Full Modeling and Parameter Compression Methods in configuration space for DESI 2024 and beyond

S. Ramirez-Solano; M. Icaza-Lizaola; H. E. Noriega; M. Vargas-Magaña; S. Fromenteau; A. Aviles; F. Rodriguez-Martinez; J. Aguilar; S. Ahlen; O. Alves; S. Brieden; D. Brooks; T. Claybaugh; S. Cole; A. de la Macorra; Arjun Dey; B. Dey; P. Doel; K. Fanning; J. E. Forero-Romero; E. Gaztañaga; H. Gil-Marín; S. Gontcho A Gontcho; K. Honscheid; C. Howlett; S. Juneau; Y. Lai; M. Landriau; M. Manera; M. Maus; R. Miquel; E. Mueller; A. Muñoz-Gutiérrez; A. D. Myers; S. Nadathur; J. Nie; W. J. Percival; C. Poppett; M. Rezaie; G. Rossi; E. Sanchez; D. Schlegel; M. Schubnell; H. Seo; D. Sprayberry; G. Tarlé; L. Verde; B. A. Weaver; R. H. Wechsler; S. Yuan; P. Zarrouk; H. Zou

arXiv · 2404.07268

Full Modeling and Parameter Compression Methods in configuration space for DESI 2024 and beyond

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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 $n_s$ and allowing for $w_0w_a$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 $1\sigma$ 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.

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S. Ramirez-Solano, M. Icaza-Lizaola, H. E. Noriega, M. Vargas-Magaña, S. Fromenteau, A. Aviles, F. Rodriguez-Martinez, J. Aguilar, S. Ahlen, O. Alves, S. Brieden, D. Brooks, T. Claybaugh, S. Cole, A. de la Macorra, Arjun Dey, B. Dey, P. Doel, K. Fanning, J. E. Forero-Romero, E. Gaztañaga, H. Gil-Marín, S. Gontcho A Gontcho, K. Honscheid, C. Howlett, S. Juneau, Y. Lai, M. Landriau, M. Manera, M. Maus, R. Miquel, E. Mueller, A. Muñoz-Gutiérrez, A. D. Myers, S. Nadathur, J. Nie, W. J. Percival, C. Poppett, M. Rezaie, G. Rossi, E. Sanchez, D. Schlegel, M. Schubnell, H. Seo, D. Sprayberry, G. Tarlé, L. Verde, B. A. Weaver, R. H. Wechsler, S. Yuan, P. Zarrouk, H. Zou. 2024-04-10. Full Modeling and Parameter Compression Methods in configuration space for DESI 2024 and beyond. https://arxiv.org/abs/2404.07268

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