Automating the Construction of Jet Observables with Machine Learning
| Autor: | Kaustuv Datta, Andrew J. Larkoski, Benjamin Philip Nachman |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Physics
Particle physics Large Hadron Collider Jet (mathematics) 010308 nuclear & particles physics FOS: Physical sciences Observable Computer Science::Digital Libraries 01 natural sciences Standard Model High Energy Physics - Experiment High Energy Physics - Phenomenology High Energy Physics - Experiment (hep-ex) High Energy Physics - Phenomenology (hep-ph) Simple (abstract algebra) Robustness (computer science) Phase space Product (mathematics) 0103 physical sciences 010306 general physics |
| Zdroj: | Physical Review D, vol 100, iss 9 Physical Review D, 100 (9) Physical Review |
| ISSN: | 1550-7998 0556-2821 1550-2368 |
| DOI: | 10.48550/arxiv.1902.07180 |
| Popis: | Machine-learning assisted jet substructure tagging techniques have the potential to significantly improve searches for new particles and Standard Model measurements in hadronic final states. Techniques with simple analytic forms are particularly useful for establishing robustness and gaining physical insight. We introduce procedures to automate the construction of a large class of observables that are chosen to completely specify M-body phase space. The procedures are validated on the task of distinguishing H→b¯b from g→b¯b, where M=3 and previous brute-force approaches to construct an optimal product observable for the M-body phase space have established the baseline performance. We then use the new methods to design tailored observables for the boosted Z′ search, where M=4 and brute-force methods are intractable. The new classifiers outperform standard two-prong tagging observables, illustrating the power of the new optimization method for improving searches and measurement at the LHC and beyond. Physical Review D, 100 (9) ISSN:1550-7998 ISSN:0556-2821 ISSN:1550-2368 |
| Databáze: | OpenAIRE |
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