arXiv subjects

Yao-Yuan Mao

Publications and source records attributed to Yao-Yuan Mao.

At least 73 records · Page 4Linked to original sources

arXiv2019

Probing the Fundamental Nature of Dark Matter with the Large Synoptic Survey Telescope

Astrophysical and cosmological observations currently provide the only robust, empirical measurements of dark matter. Future observations with Large Synoptic Survey Telescope (LSST) will provide necessary guidance for the experimental dark matter program. This white paper represents a community effort to summarize the science case for studying the fundamental physics of dark matter with LSST. We discuss how LSST will inform our understanding of the fundamental properties of dark matter, such as particle mass, self-interaction strength, non-gravitational couplings to the Standard Model, and compact object abundances. Additionally, we discuss the ways that LSST will complement other experiments to strengthen our understanding of the fundamental characteristics of dark matter. More information on the LSST dark matter effort can be found at https://lsstdarkmatter.github.io/ .

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arXiv2019

Astrophysical Tests of Dark Matter with Maunakea Spectroscopic Explorer

We discuss how astrophysical observations with the Maunakea Spectroscopic Explorer (MSE), a high-multiplexity (about 4300 fibers), wide field-of-view (1.5 square degree), large telescope aperture (11.25 m) facility, can probe the particle nature of dark matter. MSE will conduct a suite of surveys that will provide critical input for determinations of the mass function, phase-space distribution, and internal density profiles of dark matter halos across all mass scales. N-body and hydrodynamical simulations of cold, warm, fuzzy and self-interacting dark matter suggest that non-trivial dynamics in the dark sector could have left an imprint on structure formation. Analysed within these frameworks, the extensive and unprecedented datasets produced by MSE will be used to search for deviations away from cold and collisionless dark matter model. MSE will provide an improved estimate of the local density of dark matter, critical for direct detection experiments, and will improve estimates of the J-factor for indirect searches through self-annihilation or decay into Standard Model particles. MSE will determine the impact of low mass substructures on the dynamics of Milky Way stellar streams in velocity space, and will allow for estimates of the density profiles of the dark matter halos of Milky Way dwarf galaxies using more than an order of magnitude more tracers. In the low redshift Universe, MSE will provide critical redshifts to pin down the luminosity functions of vast numbers of satellite systems, and MSE will be an essential component of future strong lensing measurements to constrain the halo mass function. Across nearly all mass scales, the improvements offered by MSE, in comparison to other facilities, are such that the relevant analyses are limited by systematics rather than statistics.

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arXiv2019

Astro2020: Empirically Constraining Galaxy Evolution

Over the past decade, empirical constraints on the galaxy-dark matter halo connection have significantly advanced our understanding of galaxy evolution. Past techniques have focused on connections between halo properties and galaxy stellar mass and/or star formation rates. Empirical techniques in the next decade will link halo assembly histories with galaxies' circumgalactic media, supermassive black holes, morphologies, kinematics, sizes, colors, metallicities, and transient rates. Uncovering these links will resolve many critical uncertainties in galaxy formation and will enable much higher-fidelity mock catalogs essential for interpreting observations. Achieving these results will require broader and deeper spectroscopic coverage of galaxies and their circumgalactic media; survey teams will also need to meet several criteria (cross-comparisons, public access, and covariance matrices) to facilitate combining data across different surveys. Acting on these recommendations will continue enabling dramatic progress in both empirical modeling and galaxy evolution for the next decade.

