1
Deep underground neutrino experiment: DUNE
2
Non-parametric data-driven background modelling using conditional probabilities
3
Learning new physics from an imperfect machine
4
Online-compatible Unsupervised Non-resonant Anomaly Detection
5
Wire-cell 3D pattern recognition techniques for neutrino event reconstruction in large LArTPCs: algorithm description and quantitative evaluation with MicroBooNE simulation
6
Search for an anomalous excess of charged-current quasielastic
νe
interactions with the MicroBooNE experiment using Deep-Learning-based reconstruction
7
Applications and Techniques for Fast Machine Learning in Science
8
Challenges for unsupervised anomaly detection in particle physics
9
A cautionary tale of decorrelating theory uncertainties
10
AtlFast3: The Next Generation of Fast Simulation in ATLAS
11
Reconstruction of Neutrino Events in IceCube using Graph Neural Networks
12
Tree boosting for learning EFT parameters
13
Neural conditional reweighting
14
A novel trigger based on neural networks for radio neutrino detectors
15
Particle Convolution for High Energy Physics
16
nEXO: neutrinoless double beta decay search beyond 1028 year half-life sensitivity
17
Deep Learning for direct Dark Matter search with nuclear emulsions
18
Rare and Different: Anomaly Scores from a combination of likelihood and out-of-distribution models to detect new physics at the LHC
19
Extracting low energy signals from raw LArTPC waveforms using deep learning techniques — A proof of concept
20
Search for a heavy Higgs boson decaying into two lighter Higgs bosons in the ττbb final state at 13 TeV
21
Search for Active-Sterile Antineutrino Mixing Using Neutral-Current Interactions with the NOvA Experiment.
22
The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider
23
Preserving new physics while simultaneously unfolding all observables
24
Autoencoders for unsupervised anomaly detection in high energy physics
25
Better Latent Spaces for Better Autoencoders
26
Search for W' bosons decaying to a top and a bottom quark at $\sqrt{s} =$ 13 TeV in the hadronic final state
27
Understanding Event-Generation Networks via Uncertainties
28
Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physics
29
Comparing weak- and unsupervised methods for resonant anomaly detection
30
A deep-learning based raw waveform region-of-interest finder for the liquid argon time projection chamber
31
Topological obstructions to autoencoding
32
Constraints on effective field theory couplings using 311.2 days of LUX data
33
A Living Review of Machine Learning for Particle Physics
34
Scalable, End-to-End, Deep-Learning-Based Data Reconstruction Chain for Particle Imaging Detectors
35
Combine and conquer: event reconstruction with Bayesian Ensemble Neural Networks
36
The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics
37
Fast convolutional neural networks on FPGAs with hls4ml
38
Unsupervised in-distribution anomaly detection of new physics through conditional density estimation
39
Semantic segmentation with a sparse convolutional neural network for event reconstruction in MicroBooNE
40
Deep-Learning-Based Kinematic Reconstruction for DUNE
41
Search for Coherent Elastic Scattering of Solar ^{8}B Neutrinos in the XENON1T Dark Matter Experiment.
42
Equivariant energy flow networks for jet tagging
43
Cosmic Background Removal with Deep Neural Networks in SBND
44
Search for Higgs Boson Decays into a Z Boson and a Light Hadronically Decaying Resonance Using 13 TeV pp Collision Data from the ATLAS Detector.
45
Improving sensitivity to low-mass dark matter in LUX using a novel electrode background mitigation technique
46
Search for heavy neutral leptons in $$W^+\rightarrow \mu ^{+}\mu ^{\pm }\, \text {jet}$$ decays
47
Quasi anomalous knowledge: searching for new physics with embedded knowledge
49
Convolutional neural network approach to event position reconstruction in DarkSide-50 experiment
50
Enhancing searches for resonances with machine learning and moment decomposition
51
Convolutional neural network for multiple particle identification in the MicroBooNE liquid argon time projection chamber
52
The Phase-2 Upgrade of the CMS Level-1 Trigger
53
MUSiC: a model-unspecific search for new physics in proton-proton collisions at $\sqrt{s} = $ 13 TeV
54
Search for heavy resonances decaying into a pair of $Z$ bosons in the $\ell^+\ell^-\ell'^+\ell'^-$ and $\ell^+\ell^-\nu\bar\nu$ final states using 139 fb$^{-1}$ of proton-proton collisions at $\sqrt{s} = 13$ TeV with the ATLAS detector
55
Unsupervised clustering for collider physics
56
Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment
57
Robust Jet Classifiers through Distance Correlation.
