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The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider
/ Aarrestad, Thea (CERN) ; van Beekveld, Melissa (Oxford U., Theor. Phys.) ; Bona, Marcella (Queen Mary, U. of London) ; Boveia, Antonio (Ohio State U.) ; Caron, Sascha (Nikhef, Amsterdam) ; Davies, Joe (Queen Mary, U. of London) ; de Simone, Andrea (SISSA, Trieste ; INFN, Trieste) ; Doglioni, Caterina (Lund U.) ; Duarte, Javier (UC, San Diego) ; Farbin, Amir (Texas U., Arlington) et al.
We describe the outcome of a data challenge conducted as part of the Dark Machines Initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims at detecting signals of new physics at the LHC using unsupervised machine learning algorithms. [...]
arXiv:2105.14027; FERMILAB-PUB-21-285-CMS.-
2022-01-28 - 57 p.
- Published in : SciPost Phys. 12 (2022) 043
Fulltext: 2105.14027 - PDF; fermilab-pub-21-285-cms - PDF; Fulltext from Publisher: PDF; External link: Fermilab Library Server
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Searching for long-lived particles beyond the Standard Model at the Large Hadron Collider
/ Alimena, Juliette (Ohio State U.) ; Beacham, James (Duke U.) ; Borsato, Martino (Heidelberg U.) ; Cheng, Yangyang (Cornell U., LNS) ; Cid Vidal, Xabier (Santiago de Compostela U.) ; Cottin, Giovanna (Adolfo Ibanez U. ; Chile U., Catolica ; Taiwan, Natl. Taiwan U.) ; De Roeck, Albert (CERN) ; Desai, Nishita (Tata Inst.) ; Curtin, David (Toronto U.) ; Evans, Jared A. (Cincinnati U.) et al.
Particles beyond the Standard Model (SM) can generically have lifetimes that are long compared to SM particles at the weak scale. When produced at experiments such as the Large Hadron Collider (LHC) at CERN, these long-lived particles (LLPs) can decay far from the interaction vertex of the primary proton-proton collision. [...]
arXiv:1903.04497.-
2020-09-08 - 201 p.
- Published in : J. Phys. G 47 (2020) 090501
Fulltext: 1903.04497 - PDF; fermilab-pub-19-110-cd-ppd - PDF; Fulltext from Publisher: PDF; External link: Fermilab Library Server (fulltext available)
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4.
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Guiding New Physics Searches with Unsupervised Learning
/ De Simone, Andrea (speaker) (SISSA)
I will describe an approach to search for new phenomena in data, by detecting discrepancies between two datasets. These could be, for example, a simulated standard-model background, and an observed dataset containing a potential hidden signal of New Physics.
I will propose a new statistical test, built upon a test statistic which measures deviations between two samples, using a Nearest Neighbors approach to estimate the local ratio of the density of points.
The test is model-independent and non-parametric, requiring no knowledge of the shape of the underlying distributions, and it does not bin the data, thus retaining full information from the multidimensional feature space.
As a by-product, the technique is also a useful tool to identify regions of interest for further study.
As a proof-of-concept, I will show the power of the method when applied to synthetic Gaussian data, and to a simulated dark matter signal at the LHC..
2018 - 2828.
Machine Learning; IML Machine Learning Working Group: unsupervised searches and unfolding with ML
External links: Talk details; Event details
In : IML Machine Learning Working Group: unsupervised searches and unfolding with ML
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Machine Learning in High Energy Physics Community White Paper
/ Albertsson, Kim (Lulea U.) ; Altoe, Piero (NVIDIA, Santa Clara) ; Anderson, Dustin (Caltech) ; Anderson, John ; Andrews, Michael (Carnegie Mellon U.) ; Araque Espinosa, Juan Pedro (LIP, Lisbon) ; Aurisano, Adam (Cincinnati U.) ; Basara, Laurent (INFN, Padua ; Padua U.) ; Bevan, Adrian (University Coll. London) ; Bhimji, Wahid (LBL, Berkeley) et al.
Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas for machine learning in particle physics. [...]
arXiv:1807.02876; FERMILAB-PUB-18-318-CD-DI-PPD.-
2018-10-18 - 27 p.
- Published in : J. Phys.: Conf. Ser. 1085 (2018) 022008
Fulltext: Albertsson_2018_J._Phys.__Conf._Ser._1085_022008 - PDF; 1807.02876 - PDF; fermilab-pub-18-318-cd-di-ppd - PDF; fulltext1681439 - PDF; Fulltext from Publisher: PDF; External link: Fermilab Library Server (fulltext available)
In : 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, Seattle, WA, USA, 21 - 25 Aug 2017, pp.022008
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Towards the next generation of simplified Dark Matter models
/ Albert, Andreas (Aachen, Tech. Hochsch.) ; Bauer, Martin (Heidelberg U.) ; Brooke, Jim (Bristol U.) ; Buchmueller, Oliver (Imperial Coll., London) ; Cerdeño, David G. (Durham U.) ; Citron, Matthew (Imperial Coll., London) ; Davies, Gavin (Imperial Coll., London) ; De Cosa, Annapaola (Zurich U.) ; De Roeck, Albert (CERN ; Antwerp U.) ; De Simone, Andrea (INFN, Trieste) et al.
