Speaker
Description
Rare-event search experiments are continuously extending their sensitivities to unprecedented levels. Achieving these feats requires increasingly small backgrounds, partly as a result of improved shielding schemes that can suppress external backgrounds by several orders of magnitude. Detailed detector and shielding simulations are required to attain a good understanding of experimental backgrounds, and the achieved sensitivity. Studies have suggested, however, that simulations and computing-related tasks contribute approximately 10% to the average particle physicist's carbon footprint [1]. With backgrounds now frequently below 0.01 counts per kg of target per keV$_{\rm ee}$ of energy, said simulations require increasingly more computing resources thereby growing a researcher's computing-related carbon footprint. Event biasing is often applied to mitigate this issue, particularly in shielding simulations, yet there is a lack of studies providing systematic guidance on how to best optimise biasing techniques for statistical precision and CPU-time, limiting the obtained benefit. Such an optimisation study for the importance-splitting biasing technique implemented in GEANT4 will be discussed, focused on balancing statistical precision with simulation CPU-time, alongside how this can best reduce the average researcher's carbon footprint.
[1] Valerie S. Lang et al. “Know your footprint — Evaluation of the professional carbon footprint for individual researchers in high energy physics and related fields”. In: npj Climate Action 4.1 (Mar. 2025). ISSN: 2731-9814. DOI: 10.1038/s44168- 025- 00232- 7. URL: http://dx.doi.org/10.1038/s44168- 025-00232-7.