Fetching the paper…
Reading the bibliography…
Deep learning tools can incorporate all of the available information into a search for new particles, thus making the best use of the available data.
1901
Earlier work this paper cites.
1901
Earlier work this paper cites.
1902
Earlier work this paper cites.
A. Butter et al., The Machine Learning Landscape of Top Taggers
1902
Earlier work this paper cites.
H. Qu and L. Gouskos, ParticleNet: Jet Tagging via Particle Clouds
1902
Earlier work this paper cites.
1902
Earlier work this paper cites.
1902
Earlier work this paper cites.
T. S. Roy and A. H. Vijay, A robust anomaly finder based on autoencoder
1903
Earlier work this paper cites.
1903
Earlier work this paper cites.
1903
Earlier work this paper cites.
D. Derkach, N. Kazeev, F. Ratnikov, A. Ustyuzhanin, and A. Volokhova, Cherenkov Detectors Fast Simulation Using Neural Networks · 1903
Earlier work this paper cites.
S. Carrazza and F. A. Dreyer, Jet grooming through reinforcement learning
1903
Earlier work this paper cites.
B. M. Dillon, D. A. Faroughy, and J. F. Kamenik, Uncovering latent jet substructure
1904
Earlier work this paper cites.
1904
Earlier work this paper cites.
1904
Earlier work this paper cites.
1904
Earlier work this paper cites.
1905
Earlier work this paper cites.
1906
Earlier work this paper cites.
1906
Earlier work this paper cites.
A. Andreassen and B. Nachman, Neural Networks for Full Phase-space Reweighting and Parameter Tuning
1907
Earlier work this paper cites.
A. Butter, T. Plehn, and R. Winterhalder, How to GAN LHC Events
1907
Earlier work this paper cites.
1907
Earlier work this paper cites.
1907
Earlier work this paper cites.
L. Bradshaw, R. K. Mishra, A. Mitridate, and B. Ostdiek, Mass Agnostic Jet Taggers
1908
Earlier work this paper cites.
1908
Earlier work this paper cites.
1908
Earlier work this paper cites.
1909
Earlier work this paper cites.
1909
Earlier work this paper cites.
1911
Earlier work this paper cites.
1911
Earlier work this paper cites.
1912
Earlier work this paper cites.
R. T. D’Agnolo, G. Grosso, M. Pierini, A. Wulzer, and M. Zanetti, Learning Multivariate New Physics
1912
Earlier work this paper cites.
A. Butter, T. Plehn, and R. Winterhalder, How to GAN Event Subtraction
1912
Earlier work this paper cites.
J. Arjona Martinez, T. Q. Nguyen, M. Pierini, M. Spiropulu, and J.-R. Vlimant, Particle Generative Adversarial Networks for full-event simulation at the LHC and their application to pileup description · 1912
Earlier work this paper cites.
1912
Earlier work this paper cites.
J. Neyman and E. S. Pearson, On the problem of the most efficient tests of statistical hypotheses
1933
Earlier work this paper cites.
S. S. Wilks, The large-sample distribution of the likelihood ratio for testing composite hypotheses
1938
Earlier work this paper cites.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting
1958
Earlier work this paper cites.
J. Button, G. R. Kalbfleisch, G. R. Lynch, B. C. Maglić, A. H. Rosenfeld, and M. L. Stevenson, Pion-pion interaction in the reaction p ¯ + p → 2 π + + 2 π − + n π 0 \overline{p}+p\rightarrow 2{\pi}^{+}+2{\pi}^{-}+n{\pi}^{0}
1962
Earlier work this paper cites.
B. Efron, Bootstrap methods: Another look at the jackknife
1979
Earlier work this paper cites.
R. J. Barlow, Extended maximum likelihood
1990
Earlier work this paper cites.
L. Lonnblad, C. Peterson, and T. Rognvaldsson, Using neural networks to identify jets
1991
Earlier work this paper cites.
D. MacKay, Probable networks and plausible predictions – a review of practical bayesian methods for supervised neural networks
1995
Earlier work this paper cites.
T. Junk, Confidence level computation for combining searches with small statistics
1999
Earlier work this paper cites.
B. Nachman and D. Shih, Anomaly Detection with Density Estimation
2001
Earlier work this paper cites.
A. Andreassen, B. Nachman, and D. Shih, Simulation Assisted Likelihood-free Anomaly Detection
2001
Earlier work this paper cites.
G. Kasieczka and D. Shih, DisCo Fever: Robust Networks Through Distance Correlation
2001
Earlier work this paper cites.
A. L. Read, Presentation of search results: The CL(s) technique
2002
Earlier work this paper cites.
O. Amram and C. M. Suarez, Tag N’ Train: A Technique to Train Improved Classifiers on Unlabeled Data
2002
Cited alongside, same era.