58
GPU-Accelerated Machine Learning Inference as a Service for Computing in Neutrino Experiments
59
Simulation-assisted decorrelation for resonant anomaly detection
60
DCTRGAN: improving the precision of generative models with reweighting
61
Mass Unspecific Supervised Tagging (MUST) for boosted jets
62
Jet flavour classification using DeepJet
63
A review on machine learning for neutrino experiments
64
Automating the ABCD method with machine learning
65
Graph neural networks in particle physics
67
Parametrized classifiers for optimal EFT sensitivity
68
Dealing with Nuisance Parameters using Machine Learning in High Energy Physics: a Review
69
Bayesian Neural Networks for Fast SUSY Predictions
70
Scalable, Proposal-free Instance Segmentation Network for 3D Pixel Clustering and Particle Trajectory Reconstruction in Liquid Argon Time Projection Chambers
71
Clustering of Electromagnetic Showers and Particle Interactions with Graph Neural Networks in Liquid Argon Time Projection Chambers Data
72
Point proposal network for reconstructing 3D particle endpoints with subpixel precision in liquid argon time projection chambers
73
Finding new physics without learning about it: anomaly detection as a tool for searches at colliders
74
Lorentz Group Equivariant Neural Network for Particle Physics
75
PILArNet: Public Dataset for Particle Imaging Liquid Argon Detectors in High Energy Physics
76
Machine learning methods for the raw data analysis of cryogenic dark matter experiments
77
Lowering the energy threshold in COSINE-100 dark matter searches
78
Normalizing Flows: An Introduction and Review of Current Methods
79
Dijet Resonance Search with Weak Supervision Using sqrt[s]=13 TeV pp Collisions in the ATLAS Detector.
80
Adversarially Learned Anomaly Detection on CMS open data: re-discovering the top quark
81
Adversarial domain adaptation to reduce sample bias of a high energy physics classifier
82
Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
83
Event reconstruction for KM3NeT/ORCA using convolutional neural networks
84
Particle identification using semi-supervised learning in the PICO-60 dark matter detector
85
Direct optimization of the discovery significance in machine learning for new physics searches in particle colliders
86
Track finding at Belle II
87
Per-object systematics using deep-learned calibration
88
Optimal Statistical Inference in the Presence of Systematic Uncertainties Using Neural Network Optimization Based on Binned Poisson Likelihoods with Nuisance Parameters
89
Tag N’ Train: a technique to train improved classifiers on unlabeled data
90
Set2Graph: Learning Graphs From Sets
91
Deep Underground Neutrino Experiment (DUNE), Far Detector Technical Design Report, Volume II: DUNE Physics
92
Fast inference of Boosted Decision Trees in FPGAs for particle physics
93
Measurement of the Permanent Electric Dipole Moment of the Neutron.