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arXiv2019

Modeling the Connection Between Subhalos and Satellites in Milky Way-like Systems

We develop a comprehensive and flexible model for the connection between satellite galaxies and dark matter subhalos in dark matter-only zoom-in simulations of Milky Way (MW)--mass host halos. We systematically identify the physical and numerical uncertainties in the galaxy--halo connection and simulations underlying our method, including (i) the influence of host halo properties; (ii) the relationship between satellite luminosities and subhalo properties, including the effects of reionization; (iii) the relationship between satellite and subhalo locations; (iv) the relationship between satellite sizes and subhalo properties, including the effects of tidal stripping; (v) satellite and subhalo disruption due to baryonic effects; and (vi) artificial subhalo disruption and orphan satellites. To illustrate our approach, we fit this model to the luminosity distribution of both classical MW satellites and those discovered in the Sloan Digital Sky Survey by performing realistic mock observations that depend on the luminosity, size, and distance of our predicted satellites, and we infer the total satellite population that will be probed by upcoming surveys. We argue that galaxy size and surface brightness modeling will play a key role in interpreting current and future observations, as the expected number of observable satellites depends sensitively on their surface brightness distribution.

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arXiv2018

Beyond Assembly Bias: Exploring Secondary Halo Biases for Cluster-size Haloes

Secondary halo bias, commonly known as 'assembly bias,' is the dependence of halo clustering on a halo property other than mass. This prediction of the Lambda-Cold Dark Matter cosmology is essential to modelling the galaxy distribution to high precision and interpreting clustering measurements. As the name suggests, different manifestations of secondary halo bias have been thought to originate from halo assembly histories. We show conclusively that this is incorrect for cluster-size haloes. We present an up-to-date summary of secondary halo biases of high-mass haloes due to various halo properties including concentration, spin, several proxies of assembly history, and subhalo properties. While concentration, spin, and the abundance and radial distribution of subhaloes exhibit significant secondary biases, properties that directly quantify halo assembly history do not. In fact, the entire assembly histories of haloes in pairs are nearly identical to those of isolated haloes. In general, a global correlation between two halo properties does not predict whether or not these two properties exhibit similar secondary biases. For example, assembly history and concentration (or subhalo abundance) are correlated for both paired and isolated haloes, but follow slightly different conditional distributions in these two cases. This results in a secondary halo bias due to concentration (or subhalo abundance), despite the lack of assembly bias in the strict sense for cluster-size haloes. Due to this complexity, caution must be exercised in using any one halo property as a proxy to study the secondary bias due to another property.

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arXiv2018

Modeling the Impact of Baryons on Subhalo Populations with Machine Learning

We identify subhalos in dark matter-only (DMO) zoom-in simulations that are likely to be disrupted due to baryonic effects by using a random forest classifier trained on two hydrodynamic simulations of Milky Way (MW)-mass host halos from the Latte suite of the Feedback in Realistic Environments (FIRE) project. We train our classifier using five properties of each disrupted and surviving subhalo: pericentric distance and scale factor at first pericentric passage after accretion, and scale factor, virial mass, and maximum circular velocity at accretion. Our five-property classifier identifies disrupted subhalos in the FIRE simulations with an $85\%$ out-of-bag classification score. We predict surviving subhalo populations in DMO simulations of the FIRE host halos, finding excellent agreement with the hydrodynamic results; in particular, our classifier outperforms DMO zoom-in simulations that include the gravitational potential of the central galactic disk in each hydrodynamic simulation, indicating that it captures both the dynamical effects of a central disk and additional baryonic physics. We also predict surviving subhalo populations for a suite of DMO zoom-in simulations of MW-mass host halos, finding that baryons impact each system consistently and that the predicted amount of subhalo disruption is larger than the host-to-host scatter among the subhalo populations. Although the small size and specific baryonic physics prescription of our training set limits the generality of our results, our work suggests that machine-learning classification algorithms trained on hydrodynamic zoom-in simulations can efficiently predict realistic subhalo populations.

