Beecroft Institute for Particle Astrophysics and Cosmology

Abstract:

Abstract Mergers play a complex role in galaxy formation and evolution. Continuing to improve our understanding of these systems requires ever larger samples, which can be difficult (even impossible) to select from individual surveys. We use the new platform ESA Datalabs to assemble a catalog of interacting galaxies from the Hubble Space Telescope science archives; this catalog is larger than previously published catalogs by nearly an order of magnitude. In particular, we apply the Zoobot convolutional neural network directly to the entire public archive of HST F814W images and make probabilistic interaction predictions for 126 million sources from the Hubble Source Catalog. We employ a combination of automated visual representation and visual analysis to identify a clean sample of 21,926 interacting galaxy systems, mostly with z < 1. Sixty-five percent of these systems have no previous references in either the NASA Extragalactic Database or Simbad. In the process of removing contamination, we also discover many other objects of interest, such as gravitational lenses, edge-on protoplanetary disks, and “backlit” overlapping galaxies. We briefly investigate the basic properties of this sample, and we make our catalog publicly available for use by the community. In addition to providing a new catalog of scientifically interesting objects imaged by HST, this work also demonstrates the power of the ESA Datalabs tool to facilitate substantial archival analysis without placing a high computational or storage burden on the end user.

Abstract:

The role of baryonic physics, star formation and stellar feedback, in shaping the galaxies and their host halos is an evolving topic. The dark matter aspects are illustrated in this work by showing distribution features in a Milky Way sized halo. We focus on the halo morphology, geometry, and profile as well as the phase space distribution using one dark matter only and five hydrodynamical cosmological high-resolution simulations of the same halo with different subgrid prescriptions for the baryonic physics (Kennicut versus multi-freefall star formation and delayed cooling versus mechanical supernovae feedback). If some general properties like the relative halo-galaxy orientation are similar, the modifications of the gravitational potential due to the presence of baryons are found to induce different dark matter distributions (rounder and more concentrated halo). The mass density profile as well as the velocity distribution are modified distinctively according to the specific resulting baryonic distribution highlighting the variability of those properties (e.g inner power index from 1.3 to 1.8, broader speed distribution). The uncertainties on those features are of paramount importance for dark matter phenomenology, particularly when dealing with dark matter dynamics or direct and indirect detection searches. As a consequence, dark matter properties and prospects using cosmological simulations require improvement on baryonic physics description. Modeling such processes is a key issue not only for galaxy formation but also for dark matter investigations.

Abstract:

International audienceWe study primordial non-Gaussian signatures in the redshift-space halo field on non-linear scales, using a quasi-maximum likelihood estimator based on optimally compressed power spectrum and modal bispectrum statistics. We train and validate the estimator on a suite of halo catalogues constructed from the Quijote-PNG N-body simulations, which we release to accompany this paper. We verify its unbiasedness and near optimality, for the three main types of primordial non-Gaussianity (PNG): local, equilateral, and orthogonal. We compare the modal bispectrum expansion with a $k$-binning approach, showing that the former allows for faster convergence of numerical derivatives in the computation of the score-function, thus leading to better final constraints. We find, in agreement with previous studies, that the local PNG signal in the halo-field is dominated by the scale-dependent bias signature on large scales and saturates at $k \sim 0.2~h\,\mathrm{Mpc}^{-1}$, whereas the small-scale bispectrum is the main source of information for equilateral and orthogonal PNG. Combining power spectrum and bispectrum on non-linear scales plays an important role in breaking degeneracies between cosmological and PNG parameters; such degeneracies remain however strong for equilateral PNG. We forecast that PNG parameters can be constrained with $\Delta f_\mathrm{NL}^\mathrm{local} = 45$, $\Delta f_\mathrm{NL}^\mathrm{equil} = 570$, $\Delta f_\mathrm{NL}^\mathrm{ortho} = 110$, on a cubic volume of $1 \left({ {\rm Gpc}/{ {\rm h}}} \right)^3$, at $z = 1$, considering scales up to $k_\mathrm{max} = 0.5~\mathrm{Mpc}^{-1}$