94
Event generation with normalizing flows
95
Exploring phase space with Neural Importance Sampling
96
i- flow: High-dimensional integration and sampling with normalizing flows
97
Simulation assisted likelihood-free anomaly detection
98
Anomaly detection with density estimation
99
DisCo Fever: Robust Networks Through Distance Correlation
100
Learning multivariate new physics
101
A deep neural network to search for new long-lived particles decaying to jets
102
Allen: A High-Level Trigger on GPUs for LHCb
103
PyTorch: An Imperative Style, High-Performance Deep Learning Library
104
How to GAN away Detector Effects
105
ATLAS b-jet identification performance and efficiency measurement with $$t{\bar{t}}$$ events in pp collisions at $$\sqrt{s}=13$$ TeV
106
Template-free Pulse Height Estimation of Microcalorimeter Responses with PCA
107
Parametrizing the detector response with neural networks
108
Interaction networks for the identification of boosted
H→bb¯
decays
109
Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8
110
A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty
111
A Survey on Bias and Fairness in Machine Learning
112
Mass agnostic jet taggers
113
JEDI-net: a jet identification algorithm based on interaction networks
114
Reducing the Dependence of the Neural Network Function to Systematic Uncertainties in the Input Space
115
MadMiner: Machine Learning-Based Inference for Particle Physics
116
Neural networks for full phase-space reweighting and parameter tuning
117
Simulation of charge readout with segmented tiles in nEXO
118
ATLAS $b$-jet identification performance and efficiency measurement with $t\bar{t}$ events in $pp$ collisions at $\sqrt{s}=13$ TeV
119
Systematic aware learning
120
XENON1T dark matter data analysis: Signal reconstruction, calibration, and event selection
121
An Introduction to Variational Autoencoders
122
Search for Neutrinoless Double-Beta Decay with the Complete EXO-200 Dataset
123
Accelerating Deep Neural Networks for Real-time Data Selection for High-resolution Imaging Particle Detectors
124
Adversarially-trained autoencoders for robust unsupervised new physics searches
125
Flow-based generative models for Markov chain Monte Carlo in lattice field theory
126
Deep-learning jets with uncertainties and more
127
Uncovering latent jet substructure
128
Implementation of high-performance, sub-microsecond deep neural networks on FPGAs for trigger applications
129
Scalable Deep Convolutional Neural Networks for Sparse, Locally Dense Liquid Argon Time Projection Chamber Data
130
DijetGAN: a Generative-Adversarial Network approach for the simulation of QCD dijet events at the LHC
131
Machine learning templates for QCD factorization in the search for physics beyond the standard model
132
Deep learning based pulse shape discrimination for germanium detectors
133
Nuisance hardened data compression for fast likelihood-free inference
134
Pulse-shape dicrimination with deep learning in CRESST
135
The Machine Learning landscape of top taggers
136
ParticleNet: Jet Tagging via Particle Clouds
137
Extending the search for new resonances with machine learning
138
Suppression of cosmic muon spallation backgrounds in liquid scintillator detectors using convolutional neural networks
139
Generative Models for Fast Calorimeter Simulation: the LHCb case>
140
Developing a Bubble Chamber Particle Discriminator Using Semi-Supervised Learning
141
Variational autoencoders for new physics mining at the Large Hadron Collider
142
Improved energy reconstruction in NOvA with regression convolutional neural networks
143
Machine learning accelerated likelihood-free event reconstruction in dark matter direct detection
144
The Frontiers of Fairness in Machine Learning
145
QBDT, a new boosting decision tree method with systematical uncertainties into training for High Energy Physics
146
Pileup mitigation at the Large Hadron Collider with graph neural networks
147
Energy flow networks: deep sets for particle jets
148
Fundamental physics with the Square Kilometre Array
149
Graph Neural Networks for IceCube Signal Classification
150
GANs for generating EFT models
151
Searching for new physics with deep autoencoders
152
Search for low-mass dark matter with CDMSlite using a profile likelihood fit
154
Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment
155
Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber
156
U(1)
′ mediated decays of heavy sterile neutrinos in MiniBooNE
157
Machine learning at the energy and intensity frontiers of particle physics
158
Novelty Detection Meets Collider Physics
159
Dark Neutrino Portal to Explain MiniBooNE Excess.
160
Probing stop pair production at the LHC with graph neural networks
161
A strategy for a general search for new phenomena using data-derived signal regions and its application within the ATLAS experiment
162
Further developments of FORM
163
INFERNO: Inference-Aware Neural Optimisation
164
Learning new physics from a machine
165
Anomaly Detection for Resonant New Physics with Machine Learning.
166
Mining gold from implicit models to improve likelihood-free inference
167
Convolved substructure: analytically decorrelating jet substructure observables
168
A guide to constraining effective field theories with machine learning
169
Constraining Effective Field Theories with Machine Learning.
