I Congreso de Postgrado fcfm: ingeniería, ciencias e innovación

85 Santiago, 10 al 12 de agosto, 2022 INFORMED TOTAL-ERROR-MINIMIZING (ITEM) PRIORS: INTERPRETABLE COSMOLOGICAL PARAMETER CONSTRAINTS DESPITE COMPLEX NUISANCE EFFECTS Bernardita Ried 1 *, Daniel Gruen 2, Oliver Friedrich2 1 Departamento de Física, FCFM, Universidad de Chile, Av. Blanco Encalada 2008, Santiago, Chile. 2 University Observatory, Faculty of Physics, Ludwig-Maximilians-Universität München, Scheinerstr. 1, 81677 Munich, Germany. 3 Excellence Cluster ORIGINS, Boltzmannstr. 2, 85748 Garching, Germany. *Email: bernardita.ried@ug.uchile.cl ABSTRACT Present and future wide-area surveys of galaxies will provide an unprecedented volume of data over most of the extragalactic sky. An example of this is the Dark Energy Survey (DES) [1]. The promise of precision cosmology with these data can only be realized by accounting for nuisance effects - both astrophysical and in the calibration uncertainty of data - with increasingly complex models. A key step of the cosmological inference from survey data is to perform likelihood analyses that results in a many-dimensional joint posterior distributions. While Bayesian inference techniques are standard in cosmological analyses, the average audience of these analyses interprets re- sulting parameter constraints with a frequentist intuition. It is increasingly understood that this intuition can fail when marginalizing high-dimensional parameter spaces onto subsets of parameters because of what has come to be known as projection effects or prior volume effects. We present the method of Informed Total-Error-Minimizing (ITEM) priors to address this effect. ITEM priors are prior distributions on a set of nuisance parameters intended to enforce the validity of a frequentist interpretation of the posterior constraints derived for a set of target parameters (e.g. cosmological). Our method works as fol - lows: We split the vector of the model parameter into target and nuisance parameters. For a set of realistic data realizations we generate target parameter posteriors using several different candidate priors for the nuisance parameters. Next, we remove candidate priors that do not accomplish minimum requirements of bias (of point estimates) and coverage (of 1-σ confidence regions) for the target parameters. Of the priors that survive this cut we select the ITEM prior as the one that minimizes the total error of the marginalized posteriors of the target parameters. As an example, we apply our method by re-analyzing the Density Split Statistics (DSS) measured in DES Year 1 data. We provide ITEM priors needed to model the shot-noise of tracer galaxies that enter the DSS. We demon- strate that the ITEM priors substantially reduce prior volume effects that arise when marginalizing over these shot-noise parameters. ACKNOWLEDGMENTS BR was funded by the Chilean National Agency for Research and Development (ANID) - Subdirección de Capital Humano / Magíster Nacional / 2021 - ID 22210491 and the German Academic Exchange Service (DAAD, Short- Term Research Grant 2021 No. 57552337). REFERENCES [1] Dark Energy Survey Collaboration, MNRAS 460 , 1270–1299 (2016) 05 F Í S I CA Y A S T ROF Í S I CA