Synthetic Spectral Libraries Research Articles

The updated BOSZ synthetic stellar spectral library

Context. The modeling of stellar spectra of flux standards observed by the Hubble and James Webb space telescopes requires a large synthetic spectral library that covers a wide atmospheric parameter range. Aims. The aim of this paper is to present and describe the calculation methods behind the updated version of the BOSZ synthetic spectral database, which was originally designed to fit the CALSPEC flux standards. These new local thermodynamic equilibrium (LTE) models incorporate both MARCS and ATLAS9 model atmospheres, updated continuous opacities, and 23 new molecular line lists. Methods. The new grid was calculated with Synspec using the LTE approximation and covers metallicities [M/H] from −2.5 to 0.75 dex, [α/M] from −0.25 to 0.5 dex, and [C/M] from −0.75 to 0.5 dex, providing spectra for 336 unique compositions. Calculations for stars between 2800 and 8000 K use MARCS model atmospheres, and ATLAS9 is used between 7500 and 16 000 K. Results. The new BOSZ grid includes 628 620 synthetic spectra from 50 nm to 32 µm with models for 495 Teff−log 𝑔 parameter pairs per composition and per microturbulent velocity. Each spectrum has eight different resolutions spanning a range from R = 500 to 50 000 as well as the original resolution of the synthesis. The microturbulent velocities are 0, 1, 2, and 4 km s−1. Conclusions. The new BOSZ grid extends the temperature range to cooler temperatures compared to the original grid because the updated molecular line lists make modeling possible for cooler stars. A publicly available and consistently calculated database of model spectra is important for many astrophysical analyses, for example spectroscopic surveys and the determination of stellar elemental compositions.

Using in silico tools to predict flame retardant metabolites for more informative exposomics-based approaches.

Introduction: The positive identification of xenobiotics and their metabolites in human biosamples is an integral aspect of exposomics research, yet challenges in compound annotation and identification continue to limit the feasibility of comprehensive identification of total chemical exposure. Nonetheless, the adoption of in silico tools such as metabolite prediction software, QSAR-ready structural conversion workflows, and molecular standards databases can aid in identifying novel compounds in untargeted mass spectral investigations, permitting the assessment of a more expansive pool of compounds for human health hazard. This strategy is particularly applicable when it comes to flame retardant chemicals. The population is ubiquitously exposed to flame retardants, and evidence implicates some of these compounds as developmental neurotoxicants, endocrine disruptors, reproductive toxicants, immunotoxicants, and carcinogens. However, many flame retardants are poorly characterized, have not been linked to a definitive mode of toxic action, and are known to share metabolic breakdown products which may themselves harbor toxicity. As U.S. regulatory bodies begin to pursue a subclass- based risk assessment of organohalogen flame retardants, little consideration has been paid to the role of potentially toxic metabolites, or to expanding the identification of parent flame retardants and their metabolic breakdown products in human biosamples to better inform the human health hazards imposed by these compounds. Methods: The purpose of this study is to utilize publicly available in silico tools to 1) characterize the structural and metabolic fates of proposed flame retardant classes, 2) predict first pass metabolites, 3) ascertain whether metabolic products segregate among parent flame retardant classification patterns, and 4) assess the existing coverage in of these compounds in mass spectral database. Results: We found that flame retardant classes as currently defined by the National Academies of Science, Engineering and Medicine (NASEM) are structurally diverse, with highly variable predicted pharmacokinetic properties and metabolic fates among member compounds. The vast majority of flame retardants (96%) and their predicted metabolites (99%) are not present in spectral databases, posing a challenge for identifying these compounds in human biosamples. However, we also demonstrate the utility of publicly available in silico methods in generating a fit for purpose synthetic spectral library for flame retardants and their metabolites that have yet to be identified in human biosamples. Discussion: In conclusion, exposomics studies making use of fit-for-purpose synthetic spectral databases will better resolve internal exposure and windows of vulnerability associated with complex exposures to flame retardant chemicals and perturbed neurodevelopmental, reproductive, and other associated apical human health impacts.

