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Notes: it is customary in high energy particle/nuclear physics for authors to be listed in alphabetical order. Experimental collaborations are often large (hundreds to thousands!) and it is typical to list everyone as authors. The papers and notes listed below are only the ones with substantial group contribution. The publications below are ordered by date of arXiv posting (or equivalent).

Anomaly detection with flow-based fast calorimeter simulators
C. Krause, B. Nachman, I. Pang, D. Shih, Y. Zhu
e-Print: 2312.11618
Integrating Particle Flavor into Deep Learning Models for Hadronization
J. Chan, X. Ju, A. Kania, B. Nachman, V. Sangli, A. Siodmok
e-Print: 2312.08453
Non-resonant Anomaly Detection with Background Extrapolation
K. Bai, R. Mastandrea, B. Nachman
e-Print: 2311.12924
Safe but Incalculable: Energy-weighting is not all you need
S. Bright-Thonney, B. Nachman, J. Thaler
e-Print: 2311.07652
Designing Observables for Measurements with Deep Learning
O. Long, B. Nachman
e-Print: 2310.08717
Full Phase Space Resonant Anomaly Detection
E. Buhmann, C. Ewen, G. Kasieczka, V. Mikuni, B. Nachman, D. Shih
e-Print: 2310.06897
The Optimal use of Segmentation for Sampling Calorimeters
F. Acosta, B. Karki, P. Karande, A. Angerami, M. Arratia, K. Barish, R. Milton, S. Morán, B. Nachman, A. Sinha
e-Print: 2310.04442
Flows for Flows: Morphing one Dataset into another with Maximum Likelihood Estimation
T. Golling, S. Klein, R. Mastandrea, B. Nachman, J. Raine
e-Print: 2309.06472
Improving Generative Model-based Unfolding with Schrödinger Bridges
S. Diefenbacher, G. Liu, V. Mikuni, B. Nachman, W. Nie
e-Print: 2308.12351
Refining Fast Calorimeter Simulations with a Schrödinger Bridge
S. Diefenbacher, V. Mikuni, B. Nachman
e-Print: 2308.12339
CaloScore v2: Single-shot Calorimeter Shower Simulation with Diffusion Models
V. Mikuni, B. Nachman
e-Print: 2308.03847
The Interplay of Machine Learning--based Resonant Anomaly Detection Methods
T. Golling, G. Kasieczka, C. Krause, R. Mastandrea, B. Nachman, J. Raine, D. Sengupta, D. Shih, M. Sommerhalder
e-Print: 2307.11157
Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation
F. Acosta, V. Mikuni, B. Nachman, M. Arratia, K. Barish, B. Karki, R. Milton, P. Karande, A. Angerami
e-Print: 2307.04780
Learning to Isolate Muons in Data
E. Witkowski, B. Nachman, D. Whiteson
e-Print: 2306.15737
High-dimensional and Permutation Invariant Anomaly Detection
V. Mikuni, B. Nachman
e-Print: 2306.03933
Fitting a Deep Generative Hadronization Model
J. Chan, X. Ju, A. Kania, B. Nachman, V. Sangli, A. Siodmok
e-Print: 2305.17169
Learning Likelihood Ratios with Neural Network Classifiers
S. Rizvi, M. Pettee, B. Nachman
e-Print: 2305.10500
ELSA -- Enhanced latent spaces for improved collider simulations
B. Nachman, R. Winterhalder
e-Print: 2305.07696
Weakly-Supervised Anomaly Detection in the Milky Way
M. Pettee, S. Thanvantri, B. Nachman, D. Shih, M. Buckley, J. Collins
e-Print: 2305.03761
Parton Labeling without Matching: Unveiling Emergent Labelling Capabilities in Regression Models
S. Qiu, S. Han, X. Ju, B. Nachman, H. Wang
e-Print: 2304.09208
Unbinned Deep Learning Jet Substructure Measurement in High Q2 ep collisions at HERA
H1 Collaboration
e-Print: 2303.13620
Machine learning-assisted measurement of azimuthal angular asymmetries in deep-inelastic scattering with the H1 detector
H1 Collaboration
Public note: H1prelim-23-031
Unbinned Profiled Unfolding
J. Chan, B. Nachman
e-Print: 2302.05390
FETA: Flow-Enhanced Transportation for Anomaly Detection
T. Golling, S. Klein, R. Mastandrea, B. Nachman
e-Print: 2212.11285
Resonant Anomaly Detection with Multiple Reference Datasets
M. F. Chen, B. Nachman, F. Sala
e-Print: 2212.10579
Efficiently Moving Instead of Reweighting Collider Events with Machine Learning
R. Mastandrea and B. Nachman
e-Print: 2212.06155
Efficient quantum implementation of 2+1 U(1) lattice gauge theories with Gauss law constraints
C. Kane, D. M. Grabowska