Solar and Stellar Astrophysics (astro-ph.SR)

Abstract: In this work, we present Stellar Spectra Factory (SSF), a tool to generate empirical-based stellar spectra from arbitrary stellar atmospheric parameters. The relative flux-calibrated empirical spectra can be predicted by SSF given arbitrary effective temperature, surface gravity, and metallicity. SSF constructs the interpolation approach based on the SLAM, using ATLAS-A library as the training dataset. SSF is composed of 4 data-driven sub-models to predict empirical stellar spectra. SSF-N can generate spectra from A to K type and some M giant stars, covering 3700 < Teff < 8700 K, 0 < logg < 6 dex, and -1.5 < [M/H] < 0.5 dex. SSF-gM is mainly used to predict M giant spectra with 3520 < Teff < 4000K and -1.5 < [M/H] < 0.4 dex. SSF-dM is for generating M dwarf spectra with 3295 < Teff < 4040K, -1.0 < [M/H] < 0.1 dex. And SSF-B can predict B-type spectra with 9000 < Teff < 24000K and -5.2< MG < 1.5 mag. The accuracy of the predicted spectra is validated by comparing the flux of predicted spectra to those with same stellar parameters selected from the known spectral libraries, MILES and MaStar. The averaged difference of flux over optical wavelength between the predicted spectra and the corresponding ones in MILES and MaStar is less than 5%. More verification is conducted between the magnitudes calculated from the integration of the predicted spectra and the observations in PS1 and APASS bands with the same stellar parameters. No significant systematic difference is found between the predicted spectra and the photomatric observations. The uncertainty is 0.08mag in r band for SSF-gM when comparing with the stars with the same stellar parameters selected from PS1. And the uncertainty becomes 0.31mag in i band for SSF-dM when comparing with the stars with the same stellar parameters selected from APASS.

Abstract: The Dark Energy Camera (DECam) is a sensitive, wide field instrument mounted at the prime focus of the 4 m V. Blanco Telescope in Chile. Beside its main objectives, i.e. understanding the growth and evolution of structures in the Universe, the camera offers the opportunity to observe a 3 deg2 field of view in one single pointing and, with an adequate cadence, to identify the variable sources contained. In this paper, we present the result of a DECam observational campaign toward the LMC and give a catalogue of the observed variable sources. We considered all the available DECam observations of the LMC, acquired during 32 nights over a period of two years (from February 2018 to January 2020), and set up a specific pipeline for detecting and characterizing variable sources in the observed fields. Here, we report on the first 15 deg2 in and around the LMC as observed by DECam, testing the capabilities of our pipeline. Since many of the observed fields cover a rather crowded region of the sky, we adopted the ISIS subtraction package which, even in these conditions, can detect variables at a very low signal to noise ratio. All the potentially identified variable sources were then analyzed and each light curve tested for periodicity by using the Lomb-Scargle and Schwarzenberg-Czerny algorithms. Furthermore, we classified the identified sources by using the UPSILoN neural network. This analysis allowed us to find 70 981 variable stars, 1266 of which were previously unknown. We estimated the period of the variables and compared it with the available values in the catalogues. Moreover, for the 1266 newly detected objects, an attempted classification based on light curve analysis is presented.

Abstract: When gravitational waves (GWs) from a spinning neutron star arrive from behind the Sun, they are subjected to gravitational lensing that imprints a frequency-dependent modulation on the waveform. This modulation traces the projected solar density and gravitational potential along the path as the Sun passes in front of the neutron star. We calculate how accurately the solar density profile can be extracted from the lensed GWs using a Fisher analysis. For this purpose, we selected three promising candidates (the highly spinning pulsars J1022+1001, J1730-2304, and J1745-23) from the pulsar catalog of the Australia Telescope National Facility. The lensing signature can be measured with $3 \sigma$ confidence when the signal-to-noise ratio (SNR) of the GW detection reaches $100 \, (f/300 {\rm Hz})^{-1}$ over a one-year observation period (where $f$ is the GW frequency). The solar density profile can be plotted as a function of radius when the SNR improves to $\gtrsim 10^4$.