Regression-based surface water fraction mapping using a synthetic spectral library for monitoring small water bodies

ABSTRACT Small water bodies (SWBs), such as ponds and on-farm reservoirs, are a key part of the hydrological system and play important roles in diverse domains from agriculture to conservation. The monitoring of SWBs has been greatly facilitated by medium-spatial-resolution satellite images, but the monitoring accuracy is considerably affected by the mixed-pixel problem. Although various spectral unmixing methods have been applied to map sub-pixel surface water fractions for large water bodies, such as lakes and reservoirs, it is challenging to map SWBs that are small in size relative to the image pixel and have dissimilar spectral properties. In this study, a novel regression-based surface water fraction mapping method (RSWFM) using a random forest and a synthetic spectral library is proposed for mapping 10 m spatial resolution surface water fractions from Sentinel-2 imagery. The RSWFM inputs a few endmembers of water, vegetation, impervious surfaces, and soil to simulate a spectral library, and considers spectral variations in endmembers for different SWBs. Additionally, RSWFM applies noise-based data augmentation on pure endmembers to overcome the limitation often arising from the use of a small set of pure spectra in training the regression model. RSWFM was assessed in ten study sites and compared with the fully constrained least squares (FCLS) linear spectral mixture analysis, multiple endmember spectral mixture analysis (MESMA), and the nonlinear random forest (RF) regression without data-augmentation. The results showed that RSWFM decreases the water fraction mapping errors by ~ 30%, ~15%, and ~ 11% in root mean square error compared with the linear FCLS, MESMA unmixings, and the nonlinear RF regression without data-augmentation respectively. RSWFM has an accuracy of approximately 0.85 in R2 in estimating the area of SWBs smaller than 1 ha.

LASPM: the LAMOST stellar parameter pipeline for M-type stars and application to the sixth and seventh data release (DR6 and DR7)

The molecular-rich atmospheres of M type stars complicate our understanding to their atmospheric properties. Recently, great progress has been made in atmospheric modeling of M-type stars, and we take advantage of the updated BT-Settl model grid to develop a pipeline LASPM to measure atmospheric parameters (T eff, log g, [M/H]) of M-type stars from low-resolution spectra. The pipeline was applied to the sixth and seventh data release (DR6 & DR7) of Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), which released atmospheric parameters for 610 419 and 680 185 M-type spectra, respectively. The key algorithm is to find the best-matching for templates in the synthetic spectral library for an observed spectrum, and then minimizing χ 2 through a linear combination of five best-matching templates. The intrinsic precisions of the parameters were estimated by using the multiple epoch observations for the same stars, which are 118 K, 0.20 dex, 0.29 dex for T eff, log g, and [M/H] respectively. The T eff and log g are consistent with the spectral and luminosity classifications by LAMOST 1D pipeline, and the loci of giants and dwarfs both on spectral index and color-magnitude diagrams show the validity. The metallicities of LASPM are also checked with the selected members of four open clusters (NGC 2632, Melotte 22, ASCC_16, and ASCC_19), which are consistent without any bias. Comparing the results between LASPM and the APOGEE Stellar Parameter and Chemical Abundance Pipeline (ASPCAP), there is a scatter of 73 K, 0.22 dex, 0.21 dex for T eff, log g, and [M/H], respectively.

Assessing the performance of LTE and NLTE synthetic stellar spectra in a machine learning framework

ABSTRACT In the current era of stellar spectroscopic surveys, synthetic spectral libraries are the basis for the derivation of stellar parameters and chemical abundances. In this paper, we compare the stellar parameters determined using five popular synthetic spectral grids (INTRIGOSS, FERRE, AMBRE, PHOENIX, and MPIA/1DNLTE) with our convolutional neural network (CNN, StarNet). The stellar parameters are determined for six physical properties (effective temperature, surface gravity, metallicity, [α/Fe], radial velocity, and rotational velocity) given the spectral resolution, signal-to-noise ratio, and wavelength range of optical FLAMES-UVES spectra from the Gaia-ESO Survey. Both CNN modelling and epistemic uncertainties are incorporated through training an ensemble of networks. StarNet training was also adapted to mitigate differences between the synthetic grids and observed spectra by augmenting with realistic observational signatures (i.e. resolution matching, wavelength sampling, Gaussian noise, zeroing flux values, rotational and radial velocities, continuum removal, and masking telluric regions). Using the FLAMES-UVES spectra for FGK-type dwarfs and giants as a test set, we quantify the accuracy and precision of the stellar label predictions from StarNet. We find excellent results over a wide range of parameters when StarNet is trained on the MPIA/1DNLTE synthetic grid, and acceptable results over smaller parameter ranges when trained on the 1DLTE grids. These tests also show that our CNN pipeline is highly adaptable to multiple simulation grids.

