SciPost Physics

QCD or what?

Theo Heimel, Gregor Kasieczka, Tilman Plehn, Jennifer M Thompson

SciPost Phys. 6, 030 (2019) · published 8 March 2019

• doi: 10.21468/SciPostPhys.6.3.030
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Abstract

Autoencoder networks, trained only on QCD jets, can be used to search for anomalies in jet-substructure. We show how, based either on images or on 4-vectors, they identify jets from decays of arbitrary heavy resonances. To control the backgrounds and the underlying systematics we can de-correlate the jet mass using an adversarial network. Such an adversarial autoencoder allows for a general and at the same time easily controllable search for new physics. Ideally, it can be trained and applied to data in the same phase space region, allowing us to efficiently search for new physics using un-supervised learning.

• J.A. Aguilar-Saavedra et al., The minimal stealth boson: models and benchmarks
  J. High Energ. Phys. 2019, 237 (2019) [Crossref]
• Patrick T. Komiske et al., Exploring the space of jets with CMS open data
  Phys. Rev. D 101, 034009 (2020) [Crossref]
• Christopher W. Murphy, Class imbalance techniques for high energy physics
  SciPost Phys. 7, 076 (2019) [Crossref]
• Barry M. Dillon et al., Uncovering latent jet substructure
  Phys. Rev. D 100, 056002 (2019) [Crossref]
• Andrew J. Larkoski et al., A theory of quark vs. gluon discrimination
  J. High Energ. Phys. 2019, 14 (2019) [Crossref]
• Layne Bradshaw et al., Mass agnostic jet taggers
  SciPost Phys. 8, 011 (2020) [Crossref]
• Patrick T. Komiske et al., Metric Space of Collider Events
  Phys. Rev. Lett. 123, 041801 (2019) [Crossref]
• Olmo Cerri et al., Variational autoencoders for new physics mining at the Large Hadron Collider
  J. High Energ. Phys. 2019, 36 (2019) [Crossref]
• Gregor Kasieczka et al., Quark-gluon tagging: Machine learning vs detector
  SciPost Phys. 6, 069 (2019) [Crossref]
• Sascha Diefenbacher et al., CapsNets continuing the convolutional quest
  SciPost Phys. 8, 023 (2020) [Crossref]
• Andrew Blance et al., Adversarially-trained autoencoders for robust unsupervised new physics searches
  J. High Energ. Phys. 2019, 47 (2019) [Crossref]
• Sven Bollweg et al., Deep-learning jets with uncertainties and more
  SciPost Phys. 8, 006 (2020) [Crossref]
• Kaustuv Datta et al., Automating the construction of jet observables with machine learning
  Phys. Rev. D 100, 095016 (2019) [Crossref]
• Hugues Beauchesne et al., Searching for periodic signals in kinematic distributions using continuous wavelet transforms
  Eur. Phys. J. C 80, 192 (2020) [Crossref]
• Andrew J. Larkoski et al., Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning
  Physics Reports 841, 1 (2020) [Crossref]
• Amit Chakraborty et al., Interpretable deep learning for two-prong jet classification with jet spectra
  J. High Energ. Phys. 2019, 135 (2019) [Crossref]
• Biplob Bhattacherjee et al., Study of energy deposition patterns in hadron calorimeter for prompt and displaced jets using convolutional neural network
  J. High Energ. Phys. 2019, 156 (2019) [Crossref]
• Gregor Kasieczka et al., The Machine Learning landscape of top taggers
  SciPost Phys. 7, 014 (2019) [Crossref]