SDSS-IV MaStar: Data-driven Parameter Derivation for the MaStar Stellar Library

The Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) Stellar Library (MaStar) is a large collection of high-quality empirical stellar spectra designed to cover all spectral types and ideal for use in the stellar population analysis of galaxies observed in the MaNGA survey. The library contains 59,266 spectra of 24,130 unique stars with spectral resolution R ∼ 1800 and covering a wavelength range of 3622–10,354 Å. In this work, we derive five physical parameters for each spectrum in the library: effective temperature (T eff), surface gravity ( log g ), metallicity ([Fe/H]), microturbulent velocity ( log ( v micro ) ), and alpha-element abundance ([α/Fe]). These parameters are derived with a flexible data-driven algorithm that uses a neural network model. We train a neural network using the subset of 1675 MaStar targets that have also been observed in the Apache Point Observatory Galactic Evolution Experiment (APOGEE), adopting the independently-derived APOGEE Stellar Parameter and Chemical Abundance Pipeline parameters for this reference set. For the regions of parameter space not well represented by the APOGEE training set (7000 ≤ T ≤ 30,000 K), we supplement with theoretical model spectra. We present our derived parameters along with an analysis of the uncertainties and comparisons to other analyses from the literature.

Automated Stellar Spectra Classification with Ensemble Convolutional Neural Network

Large sky survey telescopes have produced a tremendous amount of astronomical data, including spectra. Machine learning methods must be employed to automatically process the spectral data obtained by these telescopes. Classification of stellar spectra by applying deep learning is an important research direction for the automatic classification of high-dimensional celestial spectra. In this paper, a robust ensemble convolutional neural network (ECNN) was designed and applied to improve the classification accuracy of massive stellar spectra from the Sloan digital sky survey. We designed six classifiers which consist six different convolutional neural networks (CNN), respectively, to recognize the spectra in DR16. Then, according the cross-entropy testing error of the spectra at different signal-to-noise ratios, we integrate the results of different classifiers in an ensemble learning way to improve the effect of classification. The experimental result proved that our one-dimensional ECNN strategy could achieve 95.0% accuracy in the classification task of the stellar spectra, a level of accuracy that exceeds that of the classical principal component analysis and support vector machine model.

The Observation and Analysis of Stellar Photospheres

This textbook describes the equipment, observational techniques, and analysis used in the investigation of stellar photospheres. Now in its fourth edition, the text has been thoroughly updated and revised to be more accessible to students. New figures have been added to illustrate key concepts, while diagrams have been redrawn and refreshed throughout. The book starts by developing the tools of analysis, and then demonstrates how they can be applied. Topics covered include radiation transfer, models of stellar photospheres, spectroscopic equipment, how to observe stellar spectra, and techniques for measuring stellar temperatures, radii, surface gravities, chemical composition, velocity fields, and rotation rates. Up-to-date results for real stars are included. Written for starting graduate students or advanced undergraduates, this textbook also includes a wealth of reference material useful to researchers. eBook formats include color imagery while print formats are greyscale only; a wide selection of the color images are available online.

Binary fraction of O and B-type stars from LAMOST data

Binary stars play an important role in the evolution of stellar populations . The intrinsic binary fraction (f bin) of O and B-type (OB) stars in LAMOST DR5 was investigated in this work. We employed a cross-correlation approach to estimate relative radial velocities for each of the stellar spectra. The algorithm described by Sana et al. (2013) was implemented and several simulations were made to assess the performance of the approach. The binary fraction of the OB stars is estimated through comparing the uni-distribution between observations and simulations with the Kolmogorov-Smirnov tests. Simulations show that it is reliable for stars most of whom have six, seven and eight repeated observations. The uncertainty of orbital parameters of binarity becomes larger when observational frequencies decrease. By adopting the fixed power exponents of π = −0.45 and κ = −1 for period and mass ratio distributions, respectively, we obtain that f bin = 0.4 − 0.06 + 0.05 for the samples with more than three observations. When we consider the full samples with at least two observations, the binary fraction turns out to be 0.37 − 0.03 + 0.03 . These two results are consistent with each other in 1σ.