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arXiv2018

The Aemulus Project III: Emulation of the Galaxy Correlation Function

Using the N-body simulations of the AEMULUS Project, we construct an emulator for the non-linear clustering of galaxies in real and redshift space. We construct our model of galaxy bias using the halo occupation framework, accounting for possible velocity bias. The model includes 15 parameters, including both cosmological and galaxy bias parameters. We demonstrate that our emulator achieves ~ 1% precision at the scales of interest, 0.1<r<10 h^{-1} Mpc, and recovers the true cosmology when tested against independent simulations. Our primary parameters of interest are related to the growth rate of structure, f, and its degenerate combination fsigma_8. Using this emulator, we show that the constraining power on these parameters monotonically increases as smaller scales are included in the analysis, all the way down to 0.1 h^{-1} Mpc. For a BOSS-like survey, the constraints on fsigma_8 from r<30 h^{-1} Mpc scales alone are more than a factor of two tighter than those from the fiducial BOSS analysis of redshift-space clustering using perturbation theory at larger scales. The combination of real- and redshift-space clustering allows us to break the degeneracy between f and sigma_8, yielding a 9% constraint on f alone for a BOSS-like analysis. The current AEMULUS simulations limit this model to surveys of massive galaxies. Future simulations will allow this framework to be extended to all galaxy target types, including emission-line galaxies.

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arXiv2018

The Aemulus Project I: Numerical Simulations for Precision Cosmology

The rapidly growing statistical precision of galaxy surveys has lead to a need for ever-more precise predictions of the observables used to constrain cosmological and galaxy formation models. The primary avenue through which such predictions will be obtained is suites of numerical simulations. These simulations must span the relevant model parameter spaces, be large enough to obtain the precision demanded by upcoming data, and be thoroughly validated in order to ensure accuracy. In this paper we present one such suite of simulations, forming the basis for the AEMULUS Project, a collaboration devoted to precision emulation of galaxy survey observables. We have run a set of 75 (1.05 h^-1 Gpc)^3 simulations with mass resolution and force softening of 3.51\times 10^10 (Omega_m / 0.3) ~ h^-1 M_sun and 20 ~ h^-1 kpc respectively in 47 different wCDM cosmologies spanning the range of parameter space allowed by the combination of recent Cosmic Microwave Background, Baryon Acoustic Oscillation and Type Ia Supernovae results. We present convergence tests of several observables including spherical overdensity halo mass functions, galaxy projected correlation functions, galaxy clustering in redshift space, and matter and halo correlation functions and power spectra. We show that these statistics are converged to 1% (2%) for halos with more than 500 (200) particles respectively and scales of r>200 ~ h^-1 kpc in real space or k ~ 3 h Mpc^-1 in harmonic space for z\le 1. We find that the dominant source of uncertainty comes from varying the particle loading of the simulations. This leads to large systematic errors for statistics using halos with fewer than 200 particles and scales smaller than k ~ 4 h^-1 Mpc. We provide the halo catalogs and snapshots detailed in this work to the community at https://AemulusProject.github.io.

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arXiv2018

DESCQA: An Automated Validation Framework for Synthetic Sky Catalogs

The use of high-quality simulated sky catalogs is essential for the success of cosmological surveys. The catalogs have diverse applications, such as investigating signatures of fundamental physics in cosmological observables, understanding the effect of systematic uncertainties on measured signals and testing mitigation strategies for reducing these uncertainties, aiding analysis pipeline development and testing, and survey strategy optimization. The list of applications is growing with improvements in the quality of the catalogs and the details that they can provide. Given the importance of simulated catalogs, it is critical to provide rigorous validation protocols that enable both catalog providers and users to assess the quality of the catalogs in a straightforward and comprehensive way. For this purpose, we have developed the DESCQA framework for the Large Synoptic Survey Telescope Dark Energy Science Collaboration as well as for the broader community. The goal of DESCQA is to enable the inspection, validation, and comparison of an inhomogeneous set of synthetic catalogs via the provision of a common interface within an automated framework. In this paper, we present the design concept and first implementation of DESCQA. In order to establish and demonstrate its full functionality we use a set of interim catalogs and validation tests. We highlight several important aspects, both technical and scientific, that require thoughtful consideration when designing a validation framework, including validation metrics and how these metrics impose requirements on the synthetic sky catalogs.