Characterizing stellar parameters from high-resolution spectra of main sequence cool stars. I. The G2V–K2V stars

Abstract The goal of the present study is to construct, test, and validate a high-resolution synthetic spectral library using PHOENIX model atmospheres and develop a reliable tool to estimate stellar parameters from high-resolution optical and/or near-infrared spectra of M dwarfs. We report here the preliminary results of tests characterizing main sequence G–K stars from high-resolution spectra. We anchored the atomic line-list using the stellar standards Sun, ξ Boo A, and ϵ Eri to ensure the synthetic spectra computed with PHOENIX reproduce their observed counterparts. These stars were chosen because their parameters are very well characterized, and on which the absolute accuracy of our method depends on. We successfully estimated the stellar parameters with associated error bars for 17 stars. Using a pseudo Monte Carlo statistical analysis, we present overall improved uncertainties on the stellar parameters compared to those in the literature (on average 9 K, 0.014 dex, and 0.008 dex for the effective temperature, the surface gravity, and the metallicity, respectively). Our estimated stellar parameters are also in good agreement with values found in the literature.

Data-driven Spectroscopy of Cool Stars at High Spectral Resolution

Abstract The advent of large-scale spectroscopic surveys underscores the need to develop robust techniques for determining stellar properties (“labels,” i.e., physical parameters and elemental abundances). However, traditional spectroscopic methods that utilize stellar models struggle to reproduce cool (<4700 K) stellar atmospheres due to an abundance of unconstrained molecular transitions, making modeling via synthetic spectral libraries difficult. Because small, cool stars such as K and M dwarfs are both common and good targets for finding small, cool planets, establishing precise spectral modeling techniques for these stars is of high priority. To address this, we apply The Cannon, a data-driven method of determining stellar labels, to Keck High Resolution Echelle Spectrometer spectra of 141 cool (<5200 K) stars from the California Planet Search. Our implementation is capable of predicting labels for small (<1 R ⊙) stars of spectral types K and later with accuracies of 68 K in effective temperature (T eff), 5% in stellar radius (R *), and 0.08 dex in bulk metallicity ([Fe/H]), and maintains this performance at low spectral resolutions (R < 5000). As M dwarfs are the focus of many future planet-detection surveys, this work can aid efforts to better characterize the cool star population and uncover correlations between cool star abundances and planet occurrence for constraining planet formation theories.

Chemical Abundances of Main-sequence, Turnoff, Subgiant, and Red Giant Stars from APOGEE Spectra. II. Atomic Diffusion in M67 Stars

AbstractChemical abundances for 15 elements (C, N, O, Na, Mg, Al, Si, K, Ca, Ti, V, Cr, Mn, Fe, and Ni) are presented for 83 stellar members of the 4 Gyr old solar-metallicity open cluster M67. The sample contains stars spanning a wide range of evolutionary phases, from G dwarfs to red clump stars. The abundances were derived from near-IR (λ1.5–1.7μm) high-resolution spectra (R = 22,500) from the SDSS-IV/Apache Point Observatory Galactic Evolution Experiment (APOGEE) survey. A 1D local thermodynamic equilibrium abundance analysis was carried out using the APOGEE synthetic spectral libraries, via χ2minimization of the synthetic and observed spectra with the qASPCAP code. We found significant abundance differences (∼0.05–0.30 dex) between the M67 member stars as a function of the stellar mass (or position on the Hertzsprung–Russell diagram), where the abundance patterns exhibit a general depletion (in [X/H]) in stars at the main-sequence turnoff. The amount of the depletion is different for different elements. We find that atomic diffusion models provide, in general, good agreement with the abundance trends for most chemical species, supporting recent studies indicating that measurable atomic diffusion operates in M67 stars.