171
Deep neural networks for energy and position reconstruction in EXO-200
173
Fast inference of deep neural networks in FPGAs for particle physics
174
Updated global analysis of neutrino oscillations in the presence of eV-scale sterile neutrinos
175
Jet substructure at the Large Hadron Collider
176
Boosted decision trees in the CMS Level-1 endcap muon trigger
177
Three-dimensional convolutional neural networks for neutrinoless double-beta decay signal/background discrimination in high-pressure gaseous Time Projection Chamber
178
Ionization electron signal processing in single phase LArTPCs. Part I. Algorithm Description and quantitative evaluation with MicroBooNE simulation
179
Automatic physical inference with information maximising neural networks
180
Signal-background discrimination with convolutional neural networks in the PandaX-III experiment using MC simulation
181
Learning to classify from impure samples with high-dimensional data
182
Machine learning uncertainties with adversarial neural networks
183
Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV
184
Neural Message Passing for Jet Physics
185
Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC
186
Generative Adversarial Networks: An Overview
187
Convolved substructure: analytically decorrelating jet substructure observables
188
Modeling Smooth Backgrounds and Generic Localized Signals with Gaussian Processes
189
Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning
190
A generic anti-QCD jet tagger
191
Classification without labels: learning from mixed samples in high energy physics
192
Search for Neutrinoless Double-Beta Decay with the Upgraded EXO-200 Detector.
193
(Machine) learning to do more with less
194
CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks
196
Decorrelated jet substructure tagging using adversarial neural networks
197
QCD-aware recursive neural networks for jet physics
198
Weakly supervised classification in high energy physics
199
Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
200
Design and construction of the MicroBooNE detector
201
Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber
202
Accelerating the BSM interpretation of LHC data with machine learning
203
Learning to Pivot with Adversarial Networks
204
Background rejection in NEXT using deep neural networks
205
Constraints on large extra dimensions from the MINOS experiment
206
Reweighting with Boosted Decision Trees
207
Jet flavor classification in high-energy physics with deep neural networks
208
Search for Majorana Neutrinos Near the Inverted Mass Hierarchy Region with KamLAND-Zen.
209
The BSM-AI project: SUSY-AI–generalizing LHC limits on supersymmetry with machine learning
210
A convolutional neural network neutrino event classifier
211
Jet Substructure Classification in High-Energy Physics with Deep Neural Networks
212
XGBoost: A Scalable Tree Boosting System
213
Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure
214
Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks
215
Non-standard neutrino interactions at DUNE
216
Jet-images — deep learning edition
217
Approximating Likelihood Ratios with Calibrated Discriminative Classifiers
218
Non standard neutrino interactions: current status and future prospects
219
Variational Inference with Normalizing Flows
220
U-Net: Convolutional Networks for Biomedical Image Segmentation
221
A Proposal for a Three Detector Short-Baseline Neutrino Oscillation Program in the Fermilab Booster Neutrino Beam
222
New approaches for boosting to uniformity
223
A general search for new phenomena with the ATLAS detector in pp collisions at $\sqrt{s}$ = 8 TeV
224
Searching for exotic particles in high-energy physics with deep learning
225
Auto-Encoding Variational Bayes
226
uBoost: a boosting method for producing uniform selection efficiencies from multivariate classifiers
227
The LHCb trigger and its performance in 2011
228
TMVA - Toolkit for multivariate data analysis
229
Model unspecific search for new physics in pp collision at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\sqrt{ s} {=
230
Measurement of Event Plane Correlations in Pb-Pb Collisions at $\sqrt{s_{\mathrm{NN}}}$=2.76 TeV with the ATLAS Detector
231
Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC
232
Light Sterile Neutrinos: A White Paper
233
Matter and antimatter in the universe
234
Search for New Physics at CDF
235
A General Search for New Phenomena at HERA
236
Current status and future prospects.
237
Global search for new physics with 2.0 fb(-1) at CDF
238
Model-independent and quasi-model-independent search for new physics at CDF
239
LSST: From Science Drivers to Reference Design and Anticipated Data Products
240
Model-Independent Global Search for New High-pT Physics at CDF
241
Model-Independent and Quasi-Model-Independent Search for New Physics at CDF
242
The NOvA Technical Design Report
243
TMVA - Toolkit for Multivariate Data Analysis
244
LIGO: The laser interferometer gravitational-wave observatory
245
General search for new phenomena in ep scattering at HERA
246
Lorentz and CPT violation in neutrinos
247
Direct evidence for neutrino flavor transformation from neutral-current interactions in the Sudbury Neutrino Observatory.