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arXiv2017

Brightest galaxies as halo centre tracers in SDSS DR7

Determining the positions of halo centres in large-scale structure surveys is crucial for many cosmological studies. A common assumption is that halo centres correspond to the location of their brightest member galaxies. In this paper, we study the dynamics of brightest galaxies with respect to other halo members in the Sloan Digital Sky Survey DR7. Specifically, we look at the line-of-sight velocity and spatial offsets between brightest galaxies and their neighbours. We compare those to detailed mock catalogues, constructed from high-resolution, dark-matter-only $N$-body simulations, in which it is assumed that satellite galaxies trace dark matter subhaloes. This allows us to place constraints on the fraction $f_{\rm BNC}$ of haloes in which the brightest galaxy is not the central. Compared to previous studies we explicitly take into account the unrelaxed state of the host haloes, velocity offsets of halo cores and correlations between $f_{\rm BNC}$ and the satellite occupation. We find that $f_{\rm BNC}$ strongly decreases with the luminosity of the brightest galaxy and increases with the mass of the host halo. Overall, in the halo mass range $10^{13} - 10^{14.5} h^{-1} M_\odot$ we find $f_{\rm BNC} \sim 30\%$, in good agreement with a previous study by Skibba et al. We discuss the implications of these findings for studies inferring the galaxy--halo connection from satellite kinematics, models of the conditional luminosity function and galaxy formation in general.

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arXiv2017

Forward Modeling of Large-Scale Structure: An open-source approach with Halotools

We present the first stable release of Halotools (v0.2), a community-driven Python package designed to build and test models of the galaxy-halo connection. Halotools provides a modular platform for creating mock universes of galaxies starting from a catalog of dark matter halos obtained from a cosmological simulation. The package supports many of the common forms used to describe galaxy-halo models: the halo occupation distribution (HOD), the conditional luminosity function (CLF), abundance matching, and alternatives to these models that include effects such as environmental quenching or variable galaxy assembly bias. Satellite galaxies can be modeled to live in subhalos, or to follow custom number density profiles within their halos, including spatial and/or velocity bias with respect to the dark matter profile. The package has an optimized toolkit to make mock observations on a synthetic galaxy population, including galaxy clustering, galaxy-galaxy lensing, galaxy group identification, RSD multipoles, void statistics, pairwise velocities and others, allowing direct comparison to observations. Halotools is object-oriented, enabling complex models to be built from a set of simple, interchangeable components, including those of your own creation. Halotools has an automated testing suite and is exhaustively documented on http://halotools.readthedocs.io, which includes quickstart guides, source code notes and a large collection of tutorials. The documentation is effectively an online textbook on how to build and study empirical models of galaxy formation with Python.

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arXiv2017

The Immitigable Nature of Assembly Bias: The Impact of Halo Definition on Assembly Bias

Dark matter halo clustering depends not only on halo mass, but also on other properties such as concentration and shape. This phenomenon is known broadly as assembly bias. We explore the dependence of assembly bias on halo definition, parametrized by spherical overdensity parameter, $Δ$. We summarize the strength of concentration-, shape-, and spin-dependent halo clustering as a function of halo mass and halo definition. Concentration-dependent clustering depends strongly on mass at all $Δ$. For conventional halo definitions ($Δ\sim 200\mathrm{m}-600\mathrm{m}$), concentration-dependent clustering at low mass is driven by a population of haloes that is altered through interactions with neighbouring haloes. Concentration-dependent clustering can be greatly reduced through a mass-dependent halo definition with $Δ\sim 20\mathrm{m}-40\mathrm{m}$ for haloes with $M_{200\mathrm{m}} \lesssim 10^{12}\, h^{-1}\mathrm{M}_{\odot}$. Smaller $Δ$ implies larger radii and mitigates assembly bias at low mass by subsuming altered, so-called backsplash haloes into now larger host haloes. At higher masses ($M_{200\mathrm{m}} \gtrsim 10^{13}\, h^{-1}\mathrm{M}_{\odot}$) larger overdensities, $Δ\gtrsim 600\mathrm{m}$, are necessary. Shape- and spin-dependent clustering are significant for all halo definitions that we explore and exhibit a relatively weaker mass dependence. Generally, both the strength and the sense of assembly bias depend on halo definition, varying significantly even among common definitions. We identify no halo definition that mitigates all manifestations of assembly bias. A halo definition that mitigates assembly bias based on one halo property (e.g., concentration) must be mass dependent. The halo definitions that best mitigate concentration-dependent halo clustering do not coincide with the expected average splashback radii at fixed halo mass.

