0 Assigned ID: 2284 ----- Attempting Submission 1 (Fri Apr 09 21:26:58 GMT 2021) ----- Globular clusters (GCs) contain end products of stellar evolution (neutron stars, black holes, and white dwarfs) found in X-ray binaries of various types as well as in isolation. Identifying the types of these compact objects and their binary companions, and comparing their properties with the properties of the host clusters sheds light on stellar evolution, properties of SN explosions, evolution of tight binaries, and dynamical interactions in crowded environments. Pursuing these diverse and exciting science topics requires confident classifications of large numbers of X-ray sources in GCs. Since most of these sources are faint and GCs are very crowded, the HST data are required to perform reliable matching and classifications. We propose to build upon the results of previous studies to produce a training dataset from confidently classified X-ray sources in all GCs which will include multiwavelength properties measured with HST and Chandra. We will also adopt an existing automated (machine-learning) multiwavelength classification pipeline to the GCs environments and perform classifications for all X-ray sources in 3 massive GCs with deep Chandra and milti-band, multi-epoch HST observations. In this project we will test a novel machine learning approach and verify it against the traditional classification methods. We will also perform a comprehensive census of all previously classified X-ray sources in all GCs observed by CXO and HST. The corresponding database will serve as a training dataset for the classification pipeline. The database and the classification pipeline will be released to the public to aid with classification of X-ray sources in other GCs. Dr. Oleg Kargaltsev is an expert in observations of compact objects (both isolated and in binaries). Over the past 15 years he has been PI of multiple observing programs with Hubble Space Telescope (5 programs), Chandra (13 programs), and XMM-Newton (12 programs). During this period, Dr. Kargaltsev published 16 papers based on HST observations of compact objects and more than fifty papers based on Chandra and XMM-Newton observations pulsars, neutron stars, pulsar-wind nebulae, X-ray binaries, and unidentified X-ray and gamma-ray sources. Dr. Kargaltsev has extensive expertise in HST (WFC3, ACS, COS, STIS), Chandra ACIS, and XMM-Newton EPIC data reduction and analysis. Dr. Kargaltsev has been pursuing machine-learning approach to X-ray source classification for the past few years. This effort culminated in MUWCLASS pipeline which has been used with Chandra X-ray and ground based optical survey data with the results published in 4 papers. Dr. Jeremy Hare is an expert in HST, Chandra, and XMM-Newton data analysis, including HST ACS/WFC3 data analysis for imaging observations of crowded fields such as globular clusters (most recently published ACS/WFC3 observations and analysis of the Glimpse C01 cluster). Dr. Hare is an expert in machine-learning applications to X-ray source classification currently overseeing the MUWCLASS pipeline (Klingler et al. 2020, ApJ, 901, 157) development. Dr. George Pavlov is an expert in neutron stars and their observations, including first detections of neutron stars in the optical and UV and modeling emission from NSs. In the past decade Dr. Pavlov has led several HST campaigns with 11 papers published on data analysis from carried out HST observations. Dr. Blagoy Rangelov is an expert in N-body simulations and multiwavelength identification of X-ray binaries and neutron stars. Dr. Rangelov is also an expert in HST and X-ray data analysis. Mr. Hui Yang is a PhD student in Dr. Kargaltsev's group at GWU working on machine learning classifcaion of X-ray sources with our MUWCLASS pipeline. Mr. Steven Chen is a PhD student in Dr. Kargaltsev's group at GWU working on X-ray source populations in stellar clusters using multiwavelength data. Stellar Physics and Stellar Types Stellar Populations and the Interstellar Medium 0 Regular false false false false false SUPPORTED