Hintze Centre for Astrophysical Surveys

Abstract:

Large galaxy samples from multi-object IFS surveys now allow for a statistical analysis of the z~0 galaxy population using resolved kinematics. However, the improvement in number statistics comes at a cost, with multi-object IFS surveys more severely impacted by the effect of seeing and lower signal-to-noise. We present an analysis of ~1800 galaxies from the SAMI Galaxy Survey and investigate the spread and overlap in the kinematic distributions of the spin parameter proxy $\lambda_{Re}$ as a function of stellar mass and ellipticity. For SAMI data, the distributions of galaxies identified as regular and non-regular rotators with $kinemetry$ show considerable overlap in the $\lambda_{Re}$-$\varepsilon_e$ diagram. In contrast, visually classified galaxies (obvious and non-obvious rotators) are better separated in $\lambda_{Re}$ space, with less overlap of both distributions. Then, we use a Bayesian mixture model to analyse the $\lambda_{Re}$-$\log(M_*/M_{\odot})$ distribution. As a function of mass, we investigate whether the data are best fit with a single kinematic distribution or with two. Below $\log(M_*/M_{\odot})$~10.5 a single beta distribution is sufficient to fit the complete $\lambda_{Re}$ distribution, whereas a second beta distribution is required above $\log(M_*/M_{\odot})$~10.5 to account for a population of low-$\lambda_{Re}$ galaxies, presenting the cleanest separation of the two populations. We apply the same analysis to mock-observations from cosmological simulations. The mixture model predicts a bimodal $\lambda_{Re}$ distribution for all simulations, albeit with different positions of the $\lambda_{Re}$ peaks and with different ratios of both populations. Our analysis validates the conclusions from previous, smaller IFS surveys, but also demonstrates the importance of using kinematic selection criteria that are dictated by the quality of the observed or simulated data.

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.