P. C. Bhat and H. B. Prosper, Bayesian neural networks
2005
Cited alongside, same era.
N. D. Gagunashvili, Machine learning approach to inverse problem and unfolding procedure
2006
Cited alongside, same era.
2008
Cited alongside, same era.
A. Der Kiureghian and O. Ditlevsen, Aleatoric or epistemic? does it matter?
2009
Cited alongside, same era.
2018
Later among the works it cites.
2018
Later among the works it cites.
J. W. Monk, Deep Learning as a Parton Shower
2018
Later among the works it cites.
T. Cohen, M. Freytsis, and B. Ostdiek, (Machine) Learning to Do More with Less
2018
Later among the works it cites.
A. Butter, G. Kasieczka, T. Plehn, and M. Russell, Deep-learned Top Tagging with a Lorentz Layer
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
D. Martschei, M. Feindt, S. Honc, and J. Wagner-Kuhr, Advanced event reweighting using multivariate analysis
2012
Cited alongside, same era.
D. Martschei, M. Feindt, S. Honc, and J. Wagner-Kuhr, Advanced event reweighting using multivariate analysis
2012
Cited alongside, same era.
B. Nachman and C. G. Lester, Significance Variables
2013
Cited alongside, same era.
2013
Cited alongside, same era.
2013
Cited alongside, same era.
2014
Cited alongside, same era.
2015
Cited alongside, same era.
2018
Later among the works it cites.
K. Datta and A. J. Larkoski, Novel Jet Observables from Machine Learning
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
S. Macaluso and D. Shih, Pulling Out All the Tops with Computer Vision and Deep Learning
2018
Later among the works it cites.
K. Fraser and M. D. Schwartz, Jet Charge and Machine Learning
2018
Later among the works it cites.
2018
Later among the works it cites.
J. Duarte et al., Fast inference of deep neural networks in FPGAs for particle physics
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
F. A. Dreyer, G. P. Salam, and G. Soyez, The Lund Jet Plane
2018
Later among the works it cites.
2018
Later among the works it cites.
M. Andrews, M. Paulini, S. Gleyzer, and B. Poczos, End-to-End Event Classification of High-Energy Physics Data
2018
Later among the works it cites.
ATLAS Collaboration, Search for direct top squark pair production in the 3-body decay mode with a final state containing one lepton, jets, and missing transverse momentum in s = 13 \sqrt{s}=13 TeV p p pp collision data with the ATLAS detector
2019
Closest in time.
CMS Collaboration, Search for direct top squark pair production in events with one lepton, jets and missing transverse energy at 13 TeV
2019
Closest in time.
2019
Closest in time.
E. Bothmann and L. Debbio, Reweighting a parton shower using a neural network: the final-state case
2019
Closest in time.
R. T. D’Agnolo and A. Wulzer, Learning New Physics from a Machine
2019
Closest in time.
T. Heimel, G. Kasieczka, T. Plehn, and J. M. Thompson, QCD or What?
2019
Closest in time.
2019
Closest in time.
A. De Simone and T. Jacques, Guiding New Physics Searches with Unsupervised Learning
2019
Closest in time.
P. De Castro and T. Dorigo, INFERNO: Inference-Aware Neural Optimisation
2019
Closest in time.
2019
Closest in time.
S. Vallecorsa, F. Carminati, and G. Khattak, 3D convolutional GAN for fast simulation
2019
Closest in time.
2019
Closest in time.
K. Deja, T. Trzcinski, and u. Graczykowski, Generative models for fast cluster simulations in the TPC for the ALICE experiment
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
E. Bothmann and L. Del Debbio, Reweighting a parton shower using a neural network: the final-state case
2019
Closest in time.
G. Louppe, K. Cho, C. Becot, and K. Cranmer, QCD-Aware Recursive Neural Networks for Jet Physics
2019
Closest in time.
2019
Closest in time.
P. T. Komiske, E. M. Metodiev, and J. Thaler, Energy Flow Networks: Deep Sets for Particle Jets
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
S. Choi, S. J. Lee, and M. Perelstein, Infrared Safety of a Neural-Net Top Tagging Algorithm
2019
Closest in time.
J. S. H. Lee, I. Park, I. J. Watson, and S. Yang, Quark-Gluon Jet Discrimination Using Convolutional Neural Networks
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2020
Closest in time.
L. De Oliveira, B. Nachman, and M. Paganini, Electromagnetic Showers Beyond Shower Shapes
2020
Closest in time.
2020
Closest in time.
http://purl.flvc.org/fsu/fd/FSU_migr_etd-2069
S. R. Saucedo, Bayesian Neural Networks for Classification · 2069
Closest in time.