My research interests are diverse and yet focused on understanding the populations of endpoints of stellar evolution using multi-wavelength (MW) data and state-of-the-art computational methods such as machine-learning. Neutron stars (NSs) and black holes (BHs) give us unique insight into the physical processes and conditions (found nowhere else) that take place in the vicinity of these extreme astrophysics laboratories. To study these objects, I am interested in investigating their progenitors and birth sites, which involves understanding of evolution of massive stars and binaries, as well as the formation and evolution of tight binaries in star clusters. Here are my publications on ADS.
Automated Classification of X-ray Sources in Nearby Galaxies
I am currently working on a project to classify X-ray sources in M33 using MW data including Hubble Space Telescope (HST) and the Chandra X-ray Observatory (Chandra). Only a small fraction (1/4) of the ~700 X-ray sources in M33 have plausible identifications. The goal of the program is to create an automated classification pipeline, using machine-learning methods, which would be applicable to other galaxies enabling us to perform fast and reliable classification studies of X-ray source populations in galaxies with different environments (e.g., star formation history, metallicity).
The First Detection of a Pulsar Bow Shock in Far-UV
Galactic GeV and TeV Sources
A large fraction (~30%) of Fermi/LAT sources and ~50% of the H.E.S.S. and VERITAS TeV sources in the Galactic plane still lack reliable classifications or remain unidentified, with pulsars and PWNe being the major source of very high energy emission (Kargaltsev, Rangelov & Pavlov 2013). To investigate the processes leading to particle acceleration and high-energy emission from unidentified γ-ray source, revealing the nature of the γ-ray sources is essential. We accomplish this by identifying their X-rays counterparts. I have ongoing campaigns with Chandra and XMM-Newton to observe a sample of unidentified Fermi sources. We already carried out a classification of sources HESS J1809-193 (Rangelov et al. 2014), HESS J1741-302 (Hare, Rangelov et al. 2016), and HESS J1616-508 (Hare et al. 2017).
X-Ray Binaries and Star Clusters
It has been previously suggested that X-ray binaries (XRBs) may have formed in young star clusters, because they are preferentially located near star clusters, albeit with a significant displacement (~200 pc on average). I have used data from HST to derive masses and ages of clusters in nearby star-forming galaxies, and then performed a detailed comparison between the cluster population and XRBs discovered in the Chandra data. I compared the observations with synthetic X-ray source populations, and found that while the specific recipe used to create synthetic X-ray source populations can somewhat affect the results, it is clear that the observed spatial distribution of the XRBs differs from the simulated populations (Rangelov et al. 2011). In the Antennae merger, there is a high fraction (∼20-25%) of the XRBs still reside within their parent clusters (in contrast with our galaxy or the Magellanic Clouds). The chance superposition between the XRBs and clusters is only 1-2% in both systems, and it is highly unlikely to be the reason for the observed coincidences (Rangelov et al. 2012). Knowledge of the cluster coincident or related to an XRB could provide indirect, yet solid constraints on the nature of the XRBs. Results from N-body simulations indicate that dynamics can help constrain the type of the compact object (i.e., BH or NS) in XRBs. For example, our N-body simulations (Garofali et al. 2012) suggest that high-mass XRBs hosted by star clusters have a BH as the compact object, regardless of the metallicity-dependent evolutionary path that led to the formation of the compact object, because dynamically these are much less likely to be expelled from their parent clusters.