Toward Model-Agnostic Detection of New Physics Using Data-Driven Signal Regions
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910738710265856 |
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| author | Yi, Soheun Alison, John Kuusela, Mikael |
| author_facet | Yi, Soheun Alison, John Kuusela, Mikael |
| contents | In the search for new particles in high-energy physics, it is crucial to select the Signal Region (SR) in such a way that it is enriched with signal events if they are present. While most existing search methods set the region relying on prior domain knowledge, it may be unavailable for a completely novel particle that falls outside the current scope of understanding. We address this issue by proposing a method built upon a model-agnostic but often realistic assumption about the localized topology of the signal events, in which they are concentrated in a certain area of the feature space. Considering the signal component as a localized high-frequency feature, our approach employs the notion of a low-pass filter. We define the SR as an area which is most affected when the observed events are smeared with additive random noise. We overcome challenges in density estimation in the high-dimensional feature space by learning the density ratio of events that potentially include a signal to the complementary observation of events that closely resemble the target events but are free of any signals. By applying our method to simulated $\mathrm{HH} \rightarrow 4b$ events, we demonstrate that the method can efficiently identify a data-driven SR in a high-dimensional feature space in which a high portion of signal events concentrate. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_06960 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Toward Model-Agnostic Detection of New Physics Using Data-Driven Signal Regions Yi, Soheun Alison, John Kuusela, Mikael Machine Learning Data Analysis, Statistics and Probability Applications In the search for new particles in high-energy physics, it is crucial to select the Signal Region (SR) in such a way that it is enriched with signal events if they are present. While most existing search methods set the region relying on prior domain knowledge, it may be unavailable for a completely novel particle that falls outside the current scope of understanding. We address this issue by proposing a method built upon a model-agnostic but often realistic assumption about the localized topology of the signal events, in which they are concentrated in a certain area of the feature space. Considering the signal component as a localized high-frequency feature, our approach employs the notion of a low-pass filter. We define the SR as an area which is most affected when the observed events are smeared with additive random noise. We overcome challenges in density estimation in the high-dimensional feature space by learning the density ratio of events that potentially include a signal to the complementary observation of events that closely resemble the target events but are free of any signals. By applying our method to simulated $\mathrm{HH} \rightarrow 4b$ events, we demonstrate that the method can efficiently identify a data-driven SR in a high-dimensional feature space in which a high portion of signal events concentrate. |
| title | Toward Model-Agnostic Detection of New Physics Using Data-Driven Signal Regions |
| topic | Machine Learning Data Analysis, Statistics and Probability Applications |
| url | https://arxiv.org/abs/2409.06960 |