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Improving robustness of jet tagging algorithms with adversarial training
[article]
2022
arXiv
pre-print
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Subsequently, we present an adversarial training strategy that mitigates the impact of such simulated attacks and improves the classifier robustness. ...
We investigate the classifier response to input data with injected mismodelings and probe the vulnerability of flavor tagging algorithms via application of adversarial attacks. ...
We thank Nicolas Frediani for his contributions to the project in context of his Bachelor thesis.
Compliance with ethical standards ...
arXiv:2203.13890v1
fatcat:h3gwt5537nfongsj4c57xkuq3e
Interaction networks for the identification of boosted H→bb¯ decays
2020
Physical Review D
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The interaction network achieves a drastic improvement in the identification performance with respect to state-of-the-art algorithms. ...
The algorithm is trained on simulated samples of realistic LHC collisions, released by the CMS Collaboration on the CERN Open Data Portal. ...
Adversarial training The secondary adversary network is constructed that consists of three hidden layers each with 64 nodes. ...
doi:10.1103/physrevd.102.012010
fatcat:qbny2obwqjarhjqxhkkgvduety
Interaction networks for the identification of boosted H→ bb decays
[article]
2019
arXiv
pre-print
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The interaction network achieves a drastic improvement in the identification performance with respect to state-of-the-art algorithms. ...
The algorithm is trained on simulated samples of accurate LHC collisions, released by the CMS collaboration on the CERN Open Data Portal. ...
The training begins by initializing the weights from the best classifier training. The adversary is then pre-trained for 10 epochs using the Adam algorithm with an initial learning rate of 10 −4 . ...
arXiv:1909.12285v3
fatcat:gbrgymajbnhuhn26hwdlezjscy
Robust Jet Classifiers through Distance Correlation
2020
Physical Review Letters
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We also show the feasibility of regularization with distance correlation for more powerful convolutional neural networks, as well as for the problem of hadronic top tagging. ...
networks but is much simpler and more stable to train. ...
We are grateful to Chris Delitzsch, Steven Schramm, and especially Andreas Sogaard for help with details of the ATLAS decorrelation study. G. ...
doi:10.1103/physrevlett.125.122001
pmid:33016758
fatcat:fxx54bv3qfgm5bxhfyq4d2mu3u
DisCo Fever: Robust Networks Through Distance Correlation
[article]
2020
arXiv
pre-print
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networks but is much simpler to train and has better convergence properties. ...
We also show the feasibility of DisCo regularization for more powerful convolutional neural networks, as well as for the problem of hadronic top tagging. ...
Training with adversary Adversarial training follows the approach outlined in the Introduction, with the adversary attempting to learn the PDF of the mass. ...
arXiv:2001.05310v1
fatcat:obfin7eadvhspictn2bdtkwgia
Decorrelated jet substructure tagging using adversarial neural networks
2017
Physical Review D
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We generalize the adversarial training technique to include a parametric dependence on the signal hypothesis, training a single network that provides optimized, interpolatable decorrelated jet tagging ...
The network is trained using an adversarial strategy, resulting in a tagger that learns to balance classification accuracy with decorrelation. ...
The authors acknowledge useful conversations with Kyle Cranmer, Jesse Thaler, Kevin Bauer, and Dan Guest, helpful comments from Sal Rappoccio, Derek Soeder and Michela Paganini, and are grateful to the ...
doi:10.1103/physrevd.96.074034
fatcat:odefx5vyvbfete3llbwwh2m2a4
Adversarially-trained autoencoders for robust unsupervised new physics searches
2019
Journal of High Energy Physics
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To mitigate their effect on the classifier and to allow for a realistic assessment of the method, we propose to combine the autoencoder with an adversarial neural network to remove its sensitivity to the ...
We quantify its effect and show that one can achieve a robust anomaly detection in resonance-induced tt̅ final states. ...
Acknowledgments MS acknowledges the generous hospitality of Barbara Jaeger and her group at the University of Tuebingen, as well as support of the Humboldt Society, during the completion of parts of this ...
doi:10.1007/jhep10(2019)047
fatcat:gmrqoxldpbbo3aavjampmwczay
Jet Constituents for Deep Neural Network Based Top Quark Tagging
[article]
2017
arXiv
pre-print
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Here, a sequential approach to this task is taken by using an ordered sequence of jet constituents as training inputs. ...
