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We present a novel approach to identify galaxy clusters that are undergoing a merger using a deep learning approach. This paper uses massive galaxy clusters spanning 0 ≤ z ≤ 2 from The Three Hundred project, a suite of hydrodynamic resimulations of 324 large galaxy clusters. Mock, idealized Compton- y and X-ray maps were constructed for the sample, capturing them out to a radius of 2 R 200. The idealized nature of these maps mean they do not consider observational effects such as foreground or background astrophysical objects, any spatial resolution limits or restriction on X-ray energy bands. Half of the maps belong to a merging population as defined by a mass increase Δ M/M ≥ 0.75, and the other half serves as a controlled, relaxed population. We employ a convolutional neural network architecture and train the model to classify clusters into one of the groups. A best-performing model was able to correctly distinguish between the two populations with a balanced accuracy (BA) and recall of 0.77, ROC-AUC of 0.85, PR-AUC of 0.55, and F 1 score of 0.53. Using a multichannel model relative to a single-channel model, we obtain a 3 per cent improvement in BA score, and a 6 per cent improvement in F 1 score. We use a saliency interpretation approach to discern the regions most important to each classification decision. By analysing radially binned saliency values we find a preference to utilize regions out to larger distances for mergers with respect to non-mergers, greater than ∼1.2 R 200 and ∼0.7 R 200 for SZ and X-ray, respectively. [ABSTRACT FROM AUTHOR]

Recently, a new population of circular radio (∼GHz) objects has been discovered at high Galactic latitudes, called the odd radio circles (ORCs). A fraction of the ORCs encircles massive galaxies in the sky with stellar mass ∼1011 M⊙ situated at z  = 0.2–0.6, suggesting a possible physical connection. In this paper, we explore the possibility that these radio circles originate from the accretion shocks/virial shocks around massive (⁠|${\gtrsim} 10^{13}\, \ {\rm M}_\odot$|⁠) dark matter halo at z ∼ 0.5. We found that the radio flux density of the emitting shell is marginally consistent with the ORCs. We also find that pure advection of electrons from the shock results in a radio-emitting shell that is considerably narrower than the observed one due to strong inverse-Compton cooling of electrons. Instead, we show that the diffusion of cosmic-ray (CR) electrons plays a significant role in increasing the width of the shell. We infer a diffusion coefficient, |$D_{\rm cr} \sim 10^{30}\ {\rm cm^2\, s^{-1}}$|⁠ , consistent with the values expected for low-density circumgalactic medium (CGM). If ORCs indeed trace virial shocks, then our derived CR diffusion coefficient represents one of the few estimations available for the low-density CGM. Finally, we show that the apparent discrepancy between ORC and halo number density can be mitigated by considering an incomplete halo virialization and the limited radiation efficiency of shocks. This study therefore opens up new avenues to study such shocks and non-thermal particle acceleration within them. Furthermore, our results suggest that low-mass galaxies (≲1013 M⊙) may not show ORCs due to their significantly lower radio surface brightness. [ABSTRACT FROM AUTHOR]

The overlap of galaxy surveys and cosmic microwave background (CMB) experiments presents an ideal opportunity for joint cosmological data set analyses. In this paper we develop a halo model-based method for the first joint analysis combining these two experiments using 10 correlated two-point functions (10 × 2pt) derived from galaxy position, galaxy shear, CMB lensing convergence, and Compton- y fields. We explore this method using the Vera Rubin Observatory Legacy Survey of Space and Time (LSST) and the Simons Observatory (SO) as examples. We find such LSS × CMB joint analyses lead to significant improvement in Figure-of-Merit of Ωm and S 8 over the constraints from using LSS-only probes within Λ cold dark matter (ΛCDM) model. We identify that the shear– y and y–y correlations are the most valuable additions when thermal Sunyaev–Zel'dolvich (tSZ) is included. We further identify the dominant sources of halo model uncertainties in the small-scale modelling, and investigate the impact of halo self-calibration due to the inclusion of small-scale tSZ information. [ABSTRACT FROM AUTHOR]