BrahMap: A scalable and modular map-making framework for the CMB experiments

Fuente: arXiv
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Main Authors: Anand, Avinash, Puglisi, Giuseppe
Format: Preprint
Published: 2025
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author Anand, Avinash
Puglisi, Giuseppe
author_facet Anand, Avinash
Puglisi, Giuseppe
contents The cosmic microwave background (CMB) experiments have reached an era of unprecedented precision and complexity. Aiming to detect the primordial B-mode polarization signal, these experiments will soon be equipped with $10^{4}$ to $10^{5}$ detectors. Consequently, future CMB missions will face the substantial challenge of efficiently processing vast amounts of raw data to produce the initial scientific outputs - the sky maps - within a reasonable time frame and with available computational resources. To address this, we introduce BrahMap, a new map-making framework that will be scalable across both CPU and GPU platforms. Implemented in C++ with a user-friendly Python interface for handling sparse linear systems, BrahMap employs advanced numerical analysis and high-performance computing techniques to maximize the use of super-computing infrastructure. This work features an overview of the BrahMap's capabilities and preliminary performance scaling results, with application to a generic CMB polarization experiment.
format Preprint
id arxiv_https___arxiv_org_abs_2501_16122
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle BrahMap: A scalable and modular map-making framework for the CMB experiments
Anand, Avinash
Puglisi, Giuseppe
Cosmology and Nongalactic Astrophysics
Instrumentation and Methods for Astrophysics
The cosmic microwave background (CMB) experiments have reached an era of unprecedented precision and complexity. Aiming to detect the primordial B-mode polarization signal, these experiments will soon be equipped with $10^{4}$ to $10^{5}$ detectors. Consequently, future CMB missions will face the substantial challenge of efficiently processing vast amounts of raw data to produce the initial scientific outputs - the sky maps - within a reasonable time frame and with available computational resources. To address this, we introduce BrahMap, a new map-making framework that will be scalable across both CPU and GPU platforms. Implemented in C++ with a user-friendly Python interface for handling sparse linear systems, BrahMap employs advanced numerical analysis and high-performance computing techniques to maximize the use of super-computing infrastructure. This work features an overview of the BrahMap's capabilities and preliminary performance scaling results, with application to a generic CMB polarization experiment.
title BrahMap: A scalable and modular map-making framework for the CMB experiments
topic Cosmology and Nongalactic Astrophysics
Instrumentation and Methods for Astrophysics
url https://arxiv.org/abs/2501.16122