Related papers: Machine Learning Classification of Young Stellar O…

We perform a systematic study of evolutionary stages and stellar masses of young stellar objects (YSOs) in the Large Magellanic Cloud (LMC) to investigate properties of star formation of the galaxy. There are 4825 sources in our YSO sample,…

Nowadays, Machine Learning techniques offer fast and efficient solutions for classification problems that would require intensive computational resources via traditional methods. We examine the use of a supervised Random Forest to classify…

With the coming data deluge from synoptic surveys, there is a growing need for frameworks that can quickly and automatically produce calibrated classification probabilities for newly-observed variables based on a small number of time-series…

We have introduced a semi-automated quantitative method to estimate the age and reddening of 1072 star clusters in the Large Magellanic Cloud (LMC) using the Optical Gravitational Lensing Experiment (OGLE) III survey data. This study brings…

Low- and intermediate-mass stars are one of the important dust sources in the interstellar medium (ISM) of galaxies. The compositions of dust ejected from these stars are likely to affect those in the ISM. We investigate dust in…

The Small Magellanic Cloud (SMC) provides a unique laboratory for the study of the lifecycle of dust given its low metallicity (~1/5 solar) and relative proximity (~60 kpc). This motivated the SAGE-SMC (Surveying the Agents of Galaxy…

The Magellanic System (MS), consisting of the Large Magellanic Cloud (LMC), the Small Magellanic Cloud (SMC) and the Magellanic Bridge (MBR), contains diverse sample of star clusters. Their spatial distribution, ages and chemical abundances…

Stellar parameters for large samples of stars play a crucial role in constraining the nature of stars and stellar populations in the Galaxy. An increasing number of medium-band photometric surveys are presently used in estimating stellar…

We use the Random Forest (RF) algorithm to develop a tool for automated activity classification of galaxies into 5 different classes: Star-forming (SF), AGN, LINER, Composite, and Passive. We train the algorithm on a combination of mid-IR…

We quadruple the number of quasars known behind the Large Magellanic Cloud (LMC) from 55 (42 in the LMC fields of the third phase of the Optical Gravitational Lensing Experiment (OGLE)) to 200 by spectroscopically confirming 169 (144 new)…

We study the M-type asymptotic giant branch (AGB) population of the Large Magellanic Cloud (LMC) by characterizing the individual sources in terms of the main properties of the progenitors and of the dust present in the circumstellar…

We present the first results from the SAGE-Var program, a follow on to the Spitzer legacy program Surveying the Agents of Galaxy Evolution (SAGE; Meixner, et al. 2006). We obtained 4 epochs of photometry at 3.6 & 4.5 microns covering the…

Dust radiative transfer models are presented for all 374 AGB stars candidates in one of the fields observed by the new VISTA survey of the Magellanic Clouds (VMC). Mass-loss rates, luminosities and a classification of C- and O-rich stars…

Planetary Nebulae (PNs) in the Magellanic Clouds offer the unique opportunity to study both the population and evolution of low- and intermediate-mass stars, in an environment that is free of the distance scale bias and the differential…

The environment plays a critical role in galaxy evolution, with galaxy clusters and their infall regions offering diverse conditions that shape galaxies before they enter the dense cluster core, a process known as ``pre-processing''.…

(abridged) Star formation in the outer Galaxy, i.e., outside of the Solar circle, has been lightly studied in part due to low CO brightness of molecular clouds linked with the negative metallicity gradient. Recent infrared surveys provide…

We assemble the most complete and clean red supergiant (RSG) sample (2,121 targets) so far in the Small Magellanic Cloud (SMC) with 53 different bands of data to study the MLR of RSGs. In order to match the observed spectral energy…

Studies of young stellar objects (YSOs) in the Galaxy have found that a significant fraction exhibit photometric variability. However, no systematic investigation has been conducted on the variability of extragalactic YSOs. Here we present…

Machine learning allows efficient extraction of physical properties from stellar spectra that have been obtained by large surveys. The viability of ML approaches has been demonstrated for spectra covering a variety of wavelengths and…

Stellar population models are commonly calculated using star clusters as calibrators for those evolutionary stages that depend on free parameters. However, discrepancies exist among different models, even if similar sets of calibration…