BEYONDPLANCK

IV. Simulations and validation

¹ Institute of Theoretical Astrophysics, University of Oslo, Blindern, Oslo, Norway
² Instituto de Física, Universidade de São Paulo, C.P. 66318, CEP:, 05315-970 São Paulo, Brazil
³ Department of Physics and Astronomy, University of British Columbia, Vancouver, BC V6T 1Z1, Canada
⁴ Astronomical Institute, Tohoku University, Sendai, Miyagi 980-8578, Japan
⁵ Indian Institute of Astrophysics, Koramangala II Block, Bangalore 560034, India
⁶ Department of Physics, University of California, Berkeley, Berkeley, CA, USA
⁷ Instituto Nacional de Pesquisas Espaciais, Divisão de Astrofísica, Av. dos Astronautas, 1758, 12227-010 São José dos Campos, SP, Brazil
⁸ Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, UK
⁹ Department of Physics and Electronics, Rhodes University, PO Box 94, Grahamstown 6140, South Africa
¹⁰ Planetek Hellas, Leoforos Kifisias 44, Marousi 151 25, Greece
¹¹ Dipartimento di Fisica, Università degli Studi di Milano, Via Celoria, 16, Milano, Italy
¹² INAF/IASF Milano, Via E. Bassini 15, Milano, Italy
¹³ INFN, Sezione di Milano, Via Celoria 16, Milano, Italy
¹⁴ INAF – Osservatorio Astronomico di Trieste, Via G.B. Tiepolo 11, Trieste, Italy
¹⁵ Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08544, USA
¹⁶ Kavli IPMU (WPI), UTIAS, The University of Tokyo, Kashiwa, Chiba 277-8583, Japan
¹⁷ Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA, USA
¹⁸ Department of Physics, Gustaf Hällströmin katu 2, University of Helsinki, Helsinki, Finland
¹⁹ Helsinki Institute of Physics, Gustaf Hällströmin katu 2, University of Helsinki, Helsinki, Finland
²⁰ Computational Cosmology Center, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
²¹ Haverford College Astronomy Department, 370 Lancaster Avenue, Haverford, Pennsylvania, USA
²² Max-Planck-Institut für Astrophysik, Karl-Schwarzschild-Str. 1, 85741 Garching, Germany
²³ Dipartimento di Fisica, Università degli Studi di Trieste, Via A. Valerio 2, Trieste, Italy

Received: 12 September 2022
Accepted: 2 April 2023

Abstract

End-to-end simulations play a key role in the analysis of any high-sensitivity cosmic microwave background (CMB) experiment, providing high-fidelity systematic error propagation capabilities that are unmatched by any other means. In this paper, we address an important issue regarding such simulations, namely, how to define the inputs in terms of sky model and instrument parameters. These may either be taken as a constrained realization derived from the data or as a random realization independent from the data. We refer to these as posterior and prior simulations, respectively. We show that the two options lead to significantly different correlation structures, as prior simulations (contrary to posterior simulations) effectively include cosmic variance, but they exclude realization-specific correlations from non-linear degeneracies. Consequently, they quantify fundamentally different types of uncertainties. We argue that as a result, they also have different and complementary scientific uses, even if this dichotomy is not absolute. In particular, posterior simulations are in general more convenient for parameter estimation studies, while prior simulations are generally more convenient for model testing. Before BEYONDPLANCK, most pipelines used a mix of constrained and random inputs and applied the same hybrid simulations for all applications, even though the statistical justification for this is not always evident. BEYONDPLANCK represents the first end-to-end CMB simulation framework that is able to generate both types of simulations and these new capabilities have brought this topic to the forefront. The BEYONDPLANCK posterior simulations and their uses are described extensively in a suite of companion papers. In this work, we consider one important applications of the corresponding prior simulations, namely, code validation. Specifically, we generated a set of one-year LFI 30 GHz prior simulations with known inputs and we used these to validate the core low-level BEYONDPLANCK algorithms dealing with gain estimation, correlated noise estimation, and mapmaking.