is linked to the temperature for which the data was recorded; in this case, blue and red are the lowest and highest temperatures, respectively. The spectral visualization in LIDA also contains the annotations for the vibrational modes of the molecule. Four spectral features are indicated for H2O ice. The feature around 3800 cm−1 (2.63 µm) corresponds to the free OH stretching or dangling bond. This band is often observed in amorphous water ice, and decreases upon compaction after ion irradiation of the ice as shown by Palumbo (2006). However, Bossa et al. (2014, 2015) suggest that the OH dangling bond is only a partially suited tracer of ice porosity, as a non-detection does not fully exclude that an ice is still somewhat porous. After the ice is warmed up, the dangling bond is no longer observed in this water ice spectrum. The most prominent feature is the absorption band around 3300 cm−1 (3 µm), which refers to the OH bulk stretching in the ice. This band is broad and relatively symmetric at low temperatures, whereas it becomes narrow and sharp at higher temperatures. This variation in the shape of the band is due to the phase transition of water ice from the amorphous to the crystalline structure. The water bending mode is observed at 1666 cm−1 (6 µm). The effect of the temperature on this feature is the flattening of the band during heating. Finally, the libration water band is observed around 800 cm−1. The peak position of this band is also sensitive to the physical conditions of the ice and is blueshifted at higher temperatures.

In Fig. 4, we display the UV/vis and mid-IR refractive indices (0.25–20 µm) of pure H2O ice at 30, 50, 100, and 150 K. The UV/vis was measured on the OASIS setup (He et al. 2022), whereas the mid-IR values were calculated using the refractive index calculator available via LIDA (see Sect. 4.2). The water ice refractive index shows a clear dependence on the temperature. In particular, the real refractive index at 670 nm is adopted as 1.29 and 1.32 for the amorphous and crystalline phases, respectively (e.g., Warren 1984; Hudgins et al. 1993; Mastrapa et al. 2008, 2009), which are higher than the values presented in this paper.

thumbnail Fig. 3

Screenshot of spectrum visualization window showing IR spectrum of H2O ice at different temperatures given by the color-code. The annotations of the water vibrational modes are shown in green. They can be hidden by clicking on the yellow toggle below the plot. It also describes the annotation color-code; green means the vibrational mode is calculated, and black indicates the vibrational mode is not calculated. The hover set at the position around 3000 cm−1 displays the information of the spectral data point, such as the wave number in cm−1 (bottom X-axis), wavelength in µm (top X-axis), and absorbance (Y-axis). The toolbar is placed on the right side of the plot.
thumbnail Fig. 4

Screenshot showing UV/vis and mid-IR refractive index of pure H2O at different temperatures. The color is associated with the temperature of the ice. The hover shows the wave number in cm−1 and refractive index values as indicated in this figure.
thumbnail Fig. 5

Screenshot of three-dimensional (3D) molecule viewer embedded in LIDA using the JSmol package. The viewer is connected to external public databases of molecule structures and can be viewed using the searching bar below the molecules. A few dedicated controls can be used to move and animate the molecule. The calculated vibrational modes can be listed and animated using the green button. As proof of concept, the yellow arrows indicate the vibrational motion of the water bending mode seen at 1643 cm−1 in Fig. 3. Buttons for making a movie and downloading the image file in PNG format are also provided.

3.4 3D molecule viewer

The 3D molecule viewer aims to provide complementary information about the molecules in the ice analogs available through LIDA. The viewer is built with Jmol20, an open-source Java package for the visualization of chemical structures in 3D (Jmol development team: Accessed in June 2021). The web rendering of the viewer is done via JSmol, an interactive browser object that is written in JavaScript and utilizes HTML5.

JSmol has several built-in functions that are also available in this tool, such as measurements of distances and angles and the visualization of vibrational modes, animations, orbitals, and surfaces. As a 3D viewer, the molecule can be rotated to different angles, and the type of the bonds can be changed to wire frames. A few dedicated controls are available in the viewer of LIDA; for example, “spin” to rotate the molecule, “vibration” to show an animation of the vibrational modes and “vectors” to show the direction of the vibration modes of the functional groups. All these capabilities are important for a better understanding of the spectroscopic properties of the molecules available in LIDA. It should be noted that in the ice environment molecular rotations are quenched and vibrations are hindered depending on the ice matrix. Furthermore, the ice geometry changes with the variation of the temperature and upon irradiation, which also affects the molecular vibrations.