This White Paper is an input to the ongoing discussion about the extension and refinement of simplified Dark Matter (DM) models. Based on two concrete examples, we show how existing simplified DM models (SDMM) can be extended to provide a more accurate and comprehensive framework to interpret and characterise collider searches. [...]
arXiv:1607.06680.-
2017-06 - 22 p.
- Published in : Phys. Dark Universe 16 (2017) 49-70
Fulltext: PDF; External link: Preprint
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7.
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Recommendations on presenting LHC searches for missing transverse energy signals using simplified $s$-channel models of dark matter
/ Boveia, Antonio (Ohio State U.) ; Buchmueller, Oliver (ed.) (Imperial Coll., London) ; Busoni, Giorgio (ARC, CoEPP, Australia) ; D'Eramo, Francesco (UC, Santa Cruz, Inst. Part. Phys. ; UC, Santa Cruz (main)) ; De Roeck, Albert (U. Antwerp (main) ; CERN) ; De Simone, Andrea (SISSA, Trieste ; INFN, Trieste) ; Doglioni, Caterina (ed.) (Lund Inst. Tech.) ; Dolan, Matthew J. (ARC, CoEPP, Australia) ; Genest, Marie-Helene (LPSC, Grenoble) ; Hahn, Kristian (ed.) (Northwestern U.) et al.
This document summarises the proposal of the LHC Dark Matter Working Group on how to present LHC results on $s$-channel simplified dark matter models and to compare them to direct (indirect) detection experiments..
arXiv:1603.04156; CERN-LPCC-2016-001; CERN-LPCC-2016-001.-
2019-09-09 - 9 p.
- Published in : Phys. Dark Univ. 27 (2020) 100365
Fulltext: PDF; Fulltext from Publisher: PDF;
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8.
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Dark Matter Benchmark Models for Early LHC Run-2 Searches: Report of the ATLAS/CMS Dark Matter Forum
/ Abercrombie, Daniel (MIT) ; Akchurin, Nural (Texas Tech.) ; Akilli, Ece (Geneva U.) ; Alcaraz Maestre, Juan (Madrid, CIEMAT) ; Allen, Brandon (MIT) ; Alvarez Gonzalez, Barbara (CERN) ; Andrea, Jeremy (Strasbourg, IPHC) ; Arbey, Alexandre (CERN ; Lyon Observ.) ; Azuelos, Georges (TRIUMF) ; Azzi, Patrizia (INFN, Padua) et al.
This document is the final report of the ATLAS-CMS Dark Matter Forum, a forum organized by the ATLAS and CMS collaborations with the participation of experts on theories of Dark Matter, to select a minimal basis set of dark matter simplified models that should support the design of the early LHC Run-2 searches. A prioritized, compact set of benchmark models is proposed, accompanied by studies of the parameter space of these models and a repository of generator implementations. [...]
arXiv:1507.00966; FERMILAB-PUB-15-282-CD.-
2019-09-13 - 160 p.
- Published in : Phys. Dark Univ. 27 (2020) 100371
Fulltext: arXiv:1507.00966 - PDF; 1507.00966 - PDF; Fulltext from Publisher: PDF; Fulltext from publisher: PDF; External link: Fermilab Library Server (fulltext available)
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9.
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Simplified Models for Dark Matter Searches at the LHC
/ Abdallah, Jalal (Taiwan, Inst. Phys.) ; Araujo, Henrique (Imperial Coll., London) ; Arbey, Alexandre (Lyon U. ; Lyon, Ecole Normale Superieure ; CERN) ; Ashkenazi, Adi (Tel Aviv U.) ; Belyaev, Alexander (Southampton U.) ; Berger, Joshua (SLAC) ; Boehm, Celine (Durham U., IPPP) ; Boveia, Antonio (CERN) ; Brennan, Amelia (Melbourne U.) ; Brooke, Jim (Bristol U.) et al.
This document outlines a set of simplified models for dark matter and its interactions with Standard Model particles. It is intended to summarize the main characteristics that these simplified models have when applied to dark matter searches at the LHC, and to provide a number of useful expressions for reference. [...]
arXiv:1506.03116; FERMILAB-PUB-15-283-CD; CERN-PH-TH-2015-139; CERN-PH-TH-2015-139.-
Geneva : CERN, 2016-05-12 - 16 p.
- Published in : Phys. Dark Universe 9-10 (2015) 8-23
Elsevier Open Access article: PDF; Fulltext: PDF; External link: Fermilab Accepted Manuscript
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