Triggering Dark Showers with Conditional Dual Auto-Encoders
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866914956044140544 |
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| author | Anzalone, Luca Chhibra, Simranjit Singh Maier, Benedikt Chernyavskaya, Nadezda Pierini, Maurizio |
| author_facet | Anzalone, Luca Chhibra, Simranjit Singh Maier, Benedikt Chernyavskaya, Nadezda Pierini, Maurizio |
| contents | We present a family of conditional dual auto-encoders (CoDAEs) for generic and model-independent new physics searches at colliders. New physics signals, which arise from new types of particles and interactions, are considered in our study as anomalies causing deviations in data with respect to expected background events. In this work, we perform a normal-only anomaly detection, which employs only background samples, to search for manifestations of a dark version of strong force applying (variational) auto-encoders on raw detector images, which are large and highly sparse, without leveraging any physics-based pre-processing or strong assumption on the signals. The proposed CoDAE has a dual-encoder design, which is general and can learn an auxiliary yet compact latent space through spatial conditioning, showing a neat improvement over competitive physics-based baselines and related approaches, therefore also reducing the gap with fully supervised models. It is the first time an unsupervised model is shown to exhibit excellent discrimination against multiple dark shower models, illustrating the suitability of this method as an accurate, fast, model-independent algorithm to deploy, e.g., in the real-time event triggering systems of Large Hadron Collider experiments such as ATLAS and CMS. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2306_12955 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Triggering Dark Showers with Conditional Dual Auto-Encoders Anzalone, Luca Chhibra, Simranjit Singh Maier, Benedikt Chernyavskaya, Nadezda Pierini, Maurizio High Energy Physics - Experiment Machine Learning We present a family of conditional dual auto-encoders (CoDAEs) for generic and model-independent new physics searches at colliders. New physics signals, which arise from new types of particles and interactions, are considered in our study as anomalies causing deviations in data with respect to expected background events. In this work, we perform a normal-only anomaly detection, which employs only background samples, to search for manifestations of a dark version of strong force applying (variational) auto-encoders on raw detector images, which are large and highly sparse, without leveraging any physics-based pre-processing or strong assumption on the signals. The proposed CoDAE has a dual-encoder design, which is general and can learn an auxiliary yet compact latent space through spatial conditioning, showing a neat improvement over competitive physics-based baselines and related approaches, therefore also reducing the gap with fully supervised models. It is the first time an unsupervised model is shown to exhibit excellent discrimination against multiple dark shower models, illustrating the suitability of this method as an accurate, fast, model-independent algorithm to deploy, e.g., in the real-time event triggering systems of Large Hadron Collider experiments such as ATLAS and CMS. |
| title | Triggering Dark Showers with Conditional Dual Auto-Encoders |
| topic | High Energy Physics - Experiment Machine Learning |
| url | https://arxiv.org/abs/2306.12955 |