What's anomalous in LHC jets?

Buss, Thorsten; Dillon, Barry M.; Finke, Thorben; Krämer, Michael; Morandini, Alessandro; Mück, Alexander; Oleksiyuk, Ivan; Plehn, Tilman

doi:10.21468/SciPostPhys.15.4.168

SciPost Physics

What's anomalous in LHC jets?

Thorsten Buss, Barry M. Dillon, Thorben Finke, Michael Krämer, Alessandro Morandini, Alexander Mück, Ivan Oleksiyuk, Tilman Plehn

SciPost Phys. 15, 168 (2023) · published 17 October 2023

doi: 10.21468/SciPostPhys.15.4.168
pdf
Submissions/Reports

Abstract

Searches for anomalies are a significant motivation for the LHC and help define key analysis steps, including triggers. We discuss specific examples how LHC anomalies can be defined through probability density estimates, evaluated in a physics space or in an appropriate neural network latent space, and discuss the model-dependence in choosing an appropriate data parameterisation. We illustrate this for classical k-means clustering, a Dirichlet variational autoencoder, and invertible neural networks. For two especially challenging scenarios of jets from a dark sector we evaluate the strengths and limitations of each method.

TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhys.15.4.168
TI  - What's anomalous in LHC jets?
PY  - 2023/10/17
UR  - https://www.scipost.org/SciPostPhys.15.4.168
JF  - SciPost Physics
JA  - SciPost Phys.
VL  - 15
IS  - 4
SP  - 168
A1  - Buss, Thorsten
AU  - Dillon, Barry M.
AU  - Finke, Thorben
AU  - Krämer, Michael
AU  - Morandini, Alessandro
AU  - Mück, Alexander
AU  - Oleksiyuk, Ivan
AU  - Plehn, Tilman
AB  - Searches for anomalies are a significant motivation for the LHC and help define key analysis steps, including triggers. We discuss specific examples how LHC anomalies can be defined through probability density estimates, evaluated in a physics space or in an appropriate neural network latent space, and discuss the model-dependence in choosing an appropriate data parameterisation. We illustrate this for classical k-means clustering, a Dirichlet variational autoencoder, and invertible neural networks. For two especially challenging scenarios of jets from a dark sector we evaluate the strengths and limitations of each method.
ER  -

@Article{10.21468/SciPostPhys.15.4.168,
	title={{What's anomalous in LHC jets?}},
	author={Thorsten Buss and Barry M. Dillon and Thorben Finke and Michael Krämer and Alessandro Morandini and Alexander Mück and Ivan Oleksiyuk and Tilman Plehn},
	journal={SciPost Phys.},
	volume={15},
	pages={168},
	year={2023},
	publisher={SciPost},
	doi={10.21468/SciPostPhys.15.4.168},
	url={https://scipost.org/10.21468/SciPostPhys.15.4.168},
}

Cited by 7

Authors / Affiliations: mappings to Contributors and Organizations

See all Organizations.

¹ Thorsten Buss,
¹ Barry M. Dillon,
² Thorben Finke,
² Michael Krämer,
² Alessandro Morandini,
² Alexander Mück,
² Ivan Oleksiyuk,
¹ Tilman Plehn

Funder for the research work leading to this publication

Deutsche Forschungsgemeinschaft / German Research FoundationDeutsche Forschungsgemeinschaft [DFG]