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The First High-contrast Images of X-Ray Binaries: Detection of Candidate Companions in the γ Cas Analog RX J1744.7-2713

Published version
Peer-reviewed

Change log

Abstract

jats:titleAbstract</jats:title> jats:pX-ray binaries provide exceptional laboratories for understanding the physics of matter under the most extreme conditions. Until recently, there were few, if any, observational constraints on the circumbinary environments of X-ray binaries at ∼100–5000 au scales. It remains unclear how the accretion onto the compact objects or the explosions giving rise to the compact objects interact with their immediate surroundings. Here, we present the first high-contrast adaptive optics images of X-ray binaries. These observations target all X-ray binaries within ∼3 kpc accessible with the Keck/NIRC2 vortex coronagraph. This paper focuses on one of the first key results from this campaign; our images reveal the presence of 21 sources potentially associated with the jats:italicγ</jats:italic> Cassiopeiae analog high-mass X-ray binary RX J1744.7−2713. By conducting different analyses—a preliminary proper motion analysis, a color–magnitude diagram, and a probability of chance alignment calculation—we found that three of these 21 sources have a high probability of being bound to the system. If confirmed, they would be in wide orbits (∼450 to 2500 au). While follow-up astrometric observations will be needed in ∼5–10 yr to confirm further the bound nature of these detections, these discoveries emphasize that such observations may provide a major breakthrough in the field. In fact, they would be useful not only for our understanding of stellar multiplicity, but also for our understanding of how planets, brown dwarfs, and stars can form even in the most extreme environments.</jats:p>

Description

Keywords

5101 Astronomical Sciences, 51 Physical Sciences

Journal Title

The Astronomical Journal

Conference Name

Journal ISSN

0004-6256
1538-3881

Volume Title

164

Publisher

American Astronomical Society
Sponsorship
Institute for Data Valorisation (RNA80117)