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MACK: Mismodeling Addressed with Contrastive Knowledge
Authors:
Liam Rankin Sheldon,
Dylan Sheldon Rankin,
Philip Harris
Abstract:
The use of machine learning methods in high energy physics typically relies on large volumes of precise simulation for training. As machine learning models become more complex they can become increasingly sensitive to differences between this simulation and the real data collected by experiments. We present a generic methodology based on contrastive learning which is able to greatly mitigate this…
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The use of machine learning methods in high energy physics typically relies on large volumes of precise simulation for training. As machine learning models become more complex they can become increasingly sensitive to differences between this simulation and the real data collected by experiments. We present a generic methodology based on contrastive learning which is able to greatly mitigate this negative effect. Crucially, the method does not require prior knowledge of the specifics of the mismodeling. While we demonstrate the efficacy of this technique using the task of jet-tagging at the Large Hadron Collider, it is applicable to a wide array of different tasks both in and out of the field of high energy physics.
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Submitted 17 October, 2024;
originally announced October 2024.
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The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
Authors:
Gregor Kasieczka,
Benjamin Nachman,
David Shih,
Oz Amram,
Anders Andreassen,
Kees Benkendorfer,
Blaz Bortolato,
Gustaaf Brooijmans,
Florencia Canelli,
Jack H. Collins,
Biwei Dai,
Felipe F. De Freitas,
Barry M. Dillon,
Ioan-Mihail Dinu,
Zhongtian Dong,
Julien Donini,
Javier Duarte,
D. A. Faroughy,
Julia Gonski,
Philip Harris,
Alan Kahn,
Jernej F. Kamenik,
Charanjit K. Khosa,
Patrick Komiske,
Luc Le Pottier
, et al. (22 additional authors not shown)
Abstract:
A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a…
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A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders.
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Submitted 20 January, 2021;
originally announced January 2021.
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Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge
Authors:
Sang Eon Park,
Dylan Rankin,
Silviu-Marian Udrescu,
Mikaeel Yunus,
Philip Harris
Abstract:
Discoveries of new phenomena often involve a dedicated search for a hypothetical physics signature. Recently, novel deep learning techniques have emerged for anomaly detection in the absence of a signal prior. However, by ignoring signal priors, the sensitivity of these approaches is significantly reduced. We present a new strategy dubbed Quasi Anomalous Knowledge (QUAK), whereby we introduce alte…
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Discoveries of new phenomena often involve a dedicated search for a hypothetical physics signature. Recently, novel deep learning techniques have emerged for anomaly detection in the absence of a signal prior. However, by ignoring signal priors, the sensitivity of these approaches is significantly reduced. We present a new strategy dubbed Quasi Anomalous Knowledge (QUAK), whereby we introduce alternative signal priors that capture some of the salient features of new physics signatures, allowing for the recovery of sensitivity even when the alternative signal is incorrect. This approach can be applied to a broad range of physics models and neural network architectures. In this paper, we apply QUAK to anomaly detection of new physics events at the CERN Large Hadron Collider utilizing variational autoencoders with normalizing flow.
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Submitted 11 June, 2021; v1 submitted 6 November, 2020;
originally announced November 2020.
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An approach to constraining the Higgs width at the LHC and HL-LHC
Authors:
Philip Coleman Harris,
Dylan Sheldon Rankin,
Cristina Mantilla Suarez
Abstract:
Despite the discovery of the Higgs boson decay in five separate channels many parameters of the Higgs boson remain largely unconstrained. In this paper, we present a new approach to constraining the Higgs total width by requiring the Higgs to be resolved as a single high p$_T$ jet and measuring the inclusive Higgs boson cross section. To measure the inclusive Higgs boson cross section, we rely on…
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Despite the discovery of the Higgs boson decay in five separate channels many parameters of the Higgs boson remain largely unconstrained. In this paper, we present a new approach to constraining the Higgs total width by requiring the Higgs to be resolved as a single high p$_T$ jet and measuring the inclusive Higgs boson cross section. To measure the inclusive Higgs boson cross section, we rely on new approaches from machine learning and a modified jet reconstruction. This approach is found to be complementary to the existing off-shell width measurement and, with the full HL-LHC luminosity, is capable of yielding similar sensitivity to the off-shell projections. We outline the theoretical and experimental limitations and present a path towards making this approach a truly model-independent measurement of the Higgs boson total width.
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Submitted 4 October, 2019;
originally announced October 2019.