ADFilter—A Web Tool for New Physics Searches with Autoencoder-Based Anomaly Detection Using Deep Unsupervised Neural Networks
Abstract
:1. Introduction
2. Description of ADFilter
- A ROOT file with the inputs for AE and a few test histograms. This ROOT file’s name ends with the string “rmm.root”. It contains basic kinematic distributions, the histogram “cross” with the observed cross-section (in pb), the Rapidity–Mass Matrix (RMM) [10] for the first 50 events (for demonstration purposes), and the ROOT tree “inputNN”, which stores non-zero values for the RMM and their indices.
- A ROOT file with the final result. The name of this ROOT file contains the substring “ADFilter”. It includes a histogram called “Loss”, representing the numerical value of the reconstruction loss after processing through the AE. This histogram shows the success of the encoder–decoder process in reconstructing the compressed input. The “EventFlow” histogram shows the number of events entering the AE and the number of output events that exceed the “LossCut” value, which is typically defined in the relevant publications. The output ROOT file also includes a set of histograms showing invariant masses before and after the AE, as well as the cross-section in the selected anomalous region.
- A text file that contains information about all processing steps. This file has the extension “.log”. It can be used to monitor and verify each step of data processing, from the file with the input variables to the final file with the loss distribution. It also prints the selection cuts used for event processing.
3. Technical Details on Input Files and Event Processing
3.1. Delphes Input Files
3.2. Truth-Level Event Record
3.3. LHE Parton-Level Files
3.4. Object Reconstruction Step
3.5. Pre-Processing Step
3.6. Autoencoder Step
4. Real-Life Examples
4.1. Re-Interpretation of Sequential-Standard Model Limits
4.2. Re-Interpretation of Charged Higgs Limits
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Example of the Input Data Structure
Appendix B. Alternative Representation of Limits
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Chekanov, S.V.; Islam, W.; Zhang, R.; Luongo, N. ADFilter—A Web Tool for New Physics Searches with Autoencoder-Based Anomaly Detection Using Deep Unsupervised Neural Networks. Information 2025, 16, 258. https://doi.org/10.3390/info16040258
Chekanov SV, Islam W, Zhang R, Luongo N. ADFilter—A Web Tool for New Physics Searches with Autoencoder-Based Anomaly Detection Using Deep Unsupervised Neural Networks. Information. 2025; 16(4):258. https://doi.org/10.3390/info16040258
Chicago/Turabian StyleChekanov, Sergei V., Wasikul Islam, Rui Zhang, and Nicholas Luongo. 2025. "ADFilter—A Web Tool for New Physics Searches with Autoencoder-Based Anomaly Detection Using Deep Unsupervised Neural Networks" Information 16, no. 4: 258. https://doi.org/10.3390/info16040258
APA StyleChekanov, S. V., Islam, W., Zhang, R., & Luongo, N. (2025). ADFilter—A Web Tool for New Physics Searches with Autoencoder-Based Anomaly Detection Using Deep Unsupervised Neural Networks. Information, 16(4), 258. https://doi.org/10.3390/info16040258