Gaia–ESO Survey: INTRIGOSS—A New Library of High-resolution Synthetic Spectra

Abstract We present a high-resolution synthetic spectral library, INTRIGOSS, designed for studying FGK stars. The library is based on atmosphere models computed with specified individual element abundances via ATLAS12 code. Normalized SPectra (NSPs) and surface Flux SPectra (FSP) in the wavelength range 4830–5400 Å were computed with the SPECTRUM code. INTRIGOSS uses the solar composition of Grevesse et al. and four [α/Fe] abundance ratios, and consists of 15,232 spectra. The synthetic spectra are computed with astrophysical gf-values derived by comparing synthetic predictions with a solar spectrum of very high signal-to-noise ratio and the UVES-U580 spectra of five cool giants. The validity of the NSPs is assessed by using the UVES-U580 spectra of 2212 stars observed in the framework of the Gaia–ESO (European Southern Observatory) survey and characterized by homogeneous and accurate atmospheric parameter values and by detailed chemical compositions. The greater accuracy of NSPs with respect to spectra from the synthetic spectral libraries AMBRE, GES_Grid, PHOENIX, C14, and B17 is demonstrated by evaluating the consistency of the predictions of the different libraries for stars in the UVES-U580 sample. The validity of the FSPs is checked by comparing their prediction with both the observed spectral energy distribution (SED) and spectral indices. The comparison of FSPs with SEDs derived from the libraries ELODIE, INDO–U.S., and MILES indicates that the former reproduce the observed flux distributions within a few per cent and without any systematic trend. The good agreement between observational and synthetic Lick/SDSS (Sloan Digital Sky Survey) indices shows that the predicted blanketing of FSPs well reproduces the observed one, thus confirming the reliability of INTRIGOSS FSPs.

A study on missing lines in the synthetic solar spectrum near the Ca triplet

Synthetic stellar spectra are extensively used for many different applications in astronomy, from stellar studies (such as in the determination of atmospheric parameters of observed stellar spectra), to extragalactic studies (e.g. as one of the main ingredients of stellar population models). One of the main ingredients of synthetic spectral libraries are the atomic and molecular line lists, which contain the data required to model all the absorption lines that should appear in these spectra. Although currently available line lists contain millions of lines, a relatively small fraction of these lines have accurate derived or measured transition parameters. As a consequence, many of these lines contain errors in the electronic transition parameters that can reach up to 200%. Furthermore, even for the Sun, our closest and most studied star, state-of-the-art synthetic spectra does not reproduce all the observed lines, indicating transitions that are missing in the line lists of the computed synthetic spectra. Given the importance and wide range of applications of these models, improvement of their quality is urgently necessary. In this work we catalogued missing lines in the atomic and molecular line lists used for the calculation of the synthetic spectra in the region of GAIA, comparing a solar model computed via a recent line list with a high quality solar atlas available in the literature. After that, we attempted the calibration of their atomic parameters with the code ALLiCE; the calibrated line parameters are publicly available for use.

CONSTRUCTING A FLEXIBLE LIKELIHOOD FUNCTION FOR SPECTROSCOPIC INFERENCE

We present a modular, extensible likelihood framework for spectroscopic inference based on synthetic model spectra. The subtraction of an imperfect model from a continuously sampled spectrum introduces covariance between adjacent datapoints (pixels) into the residual spectrum. For the high signal-to-noise data with large spectral range that is commonly employed in stellar astrophysics, that covariant structure can lead to dramatically underestimated parameter uncertainties (and, in some cases, biases). We construct a likelihood function that accounts for the structure of the covariance matrix, utilizing the machinery of Gaussian process kernels. This framework specifically address the common problem of mismatches in model spectral line strengths (with respect to data) due to intrinsic model imperfections (e.g., in the atomic/molecular databases or opacity prescriptions) by developing a novel local covariance kernel formalism that identifies and self-consistently downweights pathological spectral line "outliers." By fitting many spectra in a hierarchical manner, these local kernels provide a mechanism to learn about and build data-driven corrections to synthetic spectral libraries. An open-source software implementation of this approach is available at http://iancze.github.io/Starfish, including a sophisticated probabilistic scheme for spectral interpolation when using model libraries that are sparsely sampled in the stellar parameters. We demonstrate some salient features of the framework by fitting the high resolution $V$-band spectrum of WASP-14, an F5 dwarf with a transiting exoplanet, and the moderate resolution $K$-band spectrum of Gliese 51, an M5 field dwarf.