Abstract: OB associations are important probes of recent star formation and Galactic structure. In this study, we focus on the Auriga constellation, an important region of star formation due to its numerous young stars, star-forming regions and open clusters. We show using \textit{Gaia} data that its two previously documented OB associations, Aur OB1 and OB2, are too extended in proper motion and distance to be genuine associations, encouraging us to revisit the census of OB associations in Auriga with modern techniques. We identify 5617 candidate OB stars across the region using photometry, astrometry and our SED fitting code, grouping these into 5 high-confidence OB associations using HDBSCAN. Three of these are replacements to the historical pair of associations - Aur OB2 is divided between a foreground and a background association - while the other two associations are completely new. We connect these OB associations to the surrounding open clusters and star-forming regions, analyse them physically and kinematically, constraining their ages through a combination of 3D kinematic traceback, the position of their members in the HR diagram and their connection to clusters of known age. Four of these OB associations are expanding, with kinematic ages up to a few tens of Myr. Finally, we identify an age gradient in the region spanning several associations that coincides with the motion of the Perseus spiral arm over the last $\sim$20 Myr across the field of view.

Abstract: Context. The Optical Gravitational Lensing Experiment (OGLE) observed around 450,000 eclipsing binaries (EBs) towards the Galactic Bulge. Decade-long photometric observations such as these provide an exceptional opportunity to thoroughly examine the targets. However, observing dense stellar fields such as the Bulge may result in blends and contamination by close objects. Aims. We searched for periodic variations in the residual light curves of EBs in OGLE-IV and created a new catalogue for the EBs that contain `background' signals after the investigation of the source of the signal. Methods. From the about half a million EB systems, we selected those that contain more than 4000 data points. We fitted the EB signal with a simple model and subtracted it. To identify periodical signals in the residuals, we used a GPU-based phase dispersion minimisation python algorithm called cuvarbase and validated the found periods with Lomb-Scargle periodograms. We tested the reliability of our method with artificial light curves. Results. We identified 354 systems where short-period background variation was significant. In these cases, we determined whether it is a new variable or just the result of contamination by an already catalogued nearby one. We classified 292 newly found variables into EB, $\delta$ Scuti, or RR Lyrae categories, or their sub-classes, and collected them in a catalogue. We also discovered four new doubly eclipsing systems and one eclipsing multiple system with a $\delta$ Scuti variable, and modelled the outer orbits of the components.

Abstract: Aims. We introduce a new deep learning tool that estimates stellar parameters (such as effective temperature, surface gravity, and extinction) of young low-mass stars by coupling the Phoenix stellar atmosphere model with a conditional invertible neural network (cINN). Our networks allow us to infer the posterior distribution of each stellar parameter from the optical spectrum. Methods. We discuss cINNs trained on three different Phoenix grids: Settl, NextGen, and Dusty. We evaluate the performance of these cINNs on unlearned Phoenix synthetic spectra and on the spectra of 36 Class III template stars with well-characterised stellar parameters. Results. We confirm that the cINNs estimate the considered stellar parameters almost perfectly when tested on unlearned Phoenix synthetic spectra. Applying our networks to Class III stars, we find good agreement with deviations of at most 5--10 per cent. The cINNs perform slightly better for earlier-type stars than for later-type stars like late M-type stars, but we conclude that estimations of effective temperature and surface gravity are reliable for all spectral types within the network's training range. Conclusions. Our networks are time-efficient tools applicable to large amounts of observations. Among the three networks, we recommend using the cINN trained on the Settl library (Settl-Net), as it provides the best performance across the largest range of temperature and gravity.

Abstract: The detection of terrestrial planets by radial velocity and photometry is hindered by the presence of stellar signals. Those are often modeled as stationary Gaussian processes, whose kernels are based on qualitative considerations, which do not fully leverage the existing physical understanding of stars. Our aim is to build a formalism which allows to transfer the knowledge of stellar activity into practical data analysis methods. In particular, we aim at obtaining kernels with physical parameters. This has two purposes: better modelling signals of stellar origin to find smaller exoplanets, and extracting information about the star from the statistical properties of the data. We consider several observational channels such as photometry, radial velocity, activity indicators, and build a model called FENRIR to represent their stochastic variations due to stellar surface inhomogeneities. We compute analytically the covariance of this multi-channel stochastic process, and implement it in the S+LEAF framework to reduce the cost of likelihood evaluations from $O(N^3)$ to $O(N)$. We also compute analytically higher order cumulants of our FENRIR model, which quantify its non-Gaussianity. We obtain a fast Gaussian process framework with physical parameters, which we apply to the HARPS-N and SORCE observations of the Sun, and constrain a solar inclination compatible with the viewing geometry. We then discuss the application of our formalism to granulation. We exhibit non-Gaussianity in solar HARPS radial velocities, and argue that information is lost when stellar activity signals are assumed to be Gaussian. We finally discuss the origin of phase shifts between RVs and indicators, and how to build relevant activity indicators. We provide an open-source implementation of the FENRIR Gaussian process model with a Python interface.