Programma: PRIN
Responsabile scientifico per il dipartimento: Michele Ennio Maria Moresco
Struttura principale: DIFA
Data inizio e data fine: dal 28/09/2023 al 28/09/2025
Nuove tecniche per l’analisi dei redshift galattici
Il progetto sviluppa metodi innovativi per analizzare survey spettroscopiche, combinando correlazioni di ordine superiore per migliorare la stima dei parametri cosmologici e gestire gli errori sistematici.
New Techniques for Galaxy Redshift Survey Analysis
The project develops innovative methods to analyze spectroscopic surveys, combining higher-order correlations to improve cosmological parameter estimates and handle systematic errors.
Abstract
The aim of this proposal is to develop new methods to best exploit spectroscopic galaxy redshift surveys, with particular attention onsystematic errors that can introduce significant biases in the cosmological results. We will exploit for the first time at the BAO scalethe constraining power of the combination of lower- and higher-order correlation functions, that encode additional information onBAO and structure formation that is not fully accessed by current analysis methods. To achieve this, we propose to develop anend-to-end clustering analysis in which we will model the main observational systematic effects that impact particular galaxysurveys, propose mitigation strategies, measure clustering statistics with a joint analysis of 2PCF and 3PCF up to r~120 Mpc/h, andestimate cosmological parameters, assessing the gain in constraining power compared to other approaches such as reconstruction.Taking advantage of the position of the PI and CoI in international collaborations (Euclid, VIPERS), we will consider both the latestand largest real data available in which a cosmological analysis of 2PCF+3PCF has not been performed (eBOSS emission-linegalaxies, luminous red galaxies, and quasars, VIPERS), but also make use of the state-of-art simulated data to forecast theperformance in future cosmological surveys, including Roman.This work will provide the basis to maximise the scientific return of clustering analysis for future incoming cosmological surveys, and provide a robust framework to address, mitigate, and correctly propagate the effect of systematic uncertainties up to higher-ordercorrelation functions