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Variational Autoencoders for New Physics Mining at the Large Hadron Collider

Cerri, Olmo and Nguyen, Thong Q. and Pierini, Maurizio and Spiropulu, Maria and Vlimant, Jean-Roch (2019) Variational Autoencoders for New Physics Mining at the Large Hadron Collider. Journal of High Energy Physics, 2019 (5). Art. No. 36. ISSN 1126-6708. http://resolver.caltech.edu/CaltechAUTHORS:20190122-133649525

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Abstract

Using variational autoencoders trained on known physics processes, we develop a one-sided threshold test to isolate previously unseen processes as outlier events. Since the autoencoder training does not depend on any specific new physics signature, the proposed procedure doesn’t make specific assumptions on the nature of new physics. An event selection based on this algorithm would be complementary to classic LHC searches, typically based on model-dependent hypothesis testing. Such an algorithm would deliver a list of anomalous events, that the experimental collaborations could further scrutinize and even release as a catalog, similarly to what is typically done in other scientific domains. Event topologies repeating in this dataset could inspire new-physics model building and new experimental searches. Running in the trigger system of the LHC experiments, such an application could identify anomalous events that would be otherwise lost, extending the scientific reach of the LHC.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1007/JHEP05(2019)036DOIArticle
https://arxiv.org/abs/1811.10276arXivDiscussion Paper
ORCID:
AuthorORCID
Spiropulu, Maria0000-0001-8172-7081
Additional Information:© 2019 The Authors. 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) and source are credited. Article funded by SCOAP3. Received: December 6, 2018; Revised: February 18, 2019; Accepted: April 18, 2019; Published: May 7, 2019. We thank D. Rezende for his precious suggestions, which motivated us to explore Variational Autoencoders for this work. This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement no 772369) and the United States Department of Energy, Office of High Energy Physics Research under Caltech Contract No. DE-SC0011925. This work was conducted at "iBanks", the AI GPU cluster at Caltech. We acknowledge NVIDIA, SuperMicro and the Kavli Foundation for their support of "iBanks".
Group:CMS@Caltech
Funders:
Funding AgencyGrant Number
European Research Council (ERC)772369
Department of Energy (DOE)DE-SC0011925
SCOAP3UNSPECIFIED
Subject Keywords:Beyond Standard Model; Hadron-Hadron scattering (experiments)
Record Number:CaltechAUTHORS:20190122-133649525
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20190122-133649525
Official Citation:Cerri, O., Nguyen, T.Q., Pierini, M. et al. J. High Energ. Phys. (2019) 2019: 36. https://doi.org/10.1007/JHEP05(2019)036
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:92401
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:22 Jan 2019 21:46
Last Modified:09 May 2019 16:07

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