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OASIS: Optimal Analysis-Specific Importance Sampling for event generation
by Konstantin T. Matchev, Prasanth Shyamsundar
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Submission summary
| Authors (as registered SciPost users): | Konstantin Matchev · Prasanth Shyamsundar |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/2006.16972v2 (pdf) |
| Code repository: | https://gitlab.com/prasanthcakewalk/code-and-data-availability/-/tree/master/arXiv_2006.16972 |
| Date accepted: | 2021-02-02 |
| Date submitted: | 2020-12-28 17:55 |
| Submitted by: | Shyamsundar, Prasanth |
| Submitted to: | SciPost Physics |
| Ontological classification | |
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| Academic field: | Physics |
| Specialties: |
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| Approach: | Phenomenological |
Abstract
We propose a technique called Optimal Analysis-Specific Importance Sampling (OASIS) to reduce the number of simulated events required for a high-energy experimental analysis to reach a target sensitivity. We provide recipes to obtain the optimal sampling distributions which preferentially focus the event generation on the regions of phase space with high utility to the experimental analyses. OASIS leads to a conservation of resources at all stages of the Monte Carlo pipeline, including full-detector simulation, and is complementary to approaches which seek to speed-up the simulation pipeline.
List of changes
1. The conclusions sections has been completely rewritten, to incorporate the feedback from both referees.
2. For clarity of the presentation, we have expanded the notation and related definitions pertaining to equation 63 and onwards.
3. In response to referee 2's query, we have expanded the first and second bullets in section 3.2.2.
4. To address referee 1's suggestion, we have we expanded the text above eq. (63), discussing the different contributions to $\sigma_\mathrm{syst}$.
Published as SciPost Phys. 10, 034 (2021)