Minerals detection for hyperspectral images using adapted linear unmixing: LinMin

Minerals detection over large volume of spectra is the challenge addressed by current hyperspectral imaging spectrometer in Planetary Science. Instruments such OMEGA (Mars Express), CRISM (Mars Reconnaissance Orbiter), M3 (Chandrayaan-1), VIRTIS (Rosetta) and many more, have been producing very large datasets since one decade. We propose here a fast supervised detection algorithm called LinMin, in the framework of linear unmixing, with innovative arrangement in order to treat non-linear cases due to radiative transfer in both atmosphere and surface. We use reference laboratory and synthetic spectral library. Additional spectra are used in order to mimic the effect of Martian aerosols, grain size, and observation geometry discrepancies between reference and observed spectra. The proposed algorithm estimates the uncertainty on “mixing coefficient” from the uncertainty of observed spectra. Both numerical and observational tests validate the approach. Fast parallel implementation of the best algorithm (IPLS) on Graphics Processing Units (GPU) allows to significantly reduce the computation cost by a factor of ∼40.

Empirical near-infrared colors for low-mass stars and brown dwarfs in the Orion Nebula Cluster

Atmospheric and evolutionary models for low-mass stars rely on approximate assumptions on the physics of the stellar structure and the atmospheric radiative transfer. This leads to biased theoretical predictions on the photospheric Spectral Energy Distributions of Pre-Main Sequence (PMS) stars, and affects the derivation of stellar parameters from photometric data. Our goal is to correct the biases present in the theoretical predictions for the near-IR photometry of low-mass PMS stars. Using empirical intrinsic IR colors, we assess the accuracy of current synthetic spectral libraries and evolutionary models. We consider a sample of ~300 PMS stars in the Orion Nebula Cluster (age 1 Myr) with measured luminosities, temperatures and photospheric JHKs photometry. By analyzing the photospheric colors of our sample of stars, we find that the synthetic JHKs photometry provided by theoretical spectral templates for late spectral types (>K6) are accurate at the level of ~0.2 mag, while colors are accurate at ~0.1 mag. We tabulate the intrinsic photospheric colors, appropriate for the Orion Nebula Cluster, in the range K6-M8.5. They can be conveniently used as templates for the intrinsic colors of other young (age<5 Myr) stellar clusters. An empirical correction of the atmospheric templates can fix the discrepancies between expected and observed colors. Still, other biases in the evolutionary models prevent a more robust comparison between observations and theoretical absolute magnitudes. In particular, PMS evolutionary models seem to consistently underestimate the intrinsic near-infrared flux at the very late spectral types, and this may introduce spurious features in the low-mass end of the photometrically-determined Initial Mass Function of young clusters.

A spectroscopic survey of faint, high-Galactic-latitude red clump stars

Aims. The goal of our survey is to provide accurate and multi-epoch radial velocities, atmospheric parameters (Teff, log g and [M/H]), distances and space velocities of faint Red Clump stars. Methods. We recorded high signal-to-noise (S/N >= 200) spectra of Red Clump stars, over the 4750-5950 Ang range, at a resolving power 5500. The target stars are distributed over the great circle of the celestial equator. Radial velocities were obtained via cross-correlation against IAU radial velocity standards. Atmospheric parameters were derived via chi^2 fit to a synthetic spectral library. A large number of RC stars from other surveys were re-observed to check the consistency of our results. Results. A total of 245 Red Clump stars were observed (60 of them with a second epoch observation separated in time by about three months), and the results are presented in an output catalog. None of them is already present in other surveys of Red Clump stars. In addition to astrometric and photometric support data from external sources, the catalog provides radial velocities (accuracy sigma(RV)=1.3 km/s), atmospheric parameters (sigma(Teff)=88 K, sigma(log g)=0.38 dex and sigma([M/H])=0.17 dex), spectro-photometric distances, (X,Y,Z) galacto-centric positions and (U,V,W) space velocities.