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arXiv2017

The SAGA Survey: I. Satellite Galaxy Populations Around Eight Milky Way Analogs

We present the survey strategy and early results of the "Satellites Around Galactic Analogs" (SAGA) Survey. The SAGA Survey's goal is to measure the distribution of satellite galaxies around 100 systems analogous to the Milky Way down to the luminosity of the Leo I dwarf galaxy ($ M_r < -12.3 $). We define a Milky Way analog based on $K$-band luminosity and local environment. Here, we present satellite luminosity functions for 8 Milky Way analog galaxies between 20 to 40 Mpc. These systems have nearly complete spectroscopic coverage of candidate satellites within the projected host virial radius down to $ r_o < 20.75 $ using low redshift $gri$ color criteria. We have discovered a total of 25 new satellite galaxies: 14 new satellite galaxies meet our formal criteria around our complete host systems, plus 11 additional satellites in either incompletely surveyed hosts or below our formal magnitude limit. Combined with 13 previously known satellites, there are a total of 27 satellites around 8 complete Milky Way analog hosts. We find a wide distribution in the number of satellites per host, from 1 to 9, in the luminosity range for which there are five Milky Way satellites. Standard abundance matching extrapolated from higher luminosities predicts less scatter between hosts and a steeper luminosity function slope than observed. We find that the majority of satellites (26 of 27) are star-forming. These early results indicate that the Milky Way has a different satellite population than typical in our sample, potentially changing the physical interpretation of measurements based only on the Milky Way's satellite galaxies.

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arXiv2017

The importance of preventive feedback: inference from observations of the stellar masses and metallicities of Milky Way dwarf galaxies

Dwarf galaxies are known to have remarkably low star formation efficiency due to strong feedback. Adopting the dwarf galaxies of the Milky Way as a laboratory, we explore a flexible semi-analytic galaxy formation model to understand how the feedback processes shape the satellite galaxies of the Milky Way. Using Markov-Chain Monte-Carlo, we exhaustively search a large parameter space of the model and rigorously show that the general wisdom of strong outflows as the primary feedback mechanism cannot simultaneously explain the stellar mass function and the mass--metallicity relation of the Milky Way satellites. An extended model that assumes that a fraction of baryons is prevented from collapsing into low-mass halos in the first place can be accurately constrained to simultaneously reproduce those observations. The inference suggests that two different physical mechanisms are needed to explain the two different data sets. In particular, moderate outflows with weak halo mass dependence are needed to explain the mass--metallicity relation, and prevention of baryons falling into shallow gravitational potentials of low-mass halos (e.g. "pre-heating") is needed to explain the low stellar mass fraction for a given subhalo mass.

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arXiv2017

On the galaxy-halo connection in the EAGLE simulation

Empirical models of galaxy formation require assumptions about the correlations between galaxy and halo properties. These may be calibrated against observations or inferred from physical models such as hydrodynamical simulations. In this Letter, we use the EAGLE simulation to investigate the correlation of galaxy size with halo properties. We motivate this analysis by noting that the common assumption of angular momentum partition between baryons and dark matter in rotationally supported galaxies overpredicts both the spread in the stellar mass-size relation and the anticorrelation of size and velocity residuals, indicating a problem with the galaxy-halo connection it implies. We find the EAGLE galaxy population to perform significantly better on both statistics, and trace this success to the weakness of the correlations of galaxy size with halo mass, concentration and spin at fixed stellar mass. Using these correlations in empirical models will enable fine-grained aspects of galaxy scalings to be matched.

