Hintze Centre for Astrophysical Surveys

Abstract:

At ~50 Mpc, the Hydra I cluster of galaxies is among the closest cluster in the z=0 Universe, and an ideal environment to study dwarf galaxy properties in a cluster environment. We exploit deep imaging data of the Hydra I cluster to construct a new photometric catalog of dwarf galaxies in the cluster core, which is then used to derive properties of the Hydra I cluster dwarf galaxies population as well as to compare with other clusters. Moreover, we investigate the dependency of dwarf galaxy properties on their surrounding environment. The new Hydra I dwarf catalog contains 317 galaxies with luminosity between -18.5<$M_r$<-11.5 mag, a semi-major axis larger than ~200 pc (a=0.84 arcsec), of which 202 are new detections, previously unknown dwarf galaxies in the Hydra I central region. We estimate that our detection efficiency reaches 50% at the limiting magnitude $M_r$=-11.5 mag, and at the mean effective surface brightness $\overline{\mu}_{e,r}$=26.5 mag/$arcsec^2$. We present the standard scaling relations for dwarf galaxies and compare them with other nearby clusters. We find that there are no observational differences for dwarfs scaling relations in clusters of different sizes. We study the spatial distribution of galaxies, finding evidence for the presence of substructures within half the virial radius. We also find that mid- and high-luminosity dwarfs ($M_r$<-14.5 mag) become on average redder toward the cluster center, and that they have a mild increase in $R_e$ with increasing clustercentric distance, similar to what is observed for the Fornax cluster. No clear clustercentric trends are reported with surface brightness and S\'ersic index. Considering galaxies in the same magnitude-bins, we find that for high and mid-luminosity dwarfs ($M_r$<-13.5 mag) the g-r color is redder for the brighter surface brightness and higher S\'ersic n index objects.Comment: Accepted for publication in A&A. 25 pages, 21 figure

Abstract:

Computer simulations are invaluable tools for scientific discovery. However, accurate simulations are often slow to execute, which limits their applicability to extensive parameter exploration, large-scale data analysis, and uncertainty quantification. A promising route to accelerate simulations by building fast emulators with machine learning requires large training datasets, which can be prohibitively expensive to obtain with slow simulations. Here we present a method based on neural architecture search to build accurate emulators even with a limited number of training data. The method successfully emulates simulations in 10 scientific cases including astrophysics, climate science, biogeochemistry, high energy density physics, fusion energy, and seismology, using the same super-architecture, algorithm, and hyperparameters. Our approach also inherently provides emulator uncertainty estimation, adding further confidence in their use. We anticipate this work will accelerate research involving expensive simulations, allow more extensive parameters exploration, and enable new, previously unfeasible computational discovery.