A comparison between Shapefit compression and Full-Modelling method with PyBird for DESI 2024 and beyond
Yan Lai, Cullan Howlett, Mark Maus, H’ector Gil-Mar’in, Hernan E. Noriega, Sadi Ram’irez-Solano, Pauline Zarrouk, Jessica N. Aguilar, Steven Ahlen, Ot’avio Alves, Alejandro Aviles, David Brooks, Shi-Fan Chen, Todd Claybaugh, Tamara M. Davis, Kyle Dawson, Axel de la Macorra, Peter Doel, Jaime E. Forero-Romero, Enrique Gazta~naga, Satya Gontcho A Gontcho, Klaus Honscheid, Stephanie Juneau, Martin Landriau, Marc Manera, Ramon Miquel, Eva-Maria Mueller, Seshadri Nadathur, Gustavo Niz, Nathalie Palanque-Delabrouille, Will Percival, Claire Poppett, Mehdi Rezaie, Graziano Rossi, Eusebio Sanchez, Michael Schubnell, David Sprayberry, Gregory Tarl’e, Mariana Vargas-Maga~na, Licia Verde, Sihan Yuan, Rongpu Zhou, Hu Zou
arXiv:2404.07283v1 Announce Type: new
Abstract: DESI aims to provide one of the tightest constraints on cosmological parameters by analyzing the clustering of more than thirty million galaxies. However, obtaining such constraints requires special care in validating the analysis methods, and efforts to reduce the computational time required through techniques such as data compression and emulation. In this work, we perform a precision validation of the PyBird power spectrum modelling code with both a traditional, but emulated, Full-Modelling approach and the model-independent Shapefit compression approach. Using cubic simulations, which accurately reproduce the clustering and precision of the DESI survey, we find that the cosmological constraints from Shapefit and Full-Modelling are consistent with each other at the $sim0.3sigma$ level. Both Shapefit and Full-Modelling are also consistent with the true $Lambda$CDM simulation cosmology, even when including the hexadecapole, down to a scale $k_{mathrm{max}} = 0.20 h mathrm{Mpc}^{-1}$. For extended models such as the $w$CDM and the $o$CDM models, we find including the hexadecapole can significantly improve the constraints and reduce the systematic errors with the same $k_{mathrm{max}}$. Furthermore, we also show that the constraints on cosmological parameters with the correlation function evaluated from PyBird down to $s_{mathrm{min}} = 30 h^{-1} mathrm{Mpc}$ are unbiased, and consistent with the constraints from the power spectrum.arXiv:2404.07283v1 Announce Type: new
Abstract: DESI aims to provide one of the tightest constraints on cosmological parameters by analyzing the clustering of more than thirty million galaxies. However, obtaining such constraints requires special care in validating the analysis methods, and efforts to reduce the computational time required through techniques such as data compression and emulation. In this work, we perform a precision validation of the PyBird power spectrum modelling code with both a traditional, but emulated, Full-Modelling approach and the model-independent Shapefit compression approach. Using cubic simulations, which accurately reproduce the clustering and precision of the DESI survey, we find that the cosmological constraints from Shapefit and Full-Modelling are consistent with each other at the $sim0.3sigma$ level. Both Shapefit and Full-Modelling are also consistent with the true $Lambda$CDM simulation cosmology, even when including the hexadecapole, down to a scale $k_{mathrm{max}} = 0.20 h mathrm{Mpc}^{-1}$. For extended models such as the $w$CDM and the $o$CDM models, we find including the hexadecapole can significantly improve the constraints and reduce the systematic errors with the same $k_{mathrm{max}}$. Furthermore, we also show that the constraints on cosmological parameters with the correlation function evaluated from PyBird down to $s_{mathrm{min}} = 30 h^{-1} mathrm{Mpc}$ are unbiased, and consistent with the constraints from the power spectrum.