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We describe the outcome of a data challenge conducted as part of the Dark Machines (https://www.darkmachines.org) initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims to detect signals of new physics at the Large Hadron Collider (LHC) using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of >1 billion simulated LHC events corresponding to 10\, fb^{-1} 10 f b − 1 of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata.org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge.
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The upcoming CTEQ-TEA parton distributions in a nutshell
We review recent studies by the CTEQ-TEA group toward the development of a new generation of precision parton distribution functions in the nucleon for advanced studies at the high-luminosity LHC and in other experiments. Among several ongoing e!orts, we examine sensitivity to the PDFs and mutual compatibility of new measurements in production of Drell-Yan pairs, top-quark pairs, and single-inclusive jets by the ATLAS, CMS, and LHCb collaborations in the LHC Runs 1 and 2.
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- PAR ID:
- 10608037
- Publisher / Repository:
- Sissa Medialab
- Date Published:
- Page Range / eLocation ID:
- 057
- Format(s):
- Medium: X
- Location:
- Grenoble, France
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.22323/1.469.0057
