arXiv subjects

Haley Bowden

Publications and source records attributed to Haley Bowden.

4 recordsLinked to original sources

arXiv2026

Accurate modeling for 3$\times$2pt analyses in Roman and Rubin: a study of model approximations

One of the pillars of modern cosmology is the use of galaxy imaging surveys to extract information from the large-scale structure. In recent surveys, this measurement is typically performed through a 3$\times$2pt analysis, which combines auto- and cross-correlations between galaxy density and galaxy weak lensing. In this paper, we carry out a systematic study of three modeling approximations commonly used in such analyses: 1) applying the Limber approximation, 2) neglecting redshift-space distortions, and 3) using less accurate models for the nonlinear matter power spectrum. We carry out the study in the context of the final data from two major Stage-IV galaxy imaging surveys: the Nancy Grace Roman Space Telescope's High Latitude Imaging Survey and the Vera C. Rubin Observatory's Legacy Survey of Space and Time. To do this, we first validate our modeling pipeline, implemented in the software package CoCoA, against an established code base, CCL. Next, we perform a simulated likelihood analysis to assess the impact of these approximations on the cosmological constraints. We find all three effects to be important; neglecting any of them can induce biases in cosmological constraints approaching or exceeding $1\sigma$, and exceeding $2\sigma$ for Rubin in several cases. Moreover, we explore how the lens-galaxy sample configuration and scale-cut choice can influence the constraints.

astro-ph.CO↗

arXiv2026

Fisher Forecasts for Cosmological Yields from $3\!\times\!2$pt Analysis of the Roman Space Telescope High Latitude Imaging Survey

The High Latitude Imaging Survey (HLIS) of NASA's Nancy Grace Roman Space Telescope will provide powerful tests of cosmological models through sensitive measurements of cosmic shear, galaxy-galaxy lensing (GGL), and galaxy clustering. As part of the HLIS Project Infrastructure Team's Data Challenge 1 (DC1), we carry out Fisher forecasts of cosmological parameter constraints from combinations of these probes, focusing on inverse-variance figures of merit (FoMs) for the parameters $\sigma_8$ and $\Omega_{\rm{m}}$, which scale the amplitude of weak lensing signals. We find good agreement between Fisher analysis and Markov chain Monte Carlo (MCMC) analysis of the DC1 baseline data vector, and we investigate varied priors on cosmological parameters and on nuisance parameters describing unknown biases in photometric redshifts or shear measurements. The DC1 benchmark modeling assumes a ``lens $=$ source'' analysis with linear galaxy bias and no marginalization over baryonic physics. Under these assumptions, the forecast constraints from GGL+clustering are substantially stronger than those from cosmic shear, with the combination of all three probes (``$3\!\times\!2$pt'') providing moderate further improvement. Adding tight external priors on the power spectrum shape parameters $n_{\rm{s}}$, $\Omega_{\rm{b}}$, and $h_0$ can improve the $(\sigma_8, \Omega_{\rm{m}})$ FoMs by factors of $1.2$--$3.5$. The smallest scale angular bins provide much more information than the largest scale bins, and the highest redshift tomographic bins provide more information than the lowest redshift bins. Factor-of-two changes in the priors on photo-$z$ and shear biases, relative to the benchmark values based on anticipated calibration accuracy, produce changes of $\lesssim 20\%$ in FoMs.

astro-ph.CO↗

arXiv2025

Halo Properties from Observable Measures of Environment: II. Central versus Satellite Classification

A physical understanding of galaxy formation and evolution benefits from an understanding of the connections between galaxies, their host dark matter halos, and their environments. In particular, interactions with more-massive neighbors can leave lasting imprints on both galaxies and their hosts. Distinguishing between populations of galaxies with differing environments and interaction histories is therefore essential for isolating the role of environment in shaping galaxy properties. We present a novel neural-network based method, which takes advantage of observable measures of a galaxy and its environment to recover whether it (1) is a central or a satellite, (2) has experienced an interaction with a more massive neighbor, and (3) is currently orbiting or infalling onto such a neighbor. We find that projected distances to, redshift separations of, and relative stellar masses with respect to a galaxy's 25 nearest neighbors are sufficient to distinguish central from satellite halos in $> 90\%$ of cases, with projection effects accounting for most classification errors. Our method also achieves high accuracy in recovering interaction history and orbital status, though the network struggles to distinguish between splashback and infalling systems in some cases due to the lack of velocity information. With careful treatment of the uncertainties introduced by projection and other observational limitations, this method offers a new avenue for studying the role of environment in galaxy formation and evolution.

astro-ph.GA↗

arXiv2023

Halo Properties from Observable Measures of Environment: I. Halo and Subhalo Masses

The stellar mass - halo mass relation provides a strong basis for connecting galaxies to their host dark matter halos in both simulations and observations. Other observable information, such as the density of the local environment, can place further constraints on a given halo's properties. In this paper, we test how the peak masses of dark matter halos and subhalos correlate with observationally-accessible environment measures, using a neural network to extract as much information from the environment as possible. For high mass halos (peak mass $>10^{12.5} M_{\odot}$), the information on halo mass contained in stellar mass - selected galaxy samples is confined to the $\sim$ 1 Mpc region surrounding the halo center. Below this mass threshold, nearly the entirety of the information on halo mass is contained in the galaxy's own stellar mass instead of the neighboring galaxy distribution. The overall root-mean-squared error of the best-performing network was 0.20 dex. When applied to only the central halos within the test data, the same network had an error of 0.17 dex. Our findings suggest that, for the purposes of halo mass inference, both distances to the $k$th nearest neighbor and counts in cells of neighbors in a fixed aperture are similarly effective measurements of the local environment.

astro-ph.GA↗