With JSmol linked to LIDA, one can animate the normal vibrational modes of the molecules when visualizing their IR ice spectra. This is performed by reading “.xyz” via JSmol, which contains information about the molecular geometry in Cartesian coordinates, as well as the normal frequencies of the vibrational modes. The default JSmol buttons to control the vibrational mode animations are disabled when the “.xyz” is not available yet in LIDA. In Sect. 3.5, we provide further details about the calculation of the vibrational modes used in the database. This viewer only shows one molecule per ice analog. This means that for an ice mixture such as H2O:CH3CH2OH, only the H2O molecule is immediately displayed in the viewer. To allow the user to visualize other molecules (e.g., CH3CH2OH), the 3D Molecule Viewer is linked to PubChem21 (Bolton et al. 2011; Kim et al. 2020), there is a comprehensive database of freely accessible chemical information maintained by the National Center for Biotechnology Information (NCBI). Searching for a molecule is as simple as typing: ethanol to visualize the 3D shape of CH3CH2OH. The colon symbol “:” provides the key to connect with the PubChem database. These databases contain detailed information on several molecules, which can help the user to understand different aspects of the molecular properties. Figure 5 shows an example of the 3D molecule viewer, which displays a screenshot of the bending mode animation of the H2O molecule.

3.5 Vibrational mode calculation

The vibrational modes of the molecules in LIDA are calculated with the ORCA22 software (Neese 2012, 2018; Neese et al. 2020), which contains a wide variety of quantum chemistry methods for different purposes. In the 2022 release of LIDA, the aim of the calculation of the vibrational modes is to show the animation of the vibrational modes, and, therefore, the focus is not on the accuracy of the vibrational frequencies. These have to be taken from experimental values. For the calculations, it is assumed that a molecule is isolated, not in a matrix surrounded by other molecules, and it is in the electronic ground state. In addition, ORCA considers that all vibrations are strictly harmonic. The consequence of such approaches is that the wave numbers of some vibrational modes calculated with ORCA deviate from the wave numbers of the absorption bands observed in experimental IR spectra or may even be completely absent. The numerical error in the calculation of vibrational frequencies with ORCA may be as large as 50 cm−1, although it is considerably lower in most of the cases. Nonetheless, vibrational mode assignments are correct and can be used as a tool to visualize the animation of the molecular motions.

For the molecule geometry optimization and calculation of the vibrational modes, we adopt the density functional theory (DFT) with the functional B3LYP, which stands for Becke, 3-parameter, Lee-Yang-Parr” (Becke 1993; Stephens et al. 1994). The input geometry of the molecules is taken from the Pub-Chem database. The vibrational frequencies calculated for the molecules in the database can be visualized in the 3D molecule viewer described in Sect. 3.4. Additionally, the modes with calculated frequencies are indicated in green in the annotations of the spectrum visualization. Rotational transitions are not available in these files because they are quenched in the ice environment.

4 Online tools and applications

In this section, we introduce two new online tools focused on the creation of synthetic spectra using the laboratory data from the database and the derivation of the CRI at IR wavelengths of ice samples. These tools also have an intuitive graphical user interface that makes it easier to use and download the output results. The details are given in the subsections below.

4.1 SPECFY

SPECFY is an online tool available through LIDA to construct synthetic spectra of protostars containing ice absorption bands. This tool uses Python Flask to render the web page and JavaScript to show the absorbance spectra in a drop-down menu to be used by SPECFY. The web interface of SPECFY is shown in Appendix C.1. The next subsections describe the tool and show practical applications of how to use SPECFY to interpret astronomical observations.

4.1.1 Synthetic spectra

To construct a synthetic spectrum with multiple ice features, SPECFY performs a linear combination of experimental data in LIDA that is available via a drop-down menu on the web interface. The linear combination is given by (11)

where wi is the weighting factor used to increase or decrease the intensity of the ice bands, and is calculated with Eq. (3). The weighting factor wi is calculated by the following equation: (12)

where is the input ice column density provided by the user in LIDA, and is the ice column density of the sample itself, which is calculated with Eq. (4). For example, if the user requires a column density of 1018 cm−2 and the experimental spectrum has a column density of 1017 cm−2, the selected spectrum will be multiplied by a factor of 10 in Eq. (11). It is worth noting that all experimental data are interpolated during the linear combination to ensure consistency of the method and avoid spectral range variations of the input data.

