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Searching for new physics with deep autoencoders

Journal Article · · Physical Review D
Not Available
Research Organization:
Rutgers Univ., Piscataway, NJ (United States)
Sponsoring Organization:
National Science Foundation (NSF); USDOE; USDOE Office of Science (SC)
Grant/Contract Number:
SC0010008
OSTI ID:
1618141
Alternate ID(s):
OSTI ID: 1802023
Journal Information:
Physical Review D, Journal Name: Physical Review D Journal Issue: 7 Vol. 101; ISSN PRVDAQ; ISSN 2470-0010
Publisher:
American Physical SocietyCopyright Statement
Country of Publication:
United States
Language:
English

References (40)

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Pulling out all the tops with computer vision and deep learning journal October 2018
Spectral analysis of jet substructure with neural networks: boosted Higgs case journal October 2018
A generic anti-QCD jet tagger journal November 2017
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Detector Monitoring with Artificial Neural Networks at the CMS Experiment at the CERN Large Hadron Collider journal January 2019
Topology Classification with Deep Learning to Improve Real-Time Event Selection at the LHC journal August 2019
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Parton shower uncertainties in jet substructure analyses with deep neural networks journal January 2017
What is the machine learning? journal March 2018
Deep learning for R -parity violating supersymmetry searches at the LHC journal October 2018
Learning new physics from a machine journal January 2019
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Anomaly Detection for Resonant New Physics with Machine Learning journal December 2018
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Guiding new physics searches with unsupervised learning journal March 2019
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Deep Learning and Its Application to LHC Physics journal October 2018
Deep-learned Top Tagging with a Lorentz Layer journal January 2018
QCD or what? journal January 2019

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