arXiv subjects

Jacob Kennedy

Publications and source records attributed to Jacob Kennedy.

4 recordsLinked to original sources

arXiv2026

A Non-parametric Method for the Inference of Halo Occupation Distributions

The galaxy-halo connection traces processes by which galaxies form and evolve. The halo occupation distribution (HOD) describes the relationship between galaxies and their host dark matter haloes. Measurements of the galaxy two-point correlation function (2PCF) allow us to extract information about the HODs of observed galaxy samples. Several parametric HOD models have been proposed in the literature, but the choice of parameterization restricts the space of possible HODs. To resolve this issue, we introduce a non-parametric HOD fitting method in which we train an emulator to learn the mappings among the galaxy 2PCF, physical properties used to select galaxy samples, and the HOD, all obtained from simulated past lightcones constructed with the Santa Cruz semi-analytic models. Implementing this emulator within a likelihood analysis framework, we derive constraints on the HOD of a galaxy sample when provided with a measurement of its 2PCF. Using the emulator to accelerate likelihood evaluations, we test the non-parametric HOD approach on a set of 2PCFs for mock galaxy samples drawn from the TNG100-1 simulation and selected above threshold values of stellar mass and star formation rate. Our framework is able to recover TNG100-1 HODs within 0.2 dex. We use the TNG100-1 mocks to tune the reported uncertainties to estimate those expected in the analysis of observations. Comparing to parametric HOD modeling routines applied to the same mock galaxy samples, our approach consistently infers the HOD with comparable or greater precision and accuracy.

astro-ph.CO↗

arXiv2025

ODIN: Clustering Analysis of 14,000 Ly{\alpha} Emitting Galaxies at z=2.4, 3.1, and 4.5

Lyman Alpha Emitters (LAEs) are star-forming galaxies that efficiently probe the spatial distribution of galaxies in the high redshift universe. The spatial clustering of LAEs reflects the properties of their individual host dark matter halos, allowing us to study the evolution of the galaxy-halo connection. We analyze the clustering of 5233, 5220, and 3706 LAEs at $z$ = 2.4, 3.1, and 4.5, respectively, in the 9 deg$^2$ COSMOS field from the One-hundred-deg$^2$ DECam Imaging in Narrowbands (ODIN) survey. After correcting for redshift space distortions, LAE contamination rates, and the integral constraint, the observed angular correlation functions imply linear galaxy bias factors of $b$ = $1.72^{+0.26}_{-0.27}, 2.01^{+0.26}_{-0.29},$ and $2.95^{+0.40}_{-0.46}$, for $z$ = 2.4, 3.1, and 4.5, respectively. The median dark matter halo masses inferred from these measurements are $\log(M_{h}/M_{\odot})$ = $11.44^{+0.30}_{-0.28}, 11.13^{+0.26}_{-0.26}$, and $10.85^{+0.24}_{-0.24}$ for the three samples, respectively. The analysis also reveals that LAEs occupy roughly 3-7% of the halos whose clustering strength matches that of the LAEs.

astro-ph.GA↗

arXiv2023

Machine-learning recovery of foreground wedge-removed 21-cm light cones for high-$z$ galaxy mapping

Upcoming experiments will map the spatial distribution of the 21-cm signal over three-dimensional volumes of space during the Epoch of Reionization (EoR). Several methods have been proposed to mitigate the issue of astrophysical foreground contamination in tomographic images of the 21-cm signal, one of which involves the excision of a wedge-shaped region in cylindrical Fourier space. While this removes the $k$-modes most readily contaminated by foregrounds, the concurrent removal of cosmological information located within the wedge considerably distorts the structure of 21-cm images. In this study, we build upon a U-Net based deep learning algorithm to reconstruct foreground wedge-removed maps of the 21-cm signal, newly incorporating light-cone effects. Adopting the Square Kilometre Array (SKA) as our fiducial instrument, we highlight that our U-Net recovery framework retains a reasonable level of reliability even in the face of instrumental limitations and noise. We subsequently evaluate the efficacy of recovered maps in guiding high-redshift galaxy searches and providing context to existing galaxy catalogues. This will allow for studies of how the high-redshift galaxy luminosity function varies across environments, and ultimately refine our understanding of the connection between the ionization state of the intergalactic medium (IGM) and galaxies during the EoR.

astro-ph.CO↗

arXiv2021

Recovering the Wedge Modes Lost to 21-cm Foregrounds

One of the critical challenges facing imaging studies of the 21-cm signal at the Epoch of Reionization (EoR) is the separation of astrophysical foreground contamination. These foregrounds are known to lie in a wedge-shaped region of $(k_{\perp},k_{\parallel})$ Fourier space. Removing these Fourier modes excises the foregrounds at grave expense to image fidelity, since the cosmological information at these modes is also removed by the wedge filter. However, the 21-cm EoR signal is non-Gaussian, meaning that the lost wedge modes are correlated to the surviving modes by some covariance matrix. We have developed a machine learning-based method which exploits this information to identify ionized regions within a wedge-filtered image. Our method reliably identifies the largest ionized regions and can reconstruct their shape, size, and location within an image. We further demonstrate that our method remains viable when instrumental effects are accounted for, using the Hydrogen Epoch of Reionization Array and the Square Kilometre Array as fiducial instruments. The ability to recover spatial information from wedge-filtered images unlocks the potential for imaging studies using current- and next-generation instruments without relying on detailed models of the astrophysical foregrounds themselves.

astro-ph.CO↗