arXiv subjects

Andrew P. Hearin

Publications and source records attributed to Andrew P. Hearin.

At least 19 recordsLinked to original sources

arXiv2026

Diffhalos: A Generative Model of Cosmological Lightcones of Dark Matter Halos

We present a generative model of cosmological lightcones of dark matter halos, Diffhalos. In our model, we draw Monte Carlo samples of the halo mass function in a lightcone with a JAX-based implementation of the halo model, Halox, and we generate samples of subhalos by drawing from a model for the conditional subhalo mass function. We generate mass assembly histories (MAHs) using a normalizing flow trained on merger trees in cosmological N-body simulations. We show that Diffhalos can generate samples of halos, subhalos, and their MAHs with a statistical distribution that accurately approximates populations in simulated lightcones. As an example application, we use Diffhalos to calculate gradients of the halo and subhalo mass functions with respect to cosmological parameters. We conclude with a discussion of ongoing work using Diffhalos together with models of the galaxy--halo connection to make theoretical predictions for cosmological populations of galaxies, and to generate mock galaxy catalogs.

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arXiv2026

DiffstarPop: A generative physical model of galaxy star formation history

We present DiffstarPop, a differentiable forward model of cosmological populations of galaxy star formation histories (SFH). In the model, individual galaxy SFH is parametrized by Diffstar, which has parameters $θ_{\rm SFH}$ that have a direct interpretation in terms of galaxy formation physics, such as star formation efficiency and quenching. DiffstarPop is a model for the statistical connection between $θ_{\rm SFH}$ and the mass assembly history (MAH) of dark matter halos. We have formulated DiffstarPop to have the minimal flexibility needed to accurately reproduce the statistical distributions of galaxy SFH predicted by a diverse range of simulations, including the IllustrisTNG hydrodynamical simulation, the Galacticus semi-analytic model, and the UniverseMachine semi-empirical model. Our publicly available code written in JAX includes Monte Carlo generators that supply statistical samples of galaxy assembly histories that mimic the populations seen in each simulation, and can generate SFHs for $10^6$ galaxies in 1.1 CPU-seconds, or 0.03 GPU-seconds. We conclude the paper with a discussion of applications of DiffstarPop, which we are using to generate catalogs of synthetic galaxies populating the merger trees in cosmological N-body simulations.

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arXiv2025

DESC DC2 Data Release Note

In preparation for cosmological analyses of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), the LSST Dark Energy Science Collaboration (LSST DESC) has created a 300 deg$^2$ simulated survey as part of an effort called Data Challenge 2 (DC2). The DC2 simulated sky survey, in six optical bands with observations following a reference LSST observing cadence, was processed with the LSST Science Pipelines (19.0.0). In this Note, we describe the public data release of the resulting object catalogs for the coadded images of five years of simulated observations along with associated truth catalogs. We include a brief description of the major features of the available data sets. To enable convenient access to the data products, we have developed a web portal connected to Globus data services. We describe how to access the data and provide example Jupyter Notebooks in Python to aid first interactions with the data. We welcome feedback and questions about the data release via a GitHub repository.

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arXiv2025

Illuminating the Physics of Dark Energy with the Discovery Simulations

In this paper, we present the Discovery simulations: a new pair of high-resolution N-body simulations motivated by the DESI Y1 BAO cosmological constraints on dark energy. The Discovery simulations were run with identical initial conditions, and differ only in their cosmological parameters. The first simulation is based on a flat $Λ\mathrm{CDM}$ cosmology, while the second is based on a $w_0 w_a\mathrm{CDM}$ cosmology, with particular parameter values chosen based on the DESI analysis which includes constraints from BAO with CMB priors. Both simulations evolve $6720^3$ particles in a box with a side length of $L_\mathrm{box} = 1.5$ Gpc, leading to a mass resolution of $\sim4 \times 10^8$ $\mathrm{M}_{\odot}$ in each simulation. In this work we demonstrate the impact of the $w_0 w_a\mathrm{CDM}$ cosmology on the matter power spectrum, halo mass function, and halo mass accretion rate. We also populate halos with galaxies using a novel forward model for in-situ star formation, and examine the way in which changes to cosmology manifest as changes in star formation history. The Discovery simulations provide a testbed for alternative cosmological probes that may offer additional constraining power beyond BAO, such as higher-order summary statistics and observables in the nonlinear regime. Halo catalogs from the Discovery simulations are publicly available and can be downloaded from the HACC Simulation Data Portal.

