Full Modeling and Parameter Compression Methods in configuration space for DESI 2024 and beyond
Abstract
In the contemporary era of high-precision spectroscopic surveys, led by projects like DESI, there is an increasing demand for optimizing the extraction of cosmological information from clustering data. This work conducts a thorough comparison of various methodologies for modeling the full shape of the two-point statistics in configuration space. We investigate the performance of both direct fits (Full-Modeling) and the parameter compression approaches (ShapeFit and Standard). We utilize the ABACUS-SUMMIT simulations, tailored to exceed DESI's precision requirements. Particularly, we fit the two-point statistics of three distinct tracers (LRG, ELG, and QSO), by employing a Gaussian Streaming Model in tandem with Convolution Lagrangian Perturbation Theory and Effective Field Theory. We explore methodological setup variations, including the range of scales, the set of galaxy bias parameters, the inclusion of the hexadecapole, as well as model extensions encompassing varying and allowing for CDM dark energy model. Throughout these varied explorations, while precision levels fluctuate and certain configurations exhibit tighter parameter constraints, our pipeline consistently recovers the parameter values of the mocks within in all cases for a 1-year DESI volume. Additionally, we compare the performance of configuration space analysis with its Fourier space counterpart using three models: PyBird, FOLPS and velocileptors, presented in companion papers. We find good agreement with the results from all these models.
Cite
@article{arxiv.2404.07268,
title = {Full Modeling and Parameter Compression Methods in configuration space for DESI 2024 and beyond},
author = {S. Ramirez-Solano and M. Icaza-Lizaola and H. E. Noriega and M. Vargas-Magaña and S. Fromenteau and A. Aviles and F. Rodriguez-Martinez and J. Aguilar and S. Ahlen and O. Alves and S. Brieden and D. Brooks and T. Claybaugh and S. Cole and A. de la Macorra and Arjun Dey and B. Dey and P. Doel and K. Fanning and J. E. Forero-Romero and E. Gaztañaga and H. Gil-Marín and S. Gontcho A Gontcho and K. Honscheid and C. Howlett and S. Juneau and Y. Lai and M. Landriau and M. Manera and M. Maus and R. Miquel and E. Mueller and A. Muñoz-Gutiérrez and A. D. Myers and S. Nadathur and J. Nie and W. J. Percival and C. Poppett and M. Rezaie and G. Rossi and E. Sanchez and D. Schlegel and M. Schubnell and H. Seo and D. Sprayberry and G. Tarlé and L. Verde and B. A. Weaver and R. H. Wechsler and S. Yuan and P. Zarrouk and H. Zou},
journal= {arXiv preprint arXiv:2404.07268},
year = {2024}
}
Comments
Supporting publication of DESI 2024 KP5