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arXiv2017

Halo Histories vs. Galaxy Properties at z=0, III: The Properties of Star-Forming Galaxies

We measure how the properties of star-forming central galaxies correlate with large-scale environment, $δ$, measured on $10$Mpc/h scales. We use group catalogs to isolate a robust sample of central galaxies with high purity and completeness. The properties we investigate are star formation rate (SFR), exponential disk scale length $R_{\rm exp}$, and Sersic index of the light profile, $n$. We find that, at all stellar masses, there is an inverse correlation between SFR and $δ$, meaning that above-average star forming centrals live in underdense regions. For $n$ and $R_{\rm exp}$, there is no correlation with $δ$ at $M_{\rm star}\lesssim 10^{10.5}$ $M_\odot$, but at higher masses there are positive correlations; a weak correlation with $R_{\rm exp}$ and a strong correlation with $n$. These data are evidence of assembly bias within the star-forming population. The results for SFR are consistent with a model in which SFR correlates with present-day halo accretion rate, $\dot{M}_h$. In this model, galaxies are assigned to halos using the abundance matching ansatz, which maps galaxy stellar mass onto halo mass. At fixed halo mass, SFR is assigned to galaxies using the same approach,but $\dot{M}_h$ is used to map onto SFR. The best-fit model requires some scatter in the $\dot{M}_h$-SFR relation. The $R_{\rm exp}$ and $n$ measurements are consistent with a model in which these quantities are correlated with the spin parameter of the halo, $λ$. Halo spin does not correlate with $δ$ at low halo masses, but for higher mass halos, high-spin halos live in higher density environments at fixed $M_h$. Put together with the earlier installments of this series, these data demonstrate that quenching processes have limited correlation with halo formation history, but the growth of active galaxies, as well as other detailed properties, are influenced by the details of halo assembly.

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arXiv2017

The Galaxy Clustering Crisis in Abundance Matching

Galaxy clustering on small scales is significantly under-predicted by sub-halo abundance matching (SHAM) models that populate (sub-)haloes with galaxies based on peak halo mass, $M_{\rm peak}$. SHAM models based on the peak maximum circular velocity, $V_{\rm peak}$, have had much better success. The primary reason $M_{\rm peak}$ based models fail is the relatively low abundance of satellite galaxies produced in these models compared to those based on $V_{\rm peak}$. Despite success in predicting clustering, a simple $V_{\rm peak}$ based SHAM model results in predictions for galaxy growth that are at odds with observations. We evaluate three possible remedies that could "save" mass-based SHAM: (1) SHAM models require a significant population of "orphan" galaxies as a result of artificial disruption/merging of sub-haloes in modern high resolution dark matter simulations; (2) satellites must grow significantly after their accretion; and (3) stellar mass is significantly affected by halo assembly history. No solution is entirely satisfactory. However, regardless of the particulars, we show that popular SHAM models based on $M_{\rm peak}$ cannot be complete physical models as presented. Either $V_{\rm peak}$ truly is a better predictor of stellar mass at $z\sim 0$ and it remains to be seen how the correlation between stellar mass and $V_{\rm peak}$ comes about, or SHAM models are missing vital component(s) that significantly affect galaxy clustering.

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arXiv2017

The Radial Acceleration Relation in Disk Galaxies in the MassiveBlack-II Simulation

A strong correlation has been measured between the observed centripetal accelerations in galaxies and the accelerations implied by the baryonic components of galaxies. This empirical radial acceleration relation must be accounted for in any viable model of galaxy formation. We measure and compare the radial accelerations contributed by baryons and by dark matter in disk galaxies in the MassiveBlack-II hydrodynamic galaxy formation simulation. The sample of 1594 galaxies spans three orders of magnitude in luminosity and four in surface brightness, comparable to the observed sample from the Spitzer Photometry & Accurate Rotation Curves (SPARC) dataset used by McGaugh et al. (2016). We find that radial accelerations contributed by baryonic matter only and by total matter are highly correlated, with only small scatter around their mean or median relation, despite the wide ranges of galaxy luminosity and surface brightness. We further find that the radial acceleration relation in this simulation differs from that of the SPARC sample, and can be described by a simple power law in the acceleration range we are probing.

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