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arXiv2023

A Differentiable Model of the Evolution of Dark Matter Halo Concentration

We introduce a new model of the evolution of the concentration of dark matter halos, c(t). For individual halos, our model approximates c(t) as a power law with a time-dependent index, such that at early times, concentration has a nearly constant value of c=3-4, and as cosmic time progresses, c(t) smoothly increases. Using large samples of halo merger trees taken from the Bolshoi-P and MDPL2 cosmological simulations, we demonstrate that our 3-parameter model can approximate the evolution of the concentration of individual halos with a typical accuracy of 0.1 dex for t>2 Gyr for all Bolshoi-P and MDPL2 halos of present-day mass greater than 10^11.5 Msun. We additionally present a new model of the evolution of the concentration of halo populations, which we show faithfully reproduces both average concentration growth, as well as the diversity of smooth trajectories of c(t), including capturing correlations with halo mass and halo assembly history. Our publicly available source code, Diffprof, can be used to generate Monte Carlo realizations of the concentration histories of cosmologically representative halo populations; Diffprof is differentiable due to its implementation in the JAX autodiff library, which facilitates the incorporation of our model into existing analytical halo model frameworks.

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arXiv2023

Diffstar: A Fully Parametric Physical Model for Galaxy Assembly History

We present Diffstar, a smooth parametric model for the in-situ star formation history (SFH) of galaxies. Diffstar is distinct from conventional SFH models that are used to interpret the spectral energy distribution (SED) of an observed galaxy, because our model is parametrized directly in terms of basic features of galaxy formation physics. The Diffstar model assumes that star formation is fueled by the accretion of gas into the dark matter halo of the galaxy, and at the foundation of Diffstar is a parametric model for halo mass assembly, Diffmah. We include parametrized ingredients for the fraction of accreted gas that is eventually transformed into stars, $ε_{\rm ms},$ and for the timescale over which this transformation occurs, $τ_{\rm cons};$ some galaxies in Diffstar experience a quenching event at time $t_{\rm q},$ and may subsequently experience rejuvenated star formation. We fit the SFHs of galaxies predicted by the IllustrisTNG (TNG) and UniverseMachine (UM) simulations with the Diffstar parameterization, and show that our model is sufficiently flexible to describe the average stellar mass histories of galaxies in both simulations with an accuracy of $\sim0.1$ dex across most of cosmic time. We use Diffstar to compare TNG to UM in common physical terms, finding that: (i) star formation in UM is less efficient and burstier relative to TNG; (ii) galaxies in UM have longer gas consumption timescales, $τ_{\rm cons}$, relative to TNG; (iii) rejuvenated star formation is ubiquitous in UM, whereas quenched TNG galaxies rarely experience sustained rejuvenation; and (iv) in both simulations, the distributions of $ε_{\rm ms}$, $τ_{\rm cons}$, and $t_{\rm q}$ share a common characteristic dependence upon halo mass, and present significant correlations with halo assembly history. [Abridged]

