Engineering topics

Samushia, L.

Publications and source records attributed to Samushia, L..

2 records

DOE OSTI2025

$H_0$ Without the Sound Horizon (or Supernovae): A 2% Measurement in DESI DR1

The sound horizon scale $r_s$ is a key source of information for early-time $H_0$ measurements, and is therefore a common target of new physics proposed to solve the Hubble tension. We present a sub-2% measurement of the Hubble constant that is independent of this scale, using data from the first data release of the Dark Energy Spectroscopic Instrument (DESI DR1). Building on previous work, we remove dependency on the sound horizon size using a heuristic rescaling procedure at the power spectrum level. A key innovation is the inclusion of \emph{uncalibrated} (agnostic to $r_s$) post-reconstruction BAO measurements from DESI DR1, as well as using the CMB acoustic scale $θ_*$ as a high-redshift anchor. Uncalibrated type-Ia supernovae are often included as an independent source of $Ω_m$ information; here we demonstrate the robustness of our results by additionally considering two supernova-independent alternative datasets. We find somewhat higher values of $H_0$ relative to our previous work: $69.2^{+1.3}_{-1.4}$, $70.3^{+1.4}_{-1.2}$, and $69.6^{+1.3}_{-1.8}\,{\rm km\,s^{-1}\,Mpc^{-1}}$ respectively when including measurements from i) Planck/ACT CMB lensing $\times$ unWISE galaxies, ii) the DES Year 3 6$\times$2pt analysis, and iii) Planck/ACT CMB lensing + the DES Year 5 supernova analysis. These remarkably consistent constraints achieve better than 2% precision; they are among the most stringent sound horizon-independent measurements from LSS to date, and provide a powerful avenue for probing the origin of the Hubble tension.

Cosmology and Nongalactic Astrophysics (astro-ph.C↗

DOE OSTI2024

Constraining Galaxy-Halo connection using machine learning

We investigate the potential of machine learning (ML) methods to model small-scale galaxy clustering for constraining Halo Occupation Distribution (HOD) parameters. Our analysis reveals that while many ML algorithms report good statistical fits, they often yield likelihood contours that are significantly biased in both mean values and variances relative to the true model parameters. This highlights the importance of careful data processing and algorithm selection in ML applications for galaxy clustering, as even seemingly robust methods can lead to biased results if not applied correctly. ML tools offer a promising approach to exploring the HOD parameter space with significantly reduced computational costs compared to traditional brute-force methods if their robustness is established. Using our ANN-based pipeline, we successfully recreate some standard results from recent literature. Properly restricting the HOD parameter space, transforming the training data, and carefully selecting ML algorithms are essential for achieving unbiased and robust predictions. Among the methods tested, artificial neural networks (ANNs) outperform random forests (RF) and ridge regression in predicting clustering statistics, when the HOD prior space is appropriately restricted. We demonstrate these findings using the projected two-point correlation function (w p (r p )), angular multipoles of the correlation function (ξ ℓ (r)), and the void probability function (VPF) of Luminous Red Galaxies from Dark Energy Spectroscopic Instrument mocks. Our results show that while combining w p (r p ) and VPF improves parameter constraints, adding the multipoles ξ 0 , ξ 2 , and ξ 4 to w p (r p ) does not significantly improve the constraints.

cosmology↗