ADFilter—A Web Tool for New Physics Searches with Autoencoder-Based Anomaly Detection Using Deep Unsupervised Neural Networks Article Swipe
S. Chekanov
,
W. Islam
,
R. Zhang
,
N. A. Luongo
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/info16040258
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/info16040258
A web-based tool called ADFilter (short for Anomaly Detection Filter) was developed to process collision events using autoencoders based on a deep unsupervised neural network. The autoencoders are trained on a small fraction of either collision data or Standard Model (SM) Monte Carlo simulations. The tool calculates loss distributions for input events, helping to determine the degree to which the events can be considered anomalous with respect to the SM events used for training. Therefore, it can be used for new physics searches in collider experiments. Real-life examples are provided to demonstrate how the tool can be used to reinterpret existing results from the Large Hadron Collider (LHC), with the goal of significantly improving exclusion limits. This tool is expected to mitigate the “reproducibility crisis” associated with various machine learning techniques, as it can incorporate machine learning approaches from third-party publications, making them accessible to the general public.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/info16040258
- https://www.mdpi.com/2078-2489/16/4/258/pdf?version=1742631326
- OA Status
- gold
- Cited By
- 1
- References
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- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4408774938Canonical identifier for this work in OpenAlex
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https://doi.org/10.3390/info16040258Digital Object Identifier
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ADFilter—A Web Tool for New Physics Searches with Autoencoder-Based Anomaly Detection Using Deep Unsupervised Neural NetworksWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-03-22Full publication date if available
- Authors
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S. Chekanov, W. Islam, R. Zhang, N. A. LuongoList of authors in order
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https://doi.org/10.3390/info16040258Publisher landing page
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https://www.mdpi.com/2078-2489/16/4/258/pdf?version=1742631326Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2078-2489/16/4/258/pdf?version=1742631326Direct OA link when available
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Autoencoder, Anomaly detection, Artificial neural network, Anomaly (physics), Artificial intelligence, Unsupervised learning, Deep neural networks, Computer science, Pattern recognition (psychology), Physics, Condensed matter physicsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.from | 102, 138 |
| abstract_inverted_index.goal | 110 |
| abstract_inverted_index.loss | 47 |
| abstract_inverted_index.them | 142 |
| abstract_inverted_index.tool | 2, 45, 94, 117 |
| abstract_inverted_index.used | 71, 78, 97 |
| abstract_inverted_index.with | 65, 108, 126 |
| abstract_inverted_index.Carlo | 42 |
| abstract_inverted_index.Large | 104 |
| abstract_inverted_index.Model | 39 |
| abstract_inverted_index.Monte | 41 |
| abstract_inverted_index.based | 18 |
| abstract_inverted_index.input | 50 |
| abstract_inverted_index.small | 31 |
| abstract_inverted_index.using | 16 |
| abstract_inverted_index.which | 58 |
| abstract_inverted_index.(LHC), | 107 |
| abstract_inverted_index.(short | 5 |
| abstract_inverted_index.Hadron | 105 |
| abstract_inverted_index.called | 3 |
| abstract_inverted_index.degree | 56 |
| abstract_inverted_index.either | 34 |
| abstract_inverted_index.events | 15, 60, 70 |
| abstract_inverted_index.making | 141 |
| abstract_inverted_index.neural | 23 |
| abstract_inverted_index.Anomaly | 7 |
| abstract_inverted_index.Filter) | 9 |
| abstract_inverted_index.events, | 51 |
| abstract_inverted_index.general | 146 |
| abstract_inverted_index.helping | 52 |
| abstract_inverted_index.limits. | 115 |
| abstract_inverted_index.machine | 128, 135 |
| abstract_inverted_index.physics | 81 |
| abstract_inverted_index.process | 13 |
| abstract_inverted_index.public. | 147 |
| abstract_inverted_index.respect | 66 |
| abstract_inverted_index.results | 101 |
| abstract_inverted_index.trained | 28 |
| abstract_inverted_index.various | 127 |
| abstract_inverted_index.ADFilter | 4 |
| abstract_inverted_index.Collider | 106 |
| abstract_inverted_index.Standard | 38 |
| abstract_inverted_index.collider | 84 |
| abstract_inverted_index.examples | 87 |
| abstract_inverted_index.existing | 100 |
| abstract_inverted_index.expected | 119 |
| abstract_inverted_index.fraction | 32 |
| abstract_inverted_index.learning | 129, 136 |
| abstract_inverted_index.mitigate | 121 |
| abstract_inverted_index.network. | 24 |
| abstract_inverted_index.provided | 89 |
| abstract_inverted_index.searches | 82 |
| abstract_inverted_index.Detection | 8 |
| abstract_inverted_index.Real-life | 86 |
| abstract_inverted_index.anomalous | 64 |
| abstract_inverted_index.collision | 14, 35 |
| abstract_inverted_index.crisis” | 124 |
| abstract_inverted_index.determine | 54 |
| abstract_inverted_index.developed | 11 |
| abstract_inverted_index.exclusion | 114 |
| abstract_inverted_index.improving | 113 |
| abstract_inverted_index.training. | 73 |
| abstract_inverted_index.web-based | 1 |
| abstract_inverted_index.Therefore, | 74 |
| abstract_inverted_index.accessible | 143 |
| abstract_inverted_index.approaches | 137 |
| abstract_inverted_index.associated | 125 |
| abstract_inverted_index.calculates | 46 |
| abstract_inverted_index.considered | 63 |
| abstract_inverted_index.demonstrate | 91 |
| abstract_inverted_index.incorporate | 134 |
| abstract_inverted_index.reinterpret | 99 |
| abstract_inverted_index.techniques, | 130 |
| abstract_inverted_index.third-party | 139 |
| abstract_inverted_index.autoencoders | 17, 26 |
| abstract_inverted_index.experiments. | 85 |
| abstract_inverted_index.simulations. | 43 |
| abstract_inverted_index.unsupervised | 22 |
| abstract_inverted_index.distributions | 48 |
| abstract_inverted_index.publications, | 140 |
| abstract_inverted_index.significantly | 112 |
| abstract_inverted_index.“reproducibility | 123 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5040574546 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I1282105669 |
| citation_normalized_percentile.value | 0.89581011 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |