44 Search Results

ABSTRACT Within the ΛCDM cosmology, dark matter haloes are composed of both a smooth component and a population of smaller gravitationally bound subhaloes. These components are often treated as a single halo when properties, such as density profiles, are extracted from simulations. Recent work has shown that density profiles change substantially when subhalo mass is excluded. In this paper, we expand on this result by analysing three specific host halo properties – concentration (cNFW), spin (λB), and shape (c/a) – when calculated only from the smooth component of the halo. This analysis is performed on both Milky Way-mass haloes andmore » cluster-mass haloes in high-resolution zoom-in N-body simulations. We find that when subhaloes are excluded, the median value of (1) cNFW is enhanced by $$\approx 30\pm 11$$ and $$\approx 77\pm 8.1~{{\ \rm per\ cent}}$$ for Milky Way-mass ($$10^{12.1}\, \text{M}_\odot$$) and cluster-mass ($$10^{14.8}\, \text{M}_\odot$$) haloes, respectively, (2) λB is reduced for Milky Way-mass by $$\approx 11\pm 4.9~{{\ \rm per\ cent}}$$ and cluster-mass haloes by $$\approx 27\pm 3.5~{{\ \rm per\ cent}}$$. Additionally, with the removal of subhaloes, cluster-mass haloes tend to become more spherical as the ratio of minor-to-major axis, c/a, increases by $$\approx 11\pm 3.6~{{\ \rm per\ cent}}$$, whereas Milky Way-mass haloes remain approximately the same shape with c/a changed by $$\approx 1.0\pm 5.8~{{\ \rm per\ cent}}$$. Fractional changes of each of these properties depend primarily on the amount of mass in subhaloes and, to a lesser extent, mass accretion history. Our findings demonstrate that the properties of the smooth components of dark matter haloes are biased relative to the total halo mass.« less

Abstract We introduce the DESI LOW- Z Secondary Target Survey, which combines the wide-area capabilities of the Dark Energy Spectroscopic Instrument (DESI) with an efficient, low-redshift target selection method. Our selection consists of a set of color and surface brightness cuts, combined with modern machine-learning methods, to target low-redshift dwarf galaxies ( z < 0.03) between 19 < r < 21 with high completeness. We employ a convolutional neural network (CNN) to select high-priority targets. The LOW- Z survey has already obtained over 22,000 redshifts of dwarf galaxies ( M _* < 10 ⁹ M _⊙ ), comparable to themore » number of dwarf galaxies discovered in the Sloan Digital Sky Survey DR8 and GAMA. As a spare fiber survey, LOW- Z currently receives fiber allocation for just ∼50% of its targets. However, we estimate that our selection is highly complete: for galaxies at z < 0.03 within our magnitude limits, we achieve better than 95% completeness with ∼1% efficiency using catalog-level photometric cuts. We also demonstrate that our CNN selections z < 0.03 galaxies from the photometric cuts subsample at least 10 times more efficiently while maintaining high completeness. The full 5 yr DESI program will expand the LOW- Z sample, densely mapping the low-redshift Universe, providing an unprecedented sample of dwarf galaxies, and providing critical information about how to pursue effective and efficient low-redshift surveys.« less

Abstract We present Symphony, a compilation of 262 cosmological, cold-dark-matter-only zoom-in simulations spanning four decades of host halo mass, from 10 ¹¹ –10 ¹⁵ M _⊙ . This compilation includes three existing simulation suites at the cluster and Milky Way–mass scales, and two new suites: 39 Large Magellanic Cloud-mass (10 ¹¹ M _⊙ ) and 49 strong-lens-analog (10 ¹³ M _⊙ ) group-mass hosts. Across the entire host halo mass range, the highest-resolution regions in these simulations are resolved with a dark matter particle mass of ≈3 × 10 ⁻⁷ times the host virial mass and a Plummer-equivalent gravitational softeningmore » length of ≈9 × 10 ⁻⁴ times the host virial radius, on average. We measure correlations between subhalo abundance and host concentration, formation time, and maximum subhalo mass, all of which peak at the Milky Way host halo mass scale. Subhalo abundances are ≈50% higher in clusters than in lower-mass hosts at fixed sub-to-host halo mass ratios. Subhalo radial distributions are approximately self-similar as a function of host mass and are less concentrated than hosts’ underlying dark matter distributions. We compare our results to the semianalytic model Galacticus , which predicts subhalo mass functions with a higher normalization at the low-mass end and radial distributions that are slightly more concentrated than Symphony. We use UniverseMachine to model halo and subhalo star formation histories in Symphony, and we demonstrate that these predictions resolve the formation histories of the halos that host nearly all currently observable satellite galaxies in the universe. To promote open use of Symphony, data products are publicly available at http://web.stanford.edu/group/gfc/symphony .« less

ABSTRACT We present and validate 20 deg2 of overlapping synthetic imaging surveys representing the full depth of the Nancy Grace Roman Space Telescope High-Latitude Imaging Survey (HLIS) and 5 yr of observations of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). The two synthetic surveys are summarized, with reference to the existing 300 deg2 of LSST simulated imaging produced as part of Dark Energy Science Collaboration (DESC) Data Challenge 2 (DC2). Both synthetic surveys observe the same simulated DESC DC2 universe. For the synthetic Roman survey, we simulate for the first time fully chromatic images along with themore » detailed physics of the Sensor Chip Assemblies derived from lab measurements using the flight detectors. The simulated imaging and resulting pixel-level measurements of photometric properties of objects span a wavelength range of ∼0.3–2.0 μm. We also describe updates to the Roman simulation pipeline, changes in how astrophysical objects are simulated relative to the original DC2 simulations, and the resulting simulated Roman data products. We use these simulations to explore the relative fraction of unrecognized blends in LSST images, finding that 20–30 per cent of objects identified in LSST images with i-band magnitudes brighter than 25 can be identified as multiple objects in Roman images. These simulations provide a unique testing ground for the development and validation of joint pixel-level analysis techniques of ground- and space-based imaging data sets in the second half of the 2020s – in particular the case of joint Roman–LSST analyses.« less

We analyze clustering measurements of BOSS galaxies using a simulation-based emulator of two-point statistics. We focus on the monopole and quadrupole of the redshift-space correlation function, and the projected correlation function, at scales of 0.1 ~ 60 h^-1 Mpc. Although our simulations are based on wCDM with general relativity (GR), we include a scaling parameter of the halo velocity field, γ_f, defined as the amplitude of the halo velocity field relative to the GR prediction. We divide the BOSS data into three redshift bins. After marginalizing over other cosmological parameters, galaxy bias parameters, and the velocity scaling parameter, we findmore » fσ₈(z = 0.25) = 0.413 ± 0.031, fσ₈(z = 0.4) = 0.470 ± 0.026, and fσ₈(z = 0.55) = 0.396 ± 0.022. Compared with Planck observations using a flat Lambda cold dark matter model, our results are lower by 1.9σ, 0.3σ, and 3.4σ, respectively. These results are consistent with other recent simulation-based results at nonlinear scales, including weak lensing measurements of BOSS LOWZ galaxies, two-point clustering of eBOSS LRGs, and an independent clustering analysis of BOSS LOWZ. All these results are generally consistent with a combination of $${\gamma }_{f}^{1/2}{\sigma }_{8}\approx 0.75$$. We note, however, that the BOSS data is well fit assuming GR, i.e., γf = 1. We cannot rule out an unknown systematic error in the galaxy bias model at nonlinear scales, but near-future data and modeling will enhance our understanding of the galaxy–halo connection, and provide a strong test of new physics beyond the standard model.« less

ABSTRACT Studies of cosmology, galaxy evolution, and astronomical transients with current and next-generation wide-field imaging surveys like the Rubin Observatory Legacy Survey of Space and Time are all critically dependent on estimates of photometric redshifts. Capsule networks are a new type of neural network architecture that is better suited for identifying morphological features of the input images than traditional convolutional neural networks. We use a deep capsule network trained on ugriz images, spectroscopic redshifts, and Galaxy Zoo spiral/elliptical classifications of ∼400 000 Sloan Digital Sky Survey galaxies to do photometric redshift estimation. We achieve a photometric redshift prediction accuracy and amore » fraction of catastrophic outliers that are comparable to or better than current methods for SDSS main galaxy sample-like data sets (r ≤ 17.8 and zspec ≤ 0.4) while requiring less data and fewer trainable parameters. Furthermore, the decision-making of our capsule network is much more easily interpretable as capsules act as a low-dimensional encoding of the image. When the capsules are projected on a two-dimensional manifold, they form a single redshift sequence with the fraction of spirals in a region exhibiting a gradient roughly perpendicular to the redshift sequence. We perturb encodings of real galaxy images in this low-dimensional space to create synthetic galaxy images that demonstrate the image properties (e.g. size, orientation, and surface brightness) encoded by each dimension. We also measure correlations between galaxy properties (e.g. magnitudes, colours, and stellar mass) and each capsule dimension. We publicly release our code, estimated redshifts, and additional catalogues at https://biprateep.github.io/encapZulate-1.« less

