Beecroft Institute for Particle Astrophysics and Cosmology

Abstract:

Strong lenses are extremely useful probes of the distribution of matter on galaxy and cluster scales at cosmological distances, but are rare and difficult to find. The number of currently known lenses is on the order of 1,000. We wish to use crowdsourcing to carry out a lens search targeting massive galaxies selected from over 442 square degrees of photometric data from the Hyper Suprime-Cam (HSC) survey. We selected a sample of $\sim300,000$ galaxies with photometric redshifts in the range $0.2 < z_{phot} < 1.2$ and photometrically inferred stellar masses $\log{M_*} > 11.2$. We crowdsourced lens finding on this sample of galaxies on the Zooniverse platform, as part of the Space Warps project. The sample was complemented by a large set of simulated lenses and visually selected non-lenses, for training purposes. Nearly 6,000 citizen volunteers participated in the experiment. In parallel, we used YattaLens, an automated lens finding algorithm, to look for lenses in the same sample of galaxies. Based on a statistical analysis of classification data from the volunteers, we selected a sample of the most promising $\sim1,500$ candidates which we then visually inspected: half of them turned out to be possible (grade C) lenses or better. Including lenses found by YattaLens or serendipitously noticed in the discussion section of the Space Warps website, we were able to find 14 definite lenses, 129 probable lenses and 581 possible lenses. YattaLens found half the number of lenses discovered via crowdsourcing. Crowdsourcing is able to produce samples of lens candidates with high completeness and purity, compared to currently available automated algorithms. A hybrid approach, in which the visual inspection of samples of lens candidates pre-selected by discovery algorithms and/or coupled to machine learning is crowdsourced, will be a viable option for lens finding in the 2020s.

Abstract:

Our view on the deep universe has been so far biased towards optically bright galaxies. Now, the measurement of the Cosmic Infrared Background in FIRAS and DIRBE residuals, and the observations of FIR/submm sources by the ISOPHOT and SCUBA instruments begin unveiling the ``optically dark side'' of galaxy formation. Though the origin of dust heating is still unsolved, it appears very likely that a large fraction of the FIR/submm emission is due to heavily-extinguished star formation. Consequently, the level of the CIRB implies that about 2/3 of galaxy/star formation in the universe is hidden by dust shrouds. In this review, we introduce a new modeling of galaxy formation and evolution that provides us with specific predictions in FIR/submm wavebands. These predictions are compared with the current status of the observations. Finally, the capabilities of current and forthcoming instruments for all-sky and deep surveys of FIR/submm sources are briefly described.

Abstract:

The interplay between cosmic gas accretion onto galaxies and galaxy mergers drives the observed morphological diversity of galaxies. By comparing the state-of-the-art hydrodynamical cosmological simulations Horizon-AGN and Horizon-noAGN, we unambiguously identify the critical role of Active Galactic Nuclei (AGN) in setting up the correct galaxy morphology for the massive end of the population. With AGN feedback, typical kinematic and morpho-metric properties of galaxy populations as well as the galaxy-halo mass relation are in much better agreement with observations. Only AGN feedback allows massive galaxies at the center of groups and clusters to become ellipticals, while without AGN feedback those galaxies reform discs. It is the merger-enhanced AGN activity that is able to freeze the morphological type of the post-merger remnant by durably quenching its quiescent star formation. Hence morphology is shown not to be purely driven by mass but also by the nature of cosmic accretion: at constant galaxy mass, ellipticals are galaxies that are mainly assembled through mergers, while discs are preferentially built from the in situ star formation fed by smooth cosmic gas infall.

Abstract:

Cosmological hydrodynamical simulations are rich tools to understand the build-up of stellar mass and angular momentum in galaxies, but require some level of calibration to observations. We compare predictions at $z\sim0$ from the Eagle, Hydrangea, Horizon-AGN, and Magneticum simulations with IFS data from the SAMI Galaxy Survey, ATLAS-3D, CALIFA and MASSIVE surveys. The main goal of this work is to simultaneously compare structural, dynamical, and stellar population measurements. We have taken great care to ensure that our simulated measurements match the observational methods as closely as possible, and we construct samples that match the observed stellar mass distribution for the combined IFS sample. We find that the Eagle and Hydrangea simulations reproduce many galaxy relations but with some offsets at high stellar masses. There are moderate mismatches in $R_e$ (+), $\epsilon$ (-), $\sigma_e$ (-), and mean stellar age (+), where a plus sign indicates that quantities are too high on average, and minus sign too low. The Horizon-AGN simulations qualitatively reproduce several galaxy relations, but there are a number of properties where we find a quantitative offset to observations. Massive galaxies are better matched to observations than galaxies at low and intermediate masses. Overall, we find mismatches in $R_e$ (+), $\epsilon$ (-), $\sigma_e$ (-) and $(V/\sigma)_e$ (-). Magneticum matches observations well: this is the only simulation where we find ellipticities typical for disk galaxies, but there are moderate differences in $R_e$ (+), $\sigma_e$ (-), $(V/\sigma)_e$ (-) and mean stellar age (+). Our comparison between simulations and observational data has highlighted several areas for improvement, such as the need for improved modelling resulting in a better vertical disk structure, yet our results demonstrate the vast improvement of cosmological simulations in recent years.

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.