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arXiv2023

DSPS: Differentiable Stellar Population Synthesis

Models of stellar population synthesis (SPS) are the fundamental tool that relates the physical properties of a galaxy to its spectral energy distribution (SED). In this paper, we present DSPS: a python package for stellar population synthesis. All of the functionality in DSPS is implemented natively in the JAX library for automatic differentiation, and so our predictions for galaxy photometry are fully differentiable, and directly inherit the performance benefits of JAX, including portability onto GPUs. DSPS also implements several novel features, such as i) a flexible empirical model for stellar metallicity that incorporates correlations with stellar age, and ii) support for the diffstar model that provides a physically-motivated connection between the star formation history of a galaxy (SFH) and the mass assembly of its underlying dark matter halo. We detail a set of theoretical techniques for using autodiff to calculate gradients of predictions for galaxy SEDs with respect to SPS parameters that control a range of physical effects, including SFH, stellar metallicity, nebular emission, and dust attenuation. When forward modeling the colors of a synthetic galaxy population, we find that DSPS can provide a factor of 5 speedup over standard SPS codes on a CPU, and a factor of 300-400 on a modern GPU. When coupled with gradient-based techniques for optimization and inference, DSPS makes it practical to conduct expansive likelihood analyses of simulation-based models of the galaxy--halo connection that fully forward model galaxy spectra and photometry.

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arXiv2023

Constraints on $S_8$ from a full-scale and full-shape analysis of redshift-space clustering and galaxy-galaxy lensing in BOSS

We present a novel simulation-based cosmological analysis of galaxy-galaxy lensing and galaxy redshift-space clustering. Compared to analysis methods based on perturbation theory, our simulation-based approach allows us to probe a much wider range of scales, $0.4 \, h^{-1} \, \mathrm{Mpc}$ to $63 \, h^{-1} \, \mathrm{Mpc}$, including highly non-linear scales, and marginalises over astrophysical effects such as assembly bias. We apply this framework to data from the Baryon Oscillation Spectroscopic Survey LOWZ sample cross-correlated with state-of-the-art gravitational lensing catalogues from the Kilo Degree Survey and the Dark Energy Survey. We show that gravitational lensing and redshift-space clustering when analysed over a large range of scales place tight constraints on the growth-of-structure parameter $S_8 = σ_8 \sqrt{Ω_{\rm m} / 0.3}$. Overall, we infer $S_8 = 0.792 \pm 0.022$ when analysing the combination of galaxy-galaxy lensing and projected galaxy clustering and $S_8 = 0.771 \pm 0.027$ for galaxy redshift-space clustering. These findings highlight the potential constraining power of full-scale studies over studies analysing only large scales, and also showcase the benefits of analysing multiple large-scale structure surveys jointly. Our inferred values for $S_8$ fall below the value inferred from the CMB, $S_8 = 0.834 \pm 0.016$. While this difference is not statistically significant by itself, our results mirror other findings in the literature whereby low-redshift large scale structure probes infer lower values for $S_8$ than the CMB, the so-called $S_8$-tension.

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arXiv2022

A Differentiable Model of the Assembly of Individual and Populations of Dark Matter Halos

We present a new empirical model for the mass assembly of dark matter halos. We approximate the growth of individual halos as a simple power-law function of time, where the power-law index smoothly decreases as the halo transitions from the fast-accretion regime at early times, to the slow-accretion regime at late times. Using large samples of halo merger trees taken from high-resolution cosmological simulations, we demonstrate that our 3-parameter model, Diffmah, can approximate halo growth with a typical accuracy of 0.1 dex for t > 1 Gyr for all halos of present-day mass greater than 10^11Msun, including subhalos and host halos in gravity-only simulations, as well as in the TNG hydrodynamical simulation. We additionally present a new model for the assembly of halo populations, DiffmahPop, which not only reproduces average mass growth across time, but also faithfully captures the diversity with which halos assemble their mass. Our python implementation is based on the autodiff library JAX, and so our model self-consistently captures the mean and variance of halo mass accretion rate across cosmic time. We show that the connection between halo assembly and the large-scale density field, known as halo assembly bias, is accurately captured by Diffmah, and that residual errors in our approximations to halo assembly history exhibit a negligible residual correlation with the density field. Our publicly available source code can be used to generate Monte Carlo realizations of cosmologically representative halo histories; our differentiable implementation facilitates the incorporation of our model into existing analytical halo model frameworks.

