Complete Optimal Non-Resonant Anomaly Detection
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917636875485184 |
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| author | Kasieczka, Gregor Raine, John Andrew Shih, David Upadhyay, Aman |
| author_facet | Kasieczka, Gregor Raine, John Andrew Shih, David Upadhyay, Aman |
| contents | We propose the first-ever complete, model-agnostic search strategy based on the optimal anomaly score, for new physics on the tails of distributions. Signal sensitivity is achieved via a classifier trained on auxiliary features in a weakly-supervised fashion, and backgrounds are predicted using the ABCD method in the classifier output and the primary tail feature. The independence between the classifier output and the tail feature required for ABCD is achieved by first training a conditional normalizing flow that yields a decorrelated version of the auxiliary features; the classifier is then trained on these features. Both the signal sensitivity and background prediction require a sample of events accurately approximating the SM background; we assume this can be furnished by closely related control processes in the data or by accurate simulations, as is the case in countless conventional analyses. The viability of our approach is demonstrated for signatures consisting of (mono)jets and missing transverse energy, where the main SM background is $Z(νν) +\text{jets}$, and the data-driven control process is $γ+\text{jets}$. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_07258 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Complete Optimal Non-Resonant Anomaly Detection Kasieczka, Gregor Raine, John Andrew Shih, David Upadhyay, Aman High Energy Physics - Phenomenology High Energy Physics - Experiment Data Analysis, Statistics and Probability We propose the first-ever complete, model-agnostic search strategy based on the optimal anomaly score, for new physics on the tails of distributions. Signal sensitivity is achieved via a classifier trained on auxiliary features in a weakly-supervised fashion, and backgrounds are predicted using the ABCD method in the classifier output and the primary tail feature. The independence between the classifier output and the tail feature required for ABCD is achieved by first training a conditional normalizing flow that yields a decorrelated version of the auxiliary features; the classifier is then trained on these features. Both the signal sensitivity and background prediction require a sample of events accurately approximating the SM background; we assume this can be furnished by closely related control processes in the data or by accurate simulations, as is the case in countless conventional analyses. The viability of our approach is demonstrated for signatures consisting of (mono)jets and missing transverse energy, where the main SM background is $Z(νν) +\text{jets}$, and the data-driven control process is $γ+\text{jets}$. |
| title | Complete Optimal Non-Resonant Anomaly Detection |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2404.07258 |