arXiv subjects

Rajorshi Bhattacharya

Publications and source records attributed to Rajorshi Bhattacharya.

3 recordsLinked to original sources

arXiv2026

Mapping the Inner Milky Way with Infrared-Derived Distances to AGB Stars

The structure and evolution of the Milky Way (MW) can be traced with distance estimates to the evolved stellar populations in the inner galactic region. However, direct astrometric distances remain unavailable or highly uncertain for the majority of these sources due to instrumental limitations, large angular diameters, and complex variability. In this work, we develop a supervised machine-learning model to estimate statistical distances to oxygen-rich asymptotic giant branch (AGB) stars selected from the AKARI mid-infrared survey. We build an XGBoost regression model that maps multi-band IR photometry to distance using a training set of AGB stars with previously derived SED-based distances achieving a mean absolute percentage error (MAPE) of 6% on an independent test set. Distance estimates for over 36,000 AGB sources are obtained within a 36% total error margin, greatly expanding distance coverage (0.5-20 kpc) for dust-obscured AGB populations in the Galactic plane. We find good agreement with reported distances for Galactic Mira variables and independent period-luminosity relations. Utilizing the expanded distance set, we further investigate the spatial distribution of Mira variables in the Galactic bulge and disk. Longer-period Miras preferentially trace the bulge's barred morphology compared to their shorter-period counterparts that populate the disk. We also find roughly constant, but differing, relative scale heights for the bulge and disk, with the bulge vertical dispersion about 30% larger. These findings show that IR photometry-derived statistical distances can recover large-scale Galactic structures and establish long-period Mira variables as efficient tracers of stellar populations in heavily obscured regions of the MW.

astro-ph.GA↗

arXiv2024

Classification of Wolf Rayet stars using Ensemble-based Machine Learning algorithms

We develop a robust Machine Learning classifier model utilizing the eXtreme-Gradient Boosting (XGB) algorithm for improved classification of Galactic Wolf-Rayet (WR) stars based on Infrared (IR) colors and positional attributes. For our study, we choose an extensive dataset of 6555 stellar objects (from 2MASS and AllWISE data releases) lying in the Milky Way (MW) with available photometric magnitudes of different types including WR stars. Our XGB classifier model can accurately (with an 86\% detection rate) identify a sufficient number of WR stars against a large sample of non-WR sources. The XGB model outperforms other ensemble classifier models such as the Random Forest. Also, using the XGB algorithm, we develop a WR sub-type classifier model that can differentiate the WR subtypes from the non-WR sources with a high model accuracy ($>60\%$). Further, we apply both XGB-based models to a selection of 6457 stellar objects with unknown object types, detecting 58 new WR star candidates and predicting sub-types for 10 of them. The identified WR sources are mainly located in the Local spiral arm of the MW and mostly lie in the solar neighborhood.

astro-ph.SR↗

arXiv2024

Distance estimate method for Asymptotic Giant Branch stars using Infrared Spectral Energy Distributions

We present a method to estimate distances to Asymptotic Giant Branch (AGB) stars in the Galaxy, using spectral energy distributions (SEDs) in the near- and mid-infrared. By assuming that a given set of source properties (initial mass, stellar temperature, composition, and evolutionary stage) will provide a typical SED shape and brightness, sources are color-matched to a distance-calibrated template and thereafter scaled to extract the distance. The method is tested by comparing the distances obtained to those estimated from Very Long Baseline Interferometry or Gaia parallax measurements, yielding a strong correlation in both cases. Additional templates are formed by constructing a source sample likely to be close to the Galactic center, and thus with a common, typical distance for calibration of the templates. These first results provide statistical distance estimates to a set of almost 15,000 Milky Way AGB stars belonging to the Bulge Asymmetries and Dynamical Evolution (BAaDE) survey, with typical distance errors of $\pm 35$%. With these statistical distances a map of the intermediate-age population of stars traced by AGBs is formed, and a clear bar structure can be discerned, consistent with the previously reported inclination angle of 30$^\circ$ to the GC-Sun direction vector. These results motivate deeper studies of the AGB population to tease out the intermediate-age stellar distribution throughout the Galaxy, as well as determining statistical properties of the AGB population luminosity and mass-loss rate distributions.

astro-ph.GA↗