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arXiv2022

Differentiable Predictions for Large Scale Structure with SHAMNet

In simulation-based models of the galaxy-halo connection, theoretical predictions for galaxy clustering and lensing are typically made based on Monte Carlo realizations of a mock universe. In this paper, we use Subhalo Abundance Matching (SHAM) as a toy model to introduce an alternative to stochastic predictions based on mock population, demonstrating how to make simulation-based predictions for clustering and lensing that are both exact and differentiable with respect to the parameters of the model. Conventional implementations of SHAM are based on iterative algorithms such as Richardson-Lucy deconvolution; here we use the JAX library for automatic differentiation to train SHAMNet, a neural network that accurately approximates the stellar-to-halo mass relation (SMHM) defined by abundance matching. In our approach to making differentiable predictions for large scale structure, we map parameterized PDFs onto each simulated halo, and calculate gradients of summary statistics of the galaxy distribution by using autodiff to propagate the gradients of the SMHM through the statistical estimators used to measure one- and two-point functions. Our techniques are quite general, and we conclude with an overview of how they can be applied in tandem with more complex, higher-dimensional models, creating the capability to make differentiable predictions for the multi-wavelength universe of galaxies.

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arXiv2021

Main Sequence Scatter is Real: The Joint Dependence of Galaxy Clustering on Star Formation and Stellar Mass

We present new measurements of the clustering of stellar mass-complete samples of $\sim40,000$ SDSS galaxies at $z\sim0.03$ as a joint function of stellar mass and specific star formation rate (sSFR). Our results confirm what Coil et al. (2017) find at $z\sim0.7$: galaxy clustering is a stronger function of sSFR at fixed stellar mass than of stellar mass at fixed sSFR. We also find that galaxies above the star-forming main sequence (SFMS) with higher sSFR are less clustered than galaxies below the SFMS with lower sSFR, at a given stellar mass. A similar trend is present for quiescent galaxies. This confirms that main sequence scatter, and scatter within the quiescent sequence, is physically connected to the large-scale cosmic density field. We compare the resulting galaxy bias versus sSFR, and relative bias versus sSFR ratio, for different galaxy samples across ${0<z<1.2}$ to mock galaxy catalogs based on the empirical galaxy evolution model of Behroozi et al. (2019). This model fits PRIMUS and DEEP2 clustering data well at intermediate redshift, but agreement with SDSS is not as strong. We show that increasing the correlation between galaxy SFR and halo accretion rate at $z\sim0$ in the model substantially improves agreement with SDSS data. Mock catalogs suggest that central galaxies contribute substantially to the dependence of clustering on sSFR at a given stellar mass and that the signal is not simply an effect of satellite galaxy fraction differences with sSFR. Our results are highly constraining for galaxy evolution models and show that the stellar-to-halo mass relation (SHMR) depends on sSFR.

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arXiv2021

Five-percent measurements of the growth rate from simulation-based modelling of redshift-space clustering in BOSS LOWZ

We use a simulation-based modelling approach to analyse the anisotropic clustering of the BOSS LOWZ sample over the radial range $0.4 \, h^{-1} \, \mathrm{Mpc}$ to $63 \, h^{-1} \, \mathrm{Mpc}$, significantly extending what is possible with a purely analytic modelling framework. Our full-scale analysis yields constraints on the growth of structure that are a factor of two more stringent than any other study on large scales at similar redshifts. We infer $f σ_8 = 0.471 \pm 0.024$ at $z \approx 0.25$, and $f σ_8 = 0.431 \pm 0.025$ at $z \approx 0.40$; the corresponding $Λ$CDM predictions of the Planck CMB analysis are $0.470 \pm 0.006$ and $0.476 \pm 0.005$, respectively. Our results are thus consistent with Planck, but also follow the trend seen in previous low-redshift measurements of $f σ_8$ falling slightly below the $Λ$CDM+CMB prediction. We find that small and large radial scales yield mutually consistent values of $f σ_8$, but there are $1-2.5 σ$ hints of small scales ($< 10 \, h^{-1} \, \mathrm{Mpc}$) preferring lower values for $f σ_8$ relative to larger scales. We analyse the constraining power of the full range of radial scales, finding that most of the multipole information about $fσ_8$ is contained in the scales $2 \, h^{-1} \, \mathrm{Mpc} \lesssim s \lesssim 20 \, h^{-1} \, \mathrm{Mpc}$. Evidently, once the cosmological information of the quasi-to-nonlinear regime has been harvested, large-scale modes contain only modest additional information about structure growth. Finally, we compare predictions for the galaxy-galaxy lensing amplitude of the two samples against measurements from SDSS and assess the lensing-is-low effect in light of our findings.