Besides the ice spectra hosted in LIDA, the template amorphous silicate spectrum of the galactic center source GCS 3 taken from Kemper et al. (2004) is also available to be combined with the ices. This spectrum was observed with ISO towards the Galactic center and has been used as a template to remove the silicate features observed toward protostars in previous works (e.g., Boogert et al. 2008; Bottinelli et al. 2010). In LIDA, this silicate spectrum is important for synthetic spectrum calculations because it makes it possible to check the effects of the Si–O bands when blended with ice absorption features. However, we stress that no mixing rule, such as the theories of Maxwell Garnett (Garnett 1904, 1906) and Bruggeman (Bruggeman 1935, 1936), is assumed in this procedure. In practice, SPECFY assumes isolated materials. Additionally, this tool does not include secondary effects of grain size and geometry or scattering processes that might affect the shape of the ice bands. Those features will be included in future work dedicated to improving SPECFY.

The combined ice spectrum can be used to match observational data. As an example, we created a synthetic spectrum using the parameters described in Table 3. The results are shown in Fig. 6 and the outputs in the web interface of SPECFY are displayed in Fig. C.2. The LIDA model in optical depth scale is constructed with SPECFY by combining ice and silicate spectra with different input column densities. The ice components in this combination are composed of pure H2O at 15 K and the mixtures H2O:CO2 (10:1) and CO:CO2 (2:1). These three ice samples comprise the most abundant ice molecules observed toward protostars (Öberg et al. 2011; Boogert et al. 2015). Superposed in relation to the LIDA model, we display the spectrum of the protostar AFGL 989, observed with ISO (Gibb et al. 2004). The good agreement between the model and the strong bands in observations show that SPECFY is a useful tool for modeling astronomical data. This solution is not necessarily unique to the AFGL 989 spectrum, but this methodology provides the means to help in the quantification of the ice column densities as well as the interpretation of astronomical observations.

The H2O:CO2 ice mixture dominates the absorption profile of the band at 3 µm, but it cannot fully explain the absorption excess of the spectral red wing region of AFGL 989. The nature of this strong absorption profile is under debate, but it is often attributed to scattering due to large grains (e.g., Boogert et al. 2000) and ammonia hydrates (H2O:NH3; e.g., Merrill et al. 1976; Dartois et al. 2002). The water ice bending and libration modes are also observed around 6 µm and 13.6 µm. Likewise, the CO2 bands at 4.27 µm and around 15 µm are not entirely modeled by the carbon dioxide fraction in the H2O:CO2 mixture. Additional CO2 is added by the CO:CO2 ice mixture. A fraction of carbon monoxide is expected to coexist in the ice matrix of carbon dioxide, as indicated in astronomical observations (Pontoppidan et al. 2008; Poteet et al. 2013). Although this combination matches the two CO2 bands relatively well, it results in a higher CO ice peak at 4.67 µm. Finally, the absorption profile of the silicate is relatively well reproduced with the amorphous silicate of GCS 3. Similarly to the unclear origin of the absorption excess around 3.3 µm, other strong absorptions are observed at 6 µm, which are usually associated with organic refractory material (Gibb & Whittet 2002; Boogert et al. 2008), and at 6.85 µm, often attributed to CH3OH (Bottinelli et al. 2010, e.g.,) and (e.g., Keane et al. 2001; Schutte & Khanna 2003; Maté et al. 2009, 2012). This exercise shows that the resources available in LIDA can be used to analyse the spectra of protostars and obtain ice column densities.

Next, the optical depth spectrum can be converted to a flux scale in Jy units by adopting the continuum SEDs of different protostars. We compiled and added the continuum SED of seven protostars as calculated by Gibb et al. (2004) and Boogert et al. (2008) to LIDA; these are listed in Table 4. The sources are representative of Class I and Class II objects and have spectral data obtained with ground- and space-based telescopes. Except in the cases of Elias 29 and AFGL 989, which were observed with the ISO/short-wavelength spectrometer (SWS) in the entire range between 2 and 30 µm, all sources have coverages of 2.5–5 µm (except 4.0–4.4 µm) and 5–30 µm. The former interval is based on the VLT/ISAAC observations summarized in Pontoppidan et al. (2003) and van Broekhuizen et al. (2005) or Keck NIRSPEC (McLean et al. 1998) observations. The latter range is constrained by space-based observations with the Infrared Spectrograph (IRS) of the Spitzer Space Telescope. Despite the careful SED determination by Gibb et al. (2004) and Boogert et al. (2008), inaccuracies may still occur, and this must be taken into account when using these data. Once the continuum SED is known, it can be used to convert the ice’s experimental spectra from optical depth to a flux scale. The conversion to the synthetic spectrum in flux scale is performed as follows: (13)

where is the continuum SED of the protostar.