Spectral response of benthic diatoms with different sediment backgrounds

Remote sensing of terrestrial vegetation uses a wide range of vegetation indices (VIs) to monitor plant characteristics, but these indices can be very sensitive to canopy background reflectance. This study investigated background influences on VIs applied to intertidal microphytobenthos, using a synthetic spectral library constituted by a spectral combination of three contrasting types of sediment (sand, fine sand, and mud) and reflectance spectra of benthic diatom monospecific cultures obtained in controlled conditions. The spectral database exhibited, for the same biomass range (3–182 mg chlorophyll a m − 2 ), marked differences in albedo and spectral contrast linked to sediment variability in water content, grain size, and organic matter content. Several VIs were evaluated, from ratios using visible and near infrared wavelengths, to hyperspectral indices (derivative analysis, continuum removal). Among the ratios, the Normalized Difference Vegetation Index (NDVI) appeared less sensitive to background effects than VIs with soil corrections such as the Perpendicular Vegetation Index (PVI), the Soil-Adjusted Vegetation Index (SAVI), the Modified second Soil-Adjusted Vegetation Index (MSAVI2) or the Transformed Soil-Adjusted Vegetation Index (TSAVI). The lower efficacy of soil-corrected VIs may be explained by the structural differences and optical behavior of soil vs. canopies compared to sediment vs. microphytobenthos biofilms. The background effects were minimized using Modified Gaussian Model indices at 632 nm and 675 nm, and the second derivative at 632 nm, while poor results were obtained with the red-edge inflection point (REIP) and the second derivative at 675 nm. The least sensitive index was the Phytobenthos Index which is very similar to the NDVI, but uses a red wavelength at 632 nm instead of 675 nm, to account for the absorption by chlorophyll c. The modified NDVI 705, where the 705 nm wavelength replaces the red band, showed moderate background sensitivity. Moreover, the NDVI 705 and the Phytobenthos Index have the additional relevant property of being less sensitive to the index saturation response with increasing biomass. Unfortunately, these VIs cannot be applied to broad-band multispectral satellite images, and require sensors with a hyperspectral resolution. Nevertheless, this study showed that the background influence was not a limitation to applying the ubiquitous NDVI to map intertidal microphytobenthos using multispectral satellite images.

Observed Stellar Spectra As Templates For Gaia. I. The Asiago Red Clump Spectroscopic Survey At 1.22 Meter Telescope

We aim to derive accurate, multi-epoch radial velocities and atmospheric parameters (Teff, log g and [M/H]) of a large sample of Red Clump stars. We acquired data of the target stars at high signal-to-noise (S/N) and medium resolution with the Asiago B&C spectrograph. Radial velocities were obtained by applying cross-correlation against observed IAU standard stars and atmospheric parameters via χ2 fit to a synthetic spectral library. Extensive tests were carried out with the multi-epoch observations of the programme stars and re-observations of the Red Clump stars that are taken from different catalogues in the literature. A total of 305 Red Clump stars (60 of them with a second epoch observation) were observed, and presented in an output catalogue that contains accurate radial velocities (σ(RV) = 2.6 km s-1), atmospheric parameters (σ(Teff) = 68 K, σ(log g) = 0.38 dex and σ([M/H]) = 0.17 dex), distances derived from spectrophotometry, space velocities (U,V,W) with additional astrometric and photometric data taken from literature. We aim to use our final catalogue to study the substructure and kinematics of the Galaxy.