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arXiv2021

The LSST DESC DC2 Simulated Sky Survey

We describe the simulated sky survey underlying the second data challenge (DC2) carried out in preparation for analysis of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) by the LSST Dark Energy Science Collaboration (LSST DESC). Significant connections across multiple science domains will be a hallmark of LSST; the DC2 program represents a unique modeling effort that stresses this interconnectivity in a way that has not been attempted before. This effort encompasses a full end-to-end approach: starting from a large N-body simulation, through setting up LSST-like observations including realistic cadences, through image simulations, and finally processing with Rubin's LSST Science Pipelines. This last step ensures that we generate data products resembling those to be delivered by the Rubin Observatory as closely as is currently possible. The simulated DC2 sky survey covers six optical bands in a wide-fast-deep (WFD) area of approximately 300 deg^2 as well as a deep drilling field (DDF) of approximately 1 deg^2. We simulate 5 years of the planned 10-year survey. The DC2 sky survey has multiple purposes. First, the LSST DESC working groups can use the dataset to develop a range of DESC analysis pipelines to prepare for the advent of actual data. Second, it serves as a realistic testbed for the image processing software under development for LSST by the Rubin Observatory. In particular, simulated data provide a controlled way to investigate certain image-level systematic effects. Finally, the DC2 sky survey enables the exploration of new scientific ideas in both static and time-domain cosmology.

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arXiv2020

Correlations between Triaxial Shapes and Formation History of Dark Matter Haloes

The shape of dark matter haloes plays a critical role in constraining cosmology with upcoming large-scale structure surveys. In this paper, we study the correlations between the triaxial shapes and formation histories in dark matter haloes in the MultiDark Planck 2 N-body cosmological simulation. We find that halo ellipticity is strongly correlated with halo properties that serve as proxies of halo formation history, such as halo concentration and the peak-centroid offset. In particular, the correlation between halo ellipticity and halo concentration is nearly independent of the halo density peak height. We present a simple model for the correlation between halo ellipticity and concentration using conditional abundance matching, and provide fitting formulae for the multi-dimensional distributions of triaxial halo shape as a function of halo peak height. We apply our halo shape model to gauge the effects of halo ellipticity and orientation bias on the excess surface mass density profiles in cluster-size halos. Our model should be useful for exploring the impact of triaxial halo shape on cosmological constraints in upcoming weak lensing surveys of galaxy clusters.

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arXiv2019

How to Optimally Constrain Galaxy Assembly Bias: Supplement Projected Correlation Functions with Count-in-cells Statistics

Most models for the connection between galaxies and their haloes ignore the possibility that galaxy properties may be correlated with halo properties other than mass, a phenomenon known as galaxy assembly bias. Yet, it is known that such correlations can lead to systematic errors in the interpretation of survey data. At present, the degree to which galaxy assembly bias may be present in the real Universe, and the best strategies for constraining it remain uncertain. We study the ability of several observables to constrain galaxy assembly bias from redshift survey data using the decorated halo occupation distribution (dHOD), an empirical model of the galaxy--halo connection that incorporates assembly bias. We cover an expansive set of observables, including the projected two-point correlation function $w_{\mathrm{p}}(r_{\mathrm{p}})$, the galaxy--galaxy lensing signal $ΔΣ(r_{\mathrm{p}})$, the void probability function $\mathrm{VPF}(r)$, the distributions of counts-in-cylinders $P(N_{\mathrm{CIC}})$, and counts-in-annuli $P(N_{\mathrm{CIA}})$, and the distribution of the ratio of counts in cylinders of different sizes $P(N_2/N_5)$. We find that despite the frequent use of the combination $w_{\mathrm{p}}(r_{\mathrm{p}})+ΔΣ(r_{\mathrm{p}})$ in interpreting galaxy data, the count statistics, $P(N_{\mathrm{CIC}})$ and $P(N_{\mathrm{CIA}})$, are generally more efficient in constraining galaxy assembly bias when combined with $w_{\mathrm{p}}(r_{\mathrm{p}})$. Constraints based upon $w_{\mathrm{p}}(r_{\mathrm{p}})$ and $ΔΣ(r_{\mathrm{p}})$ share common degeneracy directions in the parameter space, while combinations of $w_{\mathrm{p}}(r_{\mathrm{p}})$ with the count statistics are more complementary. Therefore, we strongly suggest that count statistics should be used to complement the canonical observables in future studies of the galaxy--halo connection.

