Hintze Centre for Astrophysical Surveys

Abstract:

Black hole (BH) low mass X-ray binaries in their hard spectral state are found to display two different correlations between the radio emission from the compact jets and the X-ray emission from the inner accretion flow. Here, we present a large data set of quasi-simultaneous radio and X-ray observations of the recently discovered accreting BH MAXI J1348–630 during its 2019/2020 outburst. Our results span almost six orders of magnitude in X-ray luminosity, allowing us to probe the accretion–ejection coupling from the brightest to the faintest phases of the outburst. We find that MAXI J1348–630 belongs to the growing population of outliers at the highest observed luminosities. Interestingly, MAXI J1348–630 deviates from the outlier track at LX ≲ 7 × 1035(D/2.2  kpc)2 erg s−1 and ultimately rejoins the standard track at LX ≃ 1033(D/2.2 kpc)2 erg s−1, displaying a hybrid radio/X-ray correlation, observed only in a handful of sources. However, for MAXI J1348–630 these transitions happen at luminosities much lower than what observed for similar sources (at least an order of magnitude). We discuss the behaviour of MAXI J1348–630 in light of the currently proposed scenarios and highlight the importance of future deep monitorings of hybrid correlation sources, especially close to the transitions and in the low luminosity regime.

Abstract:

High-energy-density physics is the field of physics concerned with studying matter at extremely high temperatures and densities. Such conditions produce highly nonlinear plasmas, in which several phenomena that can normally be treated independently of one another become strongly coupled. The study of these plasmas is important for our understanding of astrophysics, nuclear fusion and fundamental physics—however, the nonlinearities and strong couplings present in these extreme physical systems makes them very difficult to understand theoretically or to optimize experimentally. Here we argue that machine learning models and data-driven methods are in the process of reshaping our exploration of these extreme systems that have hitherto proved far too nonlinear for human researchers. From a fundamental perspective, our understanding can be improved by the way in which machine learning models can rapidly discover complex interactions in large datasets. From a practical point of view, the newest generation of extreme physics facilities can perform experiments multiple times a second (as opposed to approximately daily), thus moving away from human-based control towards automatic control based on real-time interpretation of diagnostic data and updates of the physics model. To make the most of these emerging opportunities, we suggest proposals for the community in terms of research design, training, best practice and support for synthetic diagnostics and data analysis.