Geological mapping strategy using visible near‐infrared–shortwave infrared hyperspectral remote sensing: Application to the Oman ophiolite (Sumail Massif)

An airborne hyperspectral survey of the Oman ophiolite (Sumail Massif) has been conducted using the HyMap airborne imaging spectrometer with associated field measurements (GER 3700). An ASD FieldSpec3 spectrometer was also used in order to constrain the spectral signatures of the principal lithologies cropping out in the surveyed area. Our objective was to identify and map the various igneous lithologies by a direct comparison at high spectral resolution between field and airborne spectra despite strong variations in outcropping conditions such as (1) lighting, (2) surface roughness geometry, (3) blocks coated with red/brown patina and exfoliation products, or (4) deep hydrothermal weathering. On the basis of spectral signatures, we are able to distinguish three end‐members of olivine‐orthopyroxene bearing assemblages in the mantle sequence: (1) harzburgites, (2) dunites, and (3) a harzburgite with interstitial carbonate. Because plagioclase is spectrally featureless in the wavelength range studied it cannot be detected. In the crustal sequence, we therefore identified four end‐members with variable abundance of clinopyroxene: (1) massive gabbros, (2) amphibolized (upper) gabbros associated with intrusive dykes, (3) wehrlite with high serpentine content, and (4) gabbronorite (a lithology not previously recognized in the studied area). With the exception of wehrlite, spectra of olivine‐rich end‐members display characteristic Mg‐OH narrow absorption features caused by their high serpentine content. We take advantage of this observation to split the data into two subsets, corresponding to the mantle and crustal sequences, respectively. Pixels of an image often correspond to heterogeneous areas in the field and a direct comparison between airborne and in situ spectra is not straightforward. However, comparing spectra of pixels associated with the most homogeneous areas in the field with the spectra acquired in situ at the same location, we found a systematic change both in mean intensity and overall spectral shape. Dividing each spectrum by its low‐pass trend removes the effects caused by surface light scattering associated with each scale of analysis and results in an exceptional match between field and airborne spectra. However, the albedo information is lost and as a consequence, rock types only characterized by albedo change cannot be discriminated. A spectrum of a mixture of powdered minerals is usually seen as a linear combination of mineral spectra proportional to their abundance. However, this is no longer the case when minerals occur in complex arrangements in rock types. We thus develop a synthetic spectral library of all possible combinations of rock types covering the surface area of a pixel and use a simple distance calculation to identify the best match between each pixel and modeled spectra. This procedure allows the determination of the fractional cover of each rock type in a given pixel and to establish maps for each spectral end‐member. The final product is a geological map, derived from the combination of end‐member fractional cover maps, and is broadly consistent with the existing geological maps. Beyond this general agreement which demonstrates the potential of this new approach for geological mapping, imaging spectrometry allows (1) to map in detail the outline of the Moho north of Maqsad and (2) to identify a new crustal sequence enriched in silica south of Muqzah, revealing the presence of orthopyroxene, the nature and distribution of which are of relevance to the petrological and tectonic understanding of the Oman ophiolite evolution.

Gaia and its spectrometric challenge

The Gaia program was kicked off at the beginning of 2006 and is now reaching a phase of construction and integration for a launch at the end of 2011, to reach the L2 point several months later. Apart from that industrial activity, the European astronomical community is actively preparing the data treatment and in particular how to extract the astrophysical parameter outputs from the use of the spectrographs on board the satellite. This work consists of the realization of algorithms to extract fundamental parameters of the detected source points with the help of an extensive grid of synthetic spectral libraries. The number of these estimated fundamental parameters and their accuracy depend on the ability of the algorithms to treat the spectra and to compare them with the computed ones, and also on the quality of the synthetic spectra themselves. Then, on the one hand a huge effort is underway to produce these extensive grids of one-dimensional (1D) local thermodynamic equilibrium (LTE) spectra of stars, galaxies, quasi-stellar objects and asteroids, and on the other hand, new physics is tested in the atmosphere model of stars to improve the theoretical libraries. An example of the questions we need to answer is: do we need 3D non-local thermodynamic equilibrium (NLTE) modelling? If yes, the libraries will then be improved by adding corrections checked on dedicated standard stars. Shall we be able to implement this new physics in a reasonable time? This is another challenge.