248
A QUASI MODEL INDEPENDENT SEARCH FOR NEW HIGH PT PHYSICS AT D0
249
Quasi-model-independent search for new physics at large transverse momentum
250
Search for New Physics in e mu X Data at D0 Using Sleuth: A Quasi-Model-Independent Search Strategy for New Physics
251
Evidence for oscillation of atmospheric neutrinos
252
Long Short-Term Memory
254
Learning representations by back-propagating errors
255
Competition and cooperation in neural nets
257
Benchmark data and model independent event classification for the large hadron collider
258
Search for Higgs boson decays into two new lowmass spin-0 particles in the 4b channel with the ATLAS detector using pp collisions at s 13 = TeV
259
Search for heavy neutral leptons in W+→μ+μ±jet decays
260
Classifying Anomalies THrough Outer Density Estimation (CATHODE)
261
Search for dark matter particles produced in association with a Higgs boson in proton-proton collisions at √ s = 13 TeV
262
Event vertex reconstruction with deep neural networks for the DarkSide-20k experiment
263
Search for heavy resonances decaying into a pair of Z bosons in the +-+-and +-̄ final states using 139 fb-1 of proton – proton collisions at s = 13 TeV with the ATLAS detector
264
rediscovering the top quark
265
Autoencoders on FPGAs for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider
266
Search for heavy resonances decaying to a pair of boosted Higgs bosons in final states with leptons and a bottom quark-antiquark pair at √ s = 13 TeV
267
Background Mitigation in Dual Phase Xenon Time Projection
268
Search for Higgs boson pair production via vector boson fusion with highly Lorentz-boosted Higgs bosons in the four b quark final state at √ s = 13 TeV
269
Belle II Tracking Group), Track finding at Belle II, Comput
270
Oxford University Press : Review of Particle Physics, 2020-2021
271
Identification of highly Lorentzboosted heavy particles using graph neural networks and new mass decorrelation techniques
272
Resurrecting bbh with kinematic shapes
273
LHCb Upgrade GPU High Level Trigger Technical Design Report
274
georgestein/ml-in-cosmology: Machine learning in cosmology
276
Far Detector Technical Design Report Vol. I: Introduction to DUNE
277
Fast simulation of the ATLAS calorimeter system with Generative Adversarial Networks
278
initial zenodo release
280
Physics 10.1140/epjc/s10052-021-08853-y
281
A brief introduction to weakly supervised learning
282
Explorer Gaia Data Release 2 . The astrometric solution
284
GENERATIVE ADVERSARIAL NETS
285
10.1051/epjconf/201921402034
286
Performance of massdecorrelated jet substructure observables for hadronic twobody decay tagging in ATLAS
287
ATLAS), Search for pair production of higgsinos in final states with at least three b-tagged jets in √ s = 13 TeV pp collisions using the ATLAS detector
289
The Convolutional Visual Network for Identification and Reconstruction of NOvA Events
290
Research (ACAT 2016): Valparaiso, Chile, January 18-22, 2016 762 , 012036
291
largescale machine learning on heterogeneous systems
292
Search for 2 νββ decay of 136 Xe to the 0 +1 excited state of 136 Ba with EXO-200
293
Efficient, reliable and fast high-level triggering using a bonsai boosted decision tree
295
Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC
296
This PDF file includes: Materials and Methods
297
arXiv:0711.3041 [gr-qc] 271]
299
Searching for new physics: Contributions to LEP and the LHC
301
Handwritten Digit Recognition with a Back-Propagation Network
302
Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Visual Pattern Recognition
303
On the Problem of the Most Efficient Tests of Statistical Hypotheses
304
UvA-DARE (Digital Academic Repository) Neutrino interaction classification with a convolutional neural network in the DUNE far detector
306
ML applications to SM physics, see
307
Such a categorisation is not unique
309
Cosmogan: creating high-fidelity
