Alina Khalikova | Searcharxiv

arXiv2026

IY Lyr: A Thick-Disk first-overtone RR Lyrae Star with a Possible Neutron Star Companion

IY Lyr, historically misclassified as an eclipsing binary, has been previously identified as a first-overtone RR Lyrae star (RRc star). Using multiband photometry (All-Sky Automated Survey for Supernovae, Zwicky Transient Facility, TESS, and our BVRI data), Large Sky Area Multi-Object Fiber Spectroscopic Telescope spectroscopy, and Gaia astrometry, we investigate its pulsation, binarity, and Galactic population. From O-C analysis, we detect a long-term period decrease and a light-travel time effect with an orbital period of 3.94 $\pm$ 0.09 years, eccentricity of 0.46 $\pm$ 0.15, and a mass function of 0.65 $\pm$ 0.14 M$_{\odot}$. The companion is independently supported by radial velocity residuals and Gaia proper motions. Combined constraints yield an orbital inclination of 94.2$^{\circ}$ $\pm$ 1.1$^{\circ}$ and a companion mass of 1.37 $\pm$ 0.19 M$_{\odot}$. Chemical abundances ([Fe/H] $\simeq$ -1.0 $\pm$ 0.1, [$α$/Fe] $\simeq$ +0.27 $\pm$ 0.03, Xiang et al. 2019) and dynamics ($L_{\rm z}$ $\simeq$ 1287 $\pm$ 35 kpc km s$^{-1}$, $Z_{\rm max}$ $\simeq$ 1.17 $\pm$ 0.10 kpc) identify IY Lyr as likely an old, high-$α$, thick-disk star. The companion mass lies at the peak of the neutron star mass distribution, and the system's age excludes a main-sequence star; we conclude the companion is most likely a typical neutron star, although a massive white dwarf near the Chandrasekhar limit cannot be ruled out. IY Lyr is among the few RRc binaries with a compact companion supported by multiple methods, and it has important implications for thick-disk binary evolution and neutron star formation.

astro-ph.SR↗

arXiv2020

Multiwavelength classification of X-ray selected galaxy cluster candidates using convolutional neural networks

Galaxy clusters appear as extended sources in XMM-Newton images, but not all extended sources are clusters. So, their proper classification requires visual inspection with optical images, which is a slow process with biases that are almost impossible to model. We tackle this problem with a novel approach, using convolutional neural networks (CNNs), a state-of-the-art image classification tool, for automatic classification of galaxy cluster candidates. We train the networks on combined XMM-Newton X-ray observations with their optical counterparts from the all-sky Digitized Sky Survey. Our data set originates from the X-CLASS survey sample of galaxy cluster candidates, selected by a specially developed pipeline, the XAmin, tailored for extended source detection and characterisation. Our data set contains 1 707 galaxy cluster candidates classified by experts. Additionally, we create an official Zooniverse citizen science project, The Hunt for Galaxy Clusters, to probe whether citizen volunteers could help in a challenging task of galaxy cluster visual confirmation. The project contained 1 600 galaxy cluster candidates in total of which 404 overlap with the expert's sample. The networks were trained on expert and Zooniverse data separately. The CNN test sample contains 85 spectroscopically confirmed clusters and 85 non-clusters that appear in both data sets. Our custom network achieved the best performance in the binary classification of clusters and non-clusters, acquiring accuracy of 90 %, averaged after 10 runs. The results of using CNNs on combined X-ray and optical data for galaxy cluster candidate classification are encouraging and there is a lot of potential for future usage and improvements.

astro-ph.CO↗