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A Simulation Framework for the LiteBIRD Instruments

M. Tomasi, L. Pagano, A. Anand, C. Baccigalupi, A. J. Banday, M. Bortolami, G. Galloni, M. Galloway, T. Ghigna, S. Giardiello, M. Gomes, E. Hivon, N. Krachmalnicoff, S. Micheli, M. Monelli, Y. Nagano, A. Novelli, G. Patanchon, D. Poletti, G. Puglisi, N. Raffuzzi, M. Reinecke, Y. Takase, G. Weymann-Despres, D. Adak, E. Allys, J. Aumont, R. Aurvik, M. Ballardini, R. B. Barreiro, N. Bartolo, S. Basak, M. Bersanelli, A. Besnard, T. Brinckmann, E. Calabrese, P. Campeti, E. Carinos, A. Carones, F. J. Casas, K. Cheung, M. Citran, L. Clermont, F. Columbro, G. Coppi, A. Coppolecchia, F. Cuttaia, P. Dal Bo, P. de Bernardis, E. de la Hoz, M. De Lucia, S. Della Torre, P. Diego-Palazuelos, H. K. Eriksen, T. Essinger-Hileman, C. Franceschet, U. Fuskeland, M. Gerbino, M. Gervasi, C. Gimeno-Amo, E. Gjerløw, A. Gruppuso, M. Hazumi, S. Henrot-Versillé, L. T. Hergt, B. Jost, K. Kohri, L. Lamagna, T. Lari, M. Lattanzi, C. Leloup, F. Levrier, A. I. Lonappan, M. López-Caniego, G. Luzzi, J. Macias-Perez, B. Maffei, E. Martínez-González, S. Masi, S. Matarrese, T. Matsumura, L. Montier, G. Morgante, L. Mousset, R. Nagata, F. Noviello, I. Obata, A. Occhiuzzi, A. Paiella, D. Paoletti, G. Pascual-Cisneros, F. Piacentini, M. Pinchera, G. Polenta, L. Porcelli, M. Remazeilles, A. Ritacco, A. Rizzieri, J. A. Rubiño-Martín, M. Ruiz-Granda, J. Sanghavi, V. Sauvage, M. Shiraishi, G. Signorelli, S. L. Stever, R. M. Sullivan, K. Tassis, L. Terenzi, L. Vacher, B. van Tent, P. Vielva, I. K. Wehus, M. Zannoni, Y. Zhou

TL;DR

The paper presents the LiteBIRD Simulation Framework (LBS), a Python-based, MPI-enabled library designed to model the data acquisition process for LiteBIRD's LFT, MFT, and HFT. It details a modular architecture with simulation and data-reduction components, a memory-layout strategy for large TODs, and provenance tracking through a versioned Instrument Model (IMo) database. Key contributions include modules for sky map generation via PySM3, a flexible scanning strategy, realistic noise and solar-dipole modeling, ideal HWP handling, and basic map-making tools, all validated with unit/integration tests and reproducibility guarantees. The framework supports end-to-end pipeline development and interfaces with external tools like Commander, enabling accurate performance characterization and reproducible research for the LiteBIRD mission.

Abstract

LiteBIRD, the Lite (Light) satellite for the study of $B$-mode polarization and Inflation from cosmic background Radiation Detection, is a space mission focused on primordial cosmology and fundamental physics. In this paper, we present the LiteBIRD Simulation Framework (LBS), a Python package designed for the implementation of pipelines that model the outputs of the data acquisition process from the three instruments on the LiteBIRD spacecraft: LFT (Low-Frequency Telescope), MFT (Mid-Frequency Telescope), and HFT (High-Frequency Telescope). LBS provides several modules to simulate the scanning strategy of the telescopes, the measurement of realistic polarized radiation coming from the sky (including the Cosmic Microwave Background itself, the Solar and Kinematic dipole, and the diffuse foregrounds emitted by the Galaxy), the generation of instrumental noise and the effect of systematic errors, like pointing wobbling, non-idealities in the Half-Wave Plate, et cetera. Additionally, we present the implementation of a simple but complete pipeline that showcases the main features of LBS. We also discuss how we ensured that LBS lets people develop pipelines whose results are accurate and reproducible. A full end-to-end pipeline has been developed using LBS to characterize the scientific performance of the LiteBIRD experiment. This pipeline and the results of the first simulation run are presented in Puglisi et al. (2025).

A Simulation Framework for the LiteBIRD Instruments

TL;DR

The paper presents the LiteBIRD Simulation Framework (LBS), a Python-based, MPI-enabled library designed to model the data acquisition process for LiteBIRD's LFT, MFT, and HFT. It details a modular architecture with simulation and data-reduction components, a memory-layout strategy for large TODs, and provenance tracking through a versioned Instrument Model (IMo) database. Key contributions include modules for sky map generation via PySM3, a flexible scanning strategy, realistic noise and solar-dipole modeling, ideal HWP handling, and basic map-making tools, all validated with unit/integration tests and reproducibility guarantees. The framework supports end-to-end pipeline development and interfaces with external tools like Commander, enabling accurate performance characterization and reproducible research for the LiteBIRD mission.

Abstract

LiteBIRD, the Lite (Light) satellite for the study of -mode polarization and Inflation from cosmic background Radiation Detection, is a space mission focused on primordial cosmology and fundamental physics. In this paper, we present the LiteBIRD Simulation Framework (LBS), a Python package designed for the implementation of pipelines that model the outputs of the data acquisition process from the three instruments on the LiteBIRD spacecraft: LFT (Low-Frequency Telescope), MFT (Mid-Frequency Telescope), and HFT (High-Frequency Telescope). LBS provides several modules to simulate the scanning strategy of the telescopes, the measurement of realistic polarized radiation coming from the sky (including the Cosmic Microwave Background itself, the Solar and Kinematic dipole, and the diffuse foregrounds emitted by the Galaxy), the generation of instrumental noise and the effect of systematic errors, like pointing wobbling, non-idealities in the Half-Wave Plate, et cetera. Additionally, we present the implementation of a simple but complete pipeline that showcases the main features of LBS. We also discuss how we ensured that LBS lets people develop pipelines whose results are accurate and reproducible. A full end-to-end pipeline has been developed using LBS to characterize the scientific performance of the LiteBIRD experiment. This pipeline and the results of the first simulation run are presented in Puglisi et al. (2025).

Paper Structure

This paper contains 37 sections, 5 equations, 2 figures.

Table of Contents

  1. Introduction
  2. Requirements
  3. Overall design
  4. Supported platforms
  5. Memory layout
  6. Scientific samples
  7. Pointing information and Half-Wave Plate angles
  8. I/O
  9. Other data
  10. Provenance tracking
  11. Modules implemented in LBS
  12. Simulation modules
  13. Simulation of input maps
  14. Scanning strategy
  15. Instrumental noise
  16. ...and 22 more sections

Figures (2)

  • Figure 1: The way LBS can split a 2D matrix containing a TOD, as well as any other attribute associated with each detector, like the noise level, the FWHM of the beam, etc. The first row illustrates how splits are made when the code is run in serial mode, i.e., without MPI. The parameters n_blocks_det and n_blocks_time specify the number of splits between the detectors and along the time axis, respectively. The bottom right panel shows how TODs are split when there are four MPI processes. Attributes can be copied over many MPI processes if these processes handle the same detectors. (The latter is the case of the two panels on the right.)
  • Figure 2: Simplified view of the PTEP IMo bundled with LBS 0.11.0. Boxes represent "entities", i.e., the nodes that provide the structure (topology) of the tree, while gray ovals represent the "quantities" that contain actual data. (A third level is represented by "data files", which are different versions of a quantity and are similar to commits in a Git repository.) The image only shows a few entities and quantities of the whole tree: for each instrument (LFT, MFT, HFT), only two channels are represented, and each channel shows only two detectors.