Figure 7 shows three synthetic spectra using the continuum templates from AFGL 989, Elias 29, and DG Tau B, which represent three protostar categories, a high-mass protostar, a low-mass protostar, and a protoplanetary disk, respectively. The continuum applied to the optical depth model is displayed in Fig. 6 and the output in the web interface is displayed in Fig. C.2. The effect of the continuum in this example is characterized by different flux intensities and by changing the slope of the protostar SED. Additionally, Fig. 7 shows the sensitivity limits for the filters G235M and G395M of the JWST/Near-Infrared Spectrometer integral field unit (NIRSpec/IFU) and all filters of the Mid-Infrared Instrument at Medium Resolution Spectroscopy (MIRI/MRS). These values represent the minimum detectable signal corresponding to a signal-to-noise ratio of 10 obtained with an on-source integration time of 10 000 seconds (Glasse et al. 2015; Pontoppidan et al. 2016). This comparison shows that ices can be easily detected with the JWST toward sources with continuum SEDs similar to AFGL 989, Elias 29, and DG Tau B. With this feature in LIDA, one can generate input spectra for the JWST Time Exposure Calculator23 (ETC) that can be used in future proposals cycles.

Table 3

Continuum SEDs available in the SPECFY tool compiled from Gibb et al. (2004) and Boogert et al. (2008).

Table 4

Selected ice spectra and continuum model to construct a synthetic protostar spectrum. For an example, see Fig. 6.

thumbnail Fig. 6

AFGL 989 vs. LIDA model. LIDA model using the SPECFY online tool (orange) superposed to the ISO spectrum of the protostar AFGL 989 (black) in the optical depth scale taken from Gibb et al. (2004). The synthetic spectrum in the optical depth scale is composed of the linear combination of three ice spectra (pure H2O, H2O:CO2 (10:1), and CO:CO2 (2:1)) and silicate template from the GCS 3 source. The dominant ice spectrum is H2O:CO2 (10:1), shown by the gray dashed line. The assignments of a few bands are indicated. The red hatched area highlights an infrared absorption excess attributed to ammonia hydrates and large grains (see text).
thumbnail Fig. 7

Output spectra (solid lines) from SPECFY in Jy showing the effect of selecting different continuum SED templates (dashed lines). The sensitivities of JWST/NIRSpec medium resolution and MIRI for 10 000 s integration time are shown for comparison by the gray dot-dashed lines.

4.1.2 Functional groups in protostellar spectra

LIDA also has the capability of searching for molecules containing similar associations of atoms and some functional groups, as described in Sect. 3.2. Once these are chosen, one can select them from the drop-down menu in SPECFY to construct a model spectrum for comparison with the observations. A practical example is given in Fig. 8. We use two separate entries (CO and CH) in the “spectral data” field of LIDA to search for molecules sharing carbon-oxygen bonds (e.g., carbonyl-bearing molecules, alcohols) and carbon-hydrogen bonds as shown in the top panel of Fig. 8. From the CO entry, several ice analogs are found, including HCOOH, CH3OH, CH3CHO, and CH3COCH3. Similarly, the CH entry returns the same molecules because they contain CO and CH chemical bonds. In addition, LIDA also finds CH4 based on the query request.

The vibrational modes of functional groups containing a carbonyl group, C–O and C–H bonds have been assigned to the spectra of protostars (e.g., Gibb et al. 2004; Boogert et al. 2015). To illustrate how LIDA can further support astronomical data interpretation, in the middle panel of Fig. 8, we show the experimental spectra of HCOOH, CH3CHO, CH3OH, CH3COCH3, and CH4 scaled to the spectra of the low-mass protostar HH46 (Boogert et al. 2008). The HH46 spectrum is subtracted of the water ice and silicate. The chemical bonds associated with the absorption bands are indicated in the green and blue shaded areas. The bottom panel of Fig. 8 highlights the chemical bonds of the molecules contributing to the absorption bands toward HH46. The parameters used to scale laboratory data to the observations are given in Table 5. This exercise shows that LIDA can be used to identify the chemical bonds related to different absorption bands and provide upper-limit column densities for ices. Figure 8 also highlights the blending of bands at different spectral regions. For example, the C=O stretching modes of HCOOH, CH3CHO, and CH3COCH3 lie almost at the same wavelength, which hints at the need for high-sensitivity and spectral-resolution observational data that will be provided by the JWST. Clearly, this is an important tool to explore the contribution of different functional groups and chemical bonds to the overall absorption profile of features observed in interstellar ice spectra. It should be noted that such synthetic spectra allow the reproduction of observed data but do not necessarily provide a unique solution. Other public codes, such as the ENIIGMA fitting tool (Rocha et al. 2021) have the goal of quantifying the degeneracy of those fits when a large dataset of inputs is taken into account.