Absmore » tract Standard halo occupation distribution (HOD) models predict how galaxies occupy halos based on a single property, the virial mass, M _vir . To incorporate galaxy assembly bias, we consider alternative halo mass proxies to use in the HOD. A promising replacement is maximum circular velocity, $V_{\max }$ , which measures the halo’s potential well, and successfully predicts galaxy clustering in subhalo abundance matching models. We fit galaxy clustering measurements from SDSS DR7 with two modified HODs besides the standard M _vir -based model: one where $V_{\max }$ is used as the halo property that determines galaxy occupation, and the other adopting an adjustable combination of M _vir and $V_{\max }$ . Except for the reduced volume M _r  < − 20 threshold sample fit with $V_{\max }$ alone, neither change improves the model performance in predicting the projected two-point correlation function, w _p . We conclude that switching the primary variable to $V_{\max }$ does not significantly improve the HOD model’s ability to fit galaxy clustering.« less

We present observational constraints on the galaxy–halo connection, focusing particularly on galaxy assembly bias from a novel combination of counts-in-cylinders statistics, P(N_CIC), with the standard measurements of the projected two-point correlation function w_p(r_p), and number density n_gal of galaxies. We measure n_gal, w_p(r_p), and P(N_CIC) for volume-limited, luminosity-threshold samples of galaxies selected from SDSS DR7, and use them to constrain halo occupation distribution (HOD) models, including a model in which galaxy occupation depends upon a secondary halo property, namely halo concentration. We detect significant positive central assembly bias for the M_r < -20.0 and M_r <-19.5 samples. Central galaxies preferentiallymore » reside within haloes of high concentration at fixed mass. Positive central assembly bias is also favoured in the M_r < -20.5 and M_r < -19.0 samples. We find no evidence of central assembly bias in the M_r < -21.0 sample. We observe only a marginal preference for negative satellite assembly bias in the M_r < -20.0 and M_r < -19.0 samples, and non-zero satellite assembly bias is not indicated in other samples. Our findings underscore the necessity of accounting for galaxy assembly bias when interpreting galaxy survey data, and demonstrate the potential of count statistics in extracting information from the spatial distribution of galaxies, which could be applied to both galaxy–halo connection studies and cosmological analyses.« less

ABSTRACT Obtaining accurately calibrated redshift distributions of photometric samples is one of the great challenges in photometric surveys like LSST, Euclid, HSC, KiDS, and DES. We present an inference methodology that combines the redshift information from the galaxy photometry with constraints from two-point functions, utilizing cross-correlations with spatially overlapping spectroscopic samples, and illustrate the approach on CosmoDC2 simulations. Our likelihood framework is designed to integrate directly into a typical large-scale structure and weak lensing analysis based on two-point functions. We discuss efficient and accurate inference techniques that allow us to scale the method to the large samples of galaxies tomore » be expected in LSST. We consider statistical challenges like the parametrization of redshift systematics, discuss and evaluate techniques to regularize the sample redshift distributions, and investigate techniques that can help to detect and calibrate sources of systematic error using posterior predictive checks. We evaluate and forecast photometric redshift performance using data from the CosmoDC2 simulations, within which we mimic a DESI-like spectroscopic calibration sample for cross-correlations. Using a combination of spatial cross-correlations and photometry, we show that we can provide calibration of the mean of the sample redshift distribution to an accuracy of at least 0.002(1 + z), consistent with the LSST-Y1 science requirements for weak lensing and large-scale structure probes.« less

We use a recent census of the Milky Way (MW) satellite galaxy population to constrain the lifetime of particle dark matter (DM). We consider two-body decaying dark matter (DDM) in which a heavy DM particle decays with lifetime $$\tau$$ comparable to the age of the Universe to a lighter DM particle (with mass splitting $$\epsilon$$) and to a dark radiation species. These decays impart a characteristic "kick velocity," $$V_{\mathrm{kick}}=\epsilon c$$, on the DM daughter particles, significantly depleting the DM content of low-mass subhalos and making them more susceptible to tidal disruption. We fit the suppression of the present-day DDM subhalomore » mass function (SHMF) as a function of $$\tau$$ and $$V_{\mathrm{kick}}$$ using a suite of high-resolution zoom-in simulations of MW-mass halos, and we validate this model on new DDM simulations of systems specifically chosen to resemble the MW. We implement our DDM SHMF predictions in a forward model that incorporates inhomogeneities in the spatial distribution and detectability of MW satellites and uncertainties in the mapping between galaxies and DM halos, the properties of the MW system, and the disruption of subhalos by the MW disk using an empirical model for the galaxy--halo connection. By comparing to the observed MW satellite population, we conservatively exclude DDM models with $$\tau < 18\ \mathrm{Gyr}$$ ($$29\ \mathrm{Gyr}$$) for $$V_{\mathrm{kick}}=20\ \mathrm{km}\, \mathrm{s}^{-1}$$ ($$40\ \mathrm{km}\, \mathrm{s}^{-1}$$) at $$95\%$$ confidence. These constraints are among the most stringent and robust small-scale structure limits on the DM particle lifetime and strongly disfavor DDM models that have been proposed to alleviate the Hubble and $$S_8$$ tensions.« less