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arXiv2019

Cosmological Evidence Modelling: a new simulation-based approach to constrain cosmology on non-linear scales

Extracting accurate cosmological information from galaxy-galaxy and galaxy-matter correlation functions on non-linear scales ($\lesssim 10 h^{-1} \mathrm{Mpc}$) requires cosmological simulations. Additionally, one has to marginalise over several nuisance parameters of the galaxy-halo connection. However, the computational cost of such simulations prohibits naive implementations of stochastic posterior sampling methods like Markov chain Monte Carlo (MCMC) that would require of order $\mathcal{O}(10^6)$ samples in cosmological parameter space. Several groups have proposed surrogate models as a solution: a so-called emulator is trained to reproduce observables for a limited number of realisations in parameter space. Afterwards, this emulator is used as a surrogate model in an MCMC analysis. Here, we demonstrate a different method called Cosmological Evidence Modelling (CEM). First, for each simulation, we calculate the Bayesian evidence marginalised over the galaxy-halo connection by repeatedly populating the simulation with galaxies. We show that this Bayesian evidence is directly related to the posterior probability of cosmological parameters. Finally, we build a physically motivated model for how the evidence depends on cosmological parameters as sampled by the simulations. We demonstrate the feasibility of CEM by using simulations from the Aemulus simulation suite and forecasting cosmological constraints from BOSS CMASS measurements of redshift-space distortions. Our analysis includes an exploration of how galaxy assembly bias affects cosmological inference. Overall, CEM has several potential advantages over the more common approach of emulating summary statistics, including the ability to easily marginalise over highly complex models of the galaxy-halo connection and greater accuracy, thereby reducing the number of simulations required.

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arXiv2019

PRIMUS: Clustering of Star-forming and Quiescent Central Galaxies at $0.2 < z < 0.9$

Previous work has demonstrated that at a given stellar mass, quiescent galaxies are more strongly clustered than star-forming galaxies. The contribution to this signal from central, as opposed to satellite, galaxies is not known, which has strong implications for galaxy evolution models. To investigate the contribution from central galaxies, here we present measurements of the clustering of isolated primary (IP) galaxies, used as a proxy for central galaxies, at ${0.2<z<0.9}$ with data from the PRIMUS galaxy redshift survey. Using a sample of spectroscopic redshifts for $\sim60,000$ galaxies with ${M_*\gtrsim10^9~M_\odot}$ covering 5 deg$^2$ on the sky, we define IP galaxies using isolation cuts in spatial proximity and stellar mass of nearby galaxies. We find that at fixed stellar mass, quiescent IP galaxies are more strongly clustered than star-forming IP galaxies at $z\sim0.35$ $(10σ)$. Using mock galaxy catalogs based on recent halo occupation models of Behroozi et al. (2018) and designed to replicate the parameters of the PRIMUS survey dataset, we find that these clustering differences are due in part to quiescent central galaxies being more strongly clustered than star-forming central galaxies. This is consistent with either distinct stellar-to-halo mass relations for quiescent and star-forming central galaxies, and/or central galaxy assembly bias. We additionally use mock catalogs to assess the dependence of both incompleteness and satellite galaxy contamination in the IP galaxy samples on redshift, galaxy type, and stellar mass, and demonstrate how isolation criteria yield biased subsamples of central galaxies via {\it environmental incompleteness}, or the preferential exclusion of central galaxies in overdense environments.

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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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