Table 5

Ice spectra selected from LIDA entries CO and CH and their column densities after manually scaling to HH46 spectrum shown in Fig. 8.

thumbnail Fig. 8

Illustration on how to use LIDA to interpret astronomical observations. Top: LIDA entries to search for molecules sharing CO and CH chemical bonds. Middle: selected experimental data scaled to the water-silicate-subtracted spectra of the protostar HH46. Bottom: molecules representing the pure ices used to match the HH46 spectrum. The ellipses indicate which part of a molecule is responsible for a specific absorption band, and the colors follow the same color-code used in the middle panel.

4.2 Infrared refractive index calculator

In this section, we introduce the refractive index online calculator, which is publicly available through LIDA. The web interface of this tool is shown in Fig. C.3. This tool uses the approach adopted in Rocha & Pilling (2014) for the NKABS code and is briefly described below.

The goal of the tool is to calculate the real (n) and imaginary (k) parts of CRI () from the absorbance spectrum (Eq. (2)) of the ice sample as a function of the wave number (v, in units of cm−1). The input experimental data is the absorbance spectrum given in ascii format. Other input parameters are required before starting the calculation. They are the thickness of the ice sample (d) in µm, the refractive index of the sample around 670 nm (n0) or at the wavelength of the HeNe laser used in the experiments, the real refractive index of the substrate, and the mean average percentage error (MAPE) that is used as a stop criterion.

Equations (9) and (10) are solved interactively, and new values of k are used to calculate new values of n. Subsequently, new n improves k, until the convergence criteria is reached. The numerical implementation of these equations is described in Rocha & Pilling (2014) and follows the procedure presented in Ohta & Ishida (1988) to solve the Kramers-Kronig equation. As an example, we calculate the CRI values of pure H2O at 30, 75, 105, and 135 K. The H2O ice IR spectra used as input are taken from Öberg et al. (2007), namely, Pure H2O (3000 ML)24. Table 6 lists the n0 values, the number of iterations used by the tool, and the final MAPE.

The data from OASIS and from the theoretical calculation cover two spectral ranges, that is 0.25–0.7 µm and 2–20 µm, respectively. The interval between 0.7 and 2 µm is not available in the experimental spectrum. We deal with this missing data using different approaches to determine n and k. For k values, we extrapolate the imaginary refractive index at 2 µm (10−4) until 0.25 µm. In the case of n, we use a low-order polynomial to link the water ice n values from He et al. (2022) to the data starting at 2 µm. The caveat in this approach is that we do not take into account the water ice absorption bands in the interval between 0.7 and 2 µm. However, the absorption features between 1.4 and 1.8 µm for amorphous and crystalline water ice are very weak (Mastrapa et al. 2008). For example, the k values calculated by Mastrapa et al. (2008) range from 10−5 to 10−3, which is close to the value used in our extrapolation (10−4). Similarly, the variation in n is 0.2% between the lowest and highest values.

The results are visualized in the “refractive index viewer” shown in Fig. 9 for the data at 30 K. A download of the data files and plots is also available. A terminal-based version of this tool is available for download in the GitHub25 repository of LIDA. Both Linux and executable files for Windows platforms can be used for highly resolved spectral data that demand high computational efficiency.

Table 6

Input parameters used to calculate the mid-IR water ice CRI.

5 Future upgrades

LIDA already has relevant data for all molecules securely or tentatively detected toward protostars (e.g., Tables 1 and A.1) and covers the most abundant species for the JWST, but more data are necessary to further boost the interpretation of upcoming observations. Table 7 lists the molecules that are missing in the current version of LIDA but are being measured or will be targeted in future experiments. Similarly, temperature- and wavelength-dependent refractive index values of CO, CO2, NH3, and CH3OH were recently measured on the OASIS setup and will be added to the database after data reduction (Rachid et al. in prep.). We also mention that LIDA is available to host data from other astrochemistry groups. Here, we hope that having one central point from which to search for ice properties will be considered helpful for the whole ice community.

The online tools will also be further developed to support astronomical data interpretation. With this goal in mind, the effect of grain shape will be available when simulating synthetic spectra of protostars. Additionally, the UV/vis n and k values of different ices and ice mixtures will be included in LIDA. Another forthcoming LIDA upgrade is the inclusion of diagnostic plots relating the peak position and full width at half maximum (FWHM) of ice features that can be compared with similar information from different astronomical observations.

thumbnail Fig. 9

Screenshot of refractive index viewer showing wavelength-dependent CRI of H2O ice at 30 K. The top and bottom panels show the real and imaginary refractive index values, respectively.
Table 7

List of missing molecules in LIDA, which will be included via new measurements or data sharing from other laboratories.