Fuzzy Cluster Analysis: Application to Determining Metallicities for Very Metal-poor Stars

This work presents a first attempt to apply fuzzy cluster analysis (FCA) to analyzing stellar spectra. FCA is adopted to categorize line indices measured from LAMOST low-resolution spectra, and automatically remove the least metallicity-sensitive indices. The FCA-processed indices are then transferred to the artificial neural network (ANN) to derive metallicities for 147 very metal-poor (VMP) stars that have been analyzed by high-resolution spectroscopy. The FCA-ANN method could derive robust metallicities for VMP stars, with a precision of ∼0.2 dex compared with high-resolution analysis. The recommended FCA threshold value λ for this test is between 0.9965 and 0.9975. After reducing the dimension of the line indices through FCA, the derived metallicities are still robust, with no loss of accuracy, and the FCA-ANN method performs stably for different spectral quality from [Fe/H] ∼ −1.8 down to −3.5. Compared with traditional classification methods, FCA considers ambiguity in groupings and noncontinuity of data, and is thus more suitable for observational data analysis. Though this early test uses FCA to analyze low-resolution spectra, and feeds the input to the ANN method to derive metallicities, FCA should be able to, in the large data era, also analyze slitless spectroscopy and multiband photometry, and prepare the input for methods not limited to ANN, in the field of stellar physics for other studies, e.g., stellar classification, identification of peculiar objects. The literature-collected high-resolution sample can help improve pipelines to derive stellar metallicities, and systematic offsets in metallicities for VMP stars for three published LAMOST catalogs have been discussed.

Photochemistry of Venus-like Planets Orbiting K- and M-dwarf Stars

Compared to the diversity seen in exoplanets, Venus is a veritable astrophysical twin of the Earth; however, its global cloud layer truncates features in transmission spectroscopy, masking its non-Earth-like nature. Observational indicators that can distinguish an exo-Venus from an exo-Earth must therefore survive above the cloud layer. The above-cloud atmosphere is dominated by photochemistry, which depends on the spectrum of the host star and therefore changes between stellar systems. We explore the systematic changes in photochemistry above the clouds of Venus-like exoplanets orbiting K-dwarf or M-dwarf host stars, using a recently validated model of the full Venus atmosphere (0–115 km) and stellar spectra from the Measurements of the Ultraviolet Spectral Characteristics of Low-mass Exoplanetary Systems (MUSCLES) Treasury survey. SO2, OCS, and H2S are key gas species in Venus-like planets that are not present in Earth-like planets, and could therefore act as observational discriminants if their atmospheric abundances are high enough to be detected. We find that SO2, OCS, and H2S all survive above the cloud layer when irradiated by the coolest K dwarf and all seven M dwarfs, whereas these species are heavily photochemically depleted above the clouds of Venus. The production of sulfuric acid molecules that form the cloud layer decreases for decreasing stellar effective temperature. Less steady-state photochemical oxygen and ozone forms with decreasing stellar effective temperature, and the effect of chlorine-catalyzed reaction cycles diminish in favor of HO x and SO x catalyzed cycles. We conclude that trace sulfur gases will be prime observational indicators of Venus-like exoplanets around M-dwarf host stars, potentially capable of distinguishing an exo-Venus from an exo-Earth.

The relative calibration of radial velocity for LAMOST medium resolution stellar spectra

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) started a median-resolution spectroscopic (MRS, R ∼7500) survey since October 2018. The main scientific goals of MRS, including binary stars, pulsators and other variable stars, were launched with a time-domain spectroscopic survey. However, the systematic errors, including the bias induced from wavelength calibration and the systematic difference between different spectrographs, have to be carefully considered during radial velocity measurement. In this work, we provide a technique to correct the systematics in the wavelength calibration based on the relative radial velocity measurements from LAMOST MRS spectra. We show that, for the stars with multi-epoch spectra, the systematic bias which is induced from the exposures on different nights can be corrected well for LAMOST MRS in each spectrograph. In addition, the precision of radial velocity zero-point of multi-epoch time-domain observations reaches below 0.5 km s−1. As a by-product, we also give the constant star candidates**, which can be the secondary radial-velocity standard star candidates of LAMOST MRS time-domain surveys.

The impact of time-dependent stellar activity on the atmospheric chemistry and observability of exoplanets

<p class="p1">Observations of exoplanets used to characterize the chemistry and dynamics of atmospheres have developed considerably throughout the years. Nonetheless, it remains a difficult task to give a full and detailed description using solely observations. With future space missions such as JWST and ARIEL, both expected to be launched within this decade, it becomes even more crucial to be able to fully explain and predict the underlying chemistry and physics involved. In this research, we focus on modeling star-planet interactions by using synthetic flare spectra to predict chemical tracers for future missions. We make use of a chemical kinetics code that includes synthetic time-dependent stellar spectra and thermal atmospheric escape to simulate the atmospheres of known exoplanets. Using a radiative transfer model we then retrieve emission spectra. This ongoing study is focused on various known planetary systems of which the stellar spectrum has been obtained by the (mega-)MUSCLES collaboration. Preliminary results on these systems show that stellar flares and thermal escape can have a significant effect on the chemistry in atmospheres. </p>