Tootfinder

GLASD: A Loss-Function-Agnostic Global Optimizer for Robust Correlation Estimation under Data Contamination and Heavy Tails
Priyam Das
https://arxiv.org/abs/2506.14801

GLASD: A Loss-Function-Agnostic Global Optimizer for Robust Correlation Estimation under Data Contamination and Heavy Tails
Robust correlation estimation is essential in high-dimensional settings, particularly when data are contaminated by outliers or exhibit heavy-tailed behavior. Many robust loss functions of practical interest-such as those involving truncation or redescending M-estimators-lead to objective functions that are inherently non-convex and non-differentiable. Traditional methods typically focus on a single loss function tailored to a specific contamination model and develop custom algorithms tightly c…

Reanalyzing DESI DR1: 1. $\Lambda$CDM Constraints from the Power Spectrum and Bispectrum
Anton Chudaykin, Mikhail M. Ivanov, Oliver H. E. Philcox
https://arxiv.org/abs/2507.13433 …

Reanalyzing DESI DR1: 1. $Λ$CDM Constraints from the Power Spectrum and Bispectrum
We present the first independent re-analysis of the galaxy clustering data from DESI Data Release 1, utilizing an effective field theory (EFT)-based full-shape model. We analyze the power spectra and bispectra of the public catalogs using a custom-built pipeline based on window-deconvolved quasi-optimal estimators, carefully accounting for all relevant systematic effects. Compared to the official collaboration analysis, we add the galaxy power spectrum hexadecapole and the bispectrum monopole, …