6 Summary and outlook

The Leiden Ice Database has served the astronomical community for more than 20 yr by providing IR spectra of ice samples.

In 2015, all ice IR spectra were combined on one server and visualization tools were developed. In this paper, we present the most recent version of LIDA, which includes over 1100 IR spectra of ice samples in astrophysically relevant conditions, as well as the UV/vis and mid-IR refractive indices of H2O at different temperatures. In addition to the wide range of experimental data, the current upgrade includes astronomy-oriented online tools to help the interpretation of observations provided by the JWST, in general, as well as the past ice observations. Both data and tools are provided in a user-friendly format to boost the usability of the database. It is worth mentioning that LIDA is a specific deliverable within ICE AGE, an ERS JWST program.

The database is under expansion, and spectra of several COMs and refractive index values of other ices will become publicly available in the next months and years. It is also hoped that other laboratory groups will make their ice spectra available through LIDA. Also, the online tools in the database will be further developed to meet to the need to interpret ice observations in the upcoming years with the JWST, the METIS (Mid-Infrared Extremely Large Telescope Imager and Spectrograph) on the Extremely Large Telescope (ELT), and the SPHEREx (Spectro-Photometer for the History of the Universe, Epoch of Reionization and Ices Explorer). More information about LIDA can be found in the public-access online documentation26.

Acknowledgements

We thank the thoughtful comments of an anonymous referee on both manuscript and the LIDA website. WRMR thanks Leiden Observatory for financial support. We thank the many (under)graduates, postdocs and staff who have been contributing over many years to the data available in LIDA. We furthermore acknowledge the ICE AGE team whose JWST observing plans have been the trigger for updating the “old” Leiden Ice Database. We specifically mention Dr. Adwin Boogert for many useful discussions. LIDA is currently also at the base of interpreting data from JWST observations obtained within JOYS, a MIRI GTO protostar program. We are grateful for continuing support through NOVA, the Netherlands Research School for Astronomy, the NWO through its Dutch Astrochemistry Program (DANII), and the NWO VICI grant “Unlocking the chemistry of the heavens”. The present work is closely connected to ongoing research within INTERCAT, the Center for Interstellar Catalysis located in Aarhus, Denmark. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 291141 MOLDISK). We also acknowledge the technical support of the Computer group at Leiden Observatory, in particular, Dr. Erik Deul, Dr. Leonardo Lenoci and Dr. Bram Venemans.

Appendix A List of ice samples in LIDA

The current version of LIDA contains the IR spectrum of over 1100 ice samples, which are listed in Table A.1. They are categorized by ices in pure samples, and mixtures with, two, three, four, and five components. This also includes warmed-up samples or those processed by UV radiation.

Table A.1

Ice analogues hosted in LIDA. Irradiated samples are indicated by the symbol →. “s” and “h” indicate seconds and hour, respectively.

Appendix B Database design and back-end information

The structure of LIDA is built with Flask27 (Grinberg 2018), an open-source web framework written in Python. Flask is widely extensible in the sense that external software can be embedded in the web application. LIDA has two major interfaces that provide access to administrators and users, respectively. The user interface is described in Section 3.1. Here, we provide details of the administrator interface, which is obviously only accessible via an account and is thus restricted to collaborators and developers.

The administrator interface provides access to all information hosted in the database, as well as the capability to add and modify data. In this module, the database is structured in a relation design between analogs and spectra. Analogues are ice sample (e.g., Pure H2O), whereas a spectrum is the IR spectrum of the analog at a specific temperature (e.g., Pure H2O at 15 K). Table B.1 shows a scheme of the information contained in the database. All this information is also visible in the user interface, which is introduced in Section 3.1.

Table B.1

Example of relational database for pure H2O ice. All information is visible in the user interface.

In addition to the IR spectra of ice samples, the database hosts data of experimentally derived UV/vis refractive index values (optical constants) and calculated values for the mid-IR. The continuum SED of protostars is also hosted, but it is only accessible via the online tool SPECFY. The database files containing this information, are also structured in a relational design as used for analogs and spectra. The files containing the spectral data, optical constants, and continuum SED are stored on the server using a hierarchical data format (HDF5) that has been designed to store a large amount of data. The web interface allows the administrator to upload the data as a simple two-column file. The first column (X-axis) is the wave number (cm−1) for the absorbance and refractive index data, whereas wavelength (µm) is used for continuum SED data. Likewise, the second column (Y-axis) gives the physical quantities such as absorbance, refractive index, and flux in Jy, respectively. Python has a package supporting HDF5 called H5PY28, which is used to generate compressed files in LIDA to improve the efficiency of the database. Despite the files being stored in HDF5 format, they are available for download by the user as ASCII files (.txt extension). For security reasons, LIDA performs a check when uploading data that consists of validating the file extension, structure, and size. Absorbance data can be uploaded under the category of warm-up or irradiation time (exposition). Similarly, the refractive index is uploaded under the category of real or imaginary values. The continuum SED data is uploaded in the polynomial or blackbody categories.

The administrator module also contains the “access information tracker”, which makes it possible to track the number of accesses and downloads over the months and years. The goal of this feature is to check the impact of LIDA in providing the astronomical community with essential and accurate data to interpret telescope observations.

Appendix C Web interfaces of the online tools

The web interface of SPECFY is shown in Figure C.1. In “Step 1”, SPECFY sets the wavelength range to create the synthetic spectrum. This step is crucial because some absorbance spectra in the database have different ranges. By setting the range, all the absorbance spectra selected in step 2 are evenly interpolated to ensure that all spectral components have the same range. Next, in “Step 2”, the laboratory ice spectrum from LIDA can be selected to be converted to an optical depth scale and combined to another ice spectrum. This step can be repeated multiple times. Finally, in “Step 3”, the optical depth scale spectrum is converted to a spectrum in flux units based on the object and the continuum SED model adopted by the user. An example of the output files is shown in Figure C.2. The top panel shows the combined spectrum used to match the AFGL 989 ISO spectrum. The bottom panel displays the synthetic spectrum in flux scale that adopts the continuum SED of the Elias 29 protostar.

Figure C.3 shows the web interface of the refractive index calculator. This tool requires the upload of an external file containing the absorbance spectrum, which is done via the “Submit’ button’. Next, the user is asked to parse the values of three physical parameters (ice thickness, n670 nm, nsubs) and the stop criteria (MAPE). The calculations can be started by clicking on the blue button labeled “Start calculation”, and they roughly last 1–4 seconds for a spectrum with 18000 rows. The output data can be download by clicking on the green button labeled “Download the refractive index”. One of the outputs is called “lnk_optool”. This file contains the real and imaginary parts of the refractive index that are formatted to be used as input in the computational code optool29 (Dominik et al. 2021), a command-line tool written in Fortran, which is dedicated to deriving the opacity of ice and bare grains.

thumbnail Fig. C.1

Screenshot of web interface of SPECFY showing the three steps to create a synthetic protostar spectrum. The green buttons submit the information added to the white rectangles. In steps 2 and 3, the user can scroll and search for ice analogs and temperatures, and for continuum models, respectively. The blue button allows the user to download the continuum SED files.
thumbnail Fig. C.2

Screenshot of outputs of the SPECFY online tool. Top: Synthetic spectrum in optical depth scale composed by the linear combination of three ice spectra (Pure H2O, H2O:CO2, CO:CO2) and silicate template from GCS 3 source. Bottom: Synthetic spectrum in flux scale adopting the Elias 29 protostar continuum SED, taken from Boogert et al. (2008).
thumbnail Fig. C.3

Screenshot of web interface of the online tool for calculating the refractive index of ices. The user can upload the absorbance spectrum as input data and provide the ice parameters. The calculations are started by clicking on the blue button labeled “Start calculation”, and the files with the results are downloaded by clicking on the green button labeled “Download the refractive index”. The bottom yellow box informs that other formats of this tool are available for download as well.

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All Tables

Table 1

List of molecules with relevant data on LIDA and their solid-phase (tentative or non-) detection in the ISM.

In the text
Table 2

List of vibrational transitions and band strengths of the molecules in pure ices as included here and presented in the literature.

In the text
Table 3

Continuum SEDs available in the SPECFY tool compiled from Gibb et al. (2004) and Boogert et al. (2008).

In the text
Table 4

Selected ice spectra and continuum model to construct a synthetic protostar spectrum. For an example, see Fig. 6.

In the text
Table 5

Ice spectra selected from LIDA entries CO and CH and their column densities after manually scaling to HH46 spectrum shown in Fig. 8.

In the text
Table 6

Input parameters used to calculate the mid-IR water ice CRI.

In the text
Table 7

List of missing molecules in LIDA, which will be included via new measurements or data sharing from other laboratories.

In the text
Table A.1

Ice analogues hosted in LIDA. Irradiated samples are indicated by the symbol →. “s” and “h” indicate seconds and hour, respectively.

In the text
Table B.1

Example of relational database for pure H2O ice. All information is visible in the user interface.

In the text

All Figures

thumbnail Fig. 1

Overview of experimental techniques used to obtain data hosted in LIDA. The left panel shows two infrared spectroscopic techniques used to record the spectrum of ices; namely, transmission mode (a) and reflection-absorption mode (b). The right panel illustrates the technique used to measure UV/vis refractive index values. Cartoon c provides details of the diffraction and reflection phenomena that generate an interference pattern during ice growth. Cartoon d shows the incidence and reflection of the monochromatic and broadband light beams when interacting with the ice.
In the text
thumbnail Fig. 2

Pie chart displaying the percentage division of ice analogs in LIDA from pure samples to five-component mixtures.
In the text
thumbnail Fig. 3

Screenshot of spectrum visualization window showing IR spectrum of H2O ice at different temperatures given by the color-code. The annotations of the water vibrational modes are shown in green. They can be hidden by clicking on the yellow toggle below the plot. It also describes the annotation color-code; green means the vibrational mode is calculated, and black indicates the vibrational mode is not calculated. The hover set at the position around 3000 cm−1 displays the information of the spectral data point, such as the wave number in cm−1 (bottom X-axis), wavelength in µm (top X-axis), and absorbance (Y-axis). The toolbar is placed on the right side of the plot.
In the text
thumbnail Fig. 4

Screenshot showing UV/vis and mid-IR refractive index of pure H2O at different temperatures. The color is associated with the temperature of the ice. The hover shows the wave number in cm−1 and refractive index values as indicated in this figure.
In the text
thumbnail Fig. 5

Screenshot of three-dimensional (3D) molecule viewer embedded in LIDA using the JSmol package. The viewer is connected to external public databases of molecule structures and can be viewed using the searching bar below the molecules. A few dedicated controls can be used to move and animate the molecule. The calculated vibrational modes can be listed and animated using the green button. As proof of concept, the yellow arrows indicate the vibrational motion of the water bending mode seen at 1643 cm−1 in Fig. 3. Buttons for making a movie and downloading the image file in PNG format are also provided.
In the text
thumbnail Fig. 6

AFGL 989 vs. LIDA model. LIDA model using the SPECFY online tool (orange) superposed to the ISO spectrum of the protostar AFGL 989 (black) in the optical depth scale taken from Gibb et al. (2004). The synthetic spectrum in the optical depth scale is composed of the linear combination of three ice spectra (pure H2O, H2O:CO2 (10:1), and CO:CO2 (2:1)) and silicate template from the GCS 3 source. The dominant ice spectrum is H2O:CO2 (10:1), shown by the gray dashed line. The assignments of a few bands are indicated. The red hatched area highlights an infrared absorption excess attributed to ammonia hydrates and large grains (see text).
In the text
thumbnail Fig. 7

Output spectra (solid lines) from SPECFY in Jy showing the effect of selecting different continuum SED templates (dashed lines). The sensitivities of JWST/NIRSpec medium resolution and MIRI for 10 000 s integration time are shown for comparison by the gray dot-dashed lines.
In the text
thumbnail Fig. 8

Illustration on how to use LIDA to interpret astronomical observations. Top: LIDA entries to search for molecules sharing CO and CH chemical bonds. Middle: selected experimental data scaled to the water-silicate-subtracted spectra of the protostar HH46. Bottom: molecules representing the pure ices used to match the HH46 spectrum. The ellipses indicate which part of a molecule is responsible for a specific absorption band, and the colors follow the same color-code used in the middle panel.
In the text
thumbnail Fig. 9

Screenshot of refractive index viewer showing wavelength-dependent CRI of H2O ice at 30 K. The top and bottom panels show the real and imaginary refractive index values, respectively.
In the text
thumbnail Fig. C.1

Screenshot of web interface of SPECFY showing the three steps to create a synthetic protostar spectrum. The green buttons submit the information added to the white rectangles. In steps 2 and 3, the user can scroll and search for ice analogs and temperatures, and for continuum models, respectively. The blue button allows the user to download the continuum SED files.
In the text
thumbnail Fig. C.2

Screenshot of outputs of the SPECFY online tool. Top: Synthetic spectrum in optical depth scale composed by the linear combination of three ice spectra (Pure H2O, H2O:CO2, CO:CO2) and silicate template from GCS 3 source. Bottom: Synthetic spectrum in flux scale adopting the Elias 29 protostar continuum SED, taken from Boogert et al. (2008).
In the text
thumbnail Fig. C.3

Screenshot of web interface of the online tool for calculating the refractive index of ices. The user can upload the absorbance spectrum as input data and provide the ice parameters. The calculations are started by clicking on the blue button labeled “Start calculation”, and the files with the results are downloaded by clicking on the green button labeled “Download the refractive index”. The bottom yellow box informs that other formats of this tool are available for download as well.
In the text

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