The jet classification method achieves a background rejection of 45 at a 50% efficiency operating point for reconstruction level jets with transverse momentum range of 600 to 2500 GeV and is insensitive ...
[21, 28, 30, 31] have explored the performance of the algorithms top tagging. ...
arXiv:1704.02124v2
fatcat:lgeuibwj25bdvp3wkhsufwfdwq
Meta-learning and data augmentation for mass-generalised jet taggers
[article]
2021
arXiv
pre-print
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Deep neural networks trained for jet tagging are typically specific to a narrow range of transverse momenta or jet masses. ...
The meta-learning algorithms provide only a small improvement in generalisation when combined with this augmentation. ...
Computing resources were provided by the LIEF HPC-GPGPU Facility hosted at the University of Melbourne. This Facility was established with the assistance of LIEF Grant LE170100200. ...
arXiv:2111.06047v2
fatcat:vicbebenirblhgybsp7mwrnujy
Deep Learning and Its Application to LHC Physics
2018
Annual Review of Nuclear and Particle Science
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This review is aimed at the reader who is familiar with high energy physics but not machine learning. ...
the power of deep learning for analysis of LHC data, and discussion of future prospects and concerns. ...
D.W. and D.G. are supported by the Office of Science at the US Department of Energy. ...
doi:10.1146/annurev-nucl-101917-021019
fatcat:4ll2ex624jcutgimi5w7wya2bq
Image-Based Jet Analysis
[article]
2020
arXiv
pre-print
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2020 pre-print arXiv:2012.09719v1 fatcat:splwot2gmjf73mwebueuudhh5m
Image-based jet analysis is built upon the jet image representation of jets that enables a direct connection between high energy physics and the fields of computer vision and deep learning. ...
Through this connection, a wide array of new jet analysis techniques have emerged. ...
(b) ROC curves for quark / gluon jet rejection versus top jet efficiency for jet image based adversarial autoencoders with varying strength of adversarial penalty during training [79] . ...
arXiv:2012.09719v2
fatcat:lkmk52e6gvfevakbvsp2bmavl4
Achieving Model Robustness through Discrete Adversarial Training
[article]
2021
arXiv
pre-print
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Furthermore, online augmentation with search-based attacks justifies the higher training cost, significantly improving robustness on three datasets. ...
While such attacks have been extensively explored for the purpose of evaluating model robustness, their utility for improving robustness has been limited to offline augmentation only. ...
Our online augmentation of adversarial examples (ADVON, yellow circles) significantly improves robust accuracy, but is expensive to train. ...
arXiv:2104.05062v2
fatcat:dh63egxs6fc6bg7zjnpwm47v4y
Fast and accurate simulation of particle detectors using generative adversarial networks
[article]
2018
arXiv
pre-print
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We show that deep neural networks can achieve high-fidelity in this task, while attaining a speed increase of several orders of magnitude with respect to traditional algorithms. ...
In this work we apply this kind of technique to the simulation of particle-detector response to hadronic jets. ...
We strongly support this initiative and believe that it will be crucial to spark developments of new algorithms from which the HEP community as a whole can profit. We thank Dr. M. DonegÃă, Prof. G. ...
arXiv:1805.00850v1
fatcat:z7lwjj4cw5aldip2253sf765vy
AI Safety for High Energy Physics
[article]
2019
arXiv
pre-print
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In addition to providing a pragmatic diagnostic, this work will hopefully begin a dialogue within the community about the robust application of deep learning to experimental analyses. ...
The field of high-energy physics (HEP), along with many scientific disciplines, is currently experiencing a dramatic influx of new methodologies powered by modern machine learning techniques. ...
ACKNOWLEDGMENTS We thank Paul Tipton for constructive feedback since the early stages of this project. We are also grate- ...
arXiv:1910.08606v1
fatcat:qdlnlfmpqjay7nuzn5arlw3gk4
Classification without labels: learning from mixed samples in high energy physics
2017
Journal of High Energy Physics
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training samples. ...
In many applications, however, these models are trained on imperfect simulations due to a lack of truth-level information in the data, which risks the model learning artifacts of the simulation. ...
This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) ...
doi:10.1007/jhep10(2017)174
fatcat:cukk3mxxgfckxdsgjlehbp5rtq
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