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Pile-up mitigation using attention
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DOI：10.1088/2632-2153/ac7198.png)
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Particle production from secondary proton-proton collisions, commonly referred to as pile-up, impair the sensitivity of both new physics searches and precision measurements at large hadron collider (LHC) experiments. We propose a novel algorithm, Puma, for modeling pile-up with the help of deep neural networks based on sparse transformers. These attention mechanisms were developed for natural language processing but have become popular in other applications. In a realistic detector simulation, our method outperforms classical benchmark algorithms for pile-up mitigation in key observables. It provides a perspective for mitigating the effects of pile-up in the high luminosity era of the LHC, where up to 200 proton-proton collisions are expected to occur simultaneously.
Keyword:
LHC
HL-LHC
pile-up
transformers
machine learning
期刊
M
IF:
4.6
论文数:
1.1K
被引数:
3.4K
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