Reducing simulation-related emissions in rare-event searches through optimised event biasing
Abstract
Rare-event search experiments continue to extend their sensitivities to unprecedented levels. This requires increasingly small backgrounds, and shielding schemes that can suppress external backgrounds by several orders of magnitude. Moreover, detailed simulations are required to attain a good understanding of these experimental backgrounds. Studies have suggested, however, that simulations and computing-related tasks contribute approximately 10\% to the average particle physicist's carbon footprint. With backgrounds frequently below 0.01 counts per kg of target per keV of energy, simulations require more computing resources; growing a rare-event researcher's computing-related carbon footprint. Event biasing is often applied in shielding simulations as a remedy, yet there is a lack of systematic guidance on how to maximally benefit from them. An optimisation study for the importance-splitting biasing technique is discussed, focused on balancing statistical precision with simulation CPU-time, and how this can benefit the average researcher's carbon footprint.
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BibTeX
@misc{knights2026reducing,
title = {{Reducing simulation-related emissions in rare-event searches through optimised event biasing}},
author = {P. Knights and L. J. Milligan and K. Nikolopoulos},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.07523},
eprint = {2609.07523},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.07523},
note = {CC BY-SA 4.0},
}
RIS
TY - UNPB
TI - Reducing simulation-related emissions in rare-event searches through optimised event biasing
AU - P. Knights
AU - L. J. Milligan
AU - K. Nikolopoulos
PY - 2026
DA - 2026/09/07
PB - arXiv
DO - 10.48550/arxiv.2609.07523
UR - https://arxiv.org/abs/2609.07523
AB - Rare-event search experiments continue to extend their sensitivities to unprecedented levels. This requires increasingly small backgrounds, and shielding schemes that can suppress external backgrounds by several orders of magnitude. Moreover, detailed simulations are required to attain a good understanding of these experimental backgrounds. Studies have suggested, however, that simulations and computing-related tasks contribute approximately 10\% to the average particle physicist's carbon footprint. With backgrounds frequently below 0.01 counts per kg of target per keV of energy, simulations require more computing resources; growing a rare-event researcher's computing-related carbon footprint. Event biasing is often applied in shielding simulations as a remedy, yet there is a lack of systematic guidance on how to maximally benefit from them. An optimisation study for the importance-splitting biasing technique is discussed, focused on balancing statistical precision with simulation CPU-time, and how this can benefit the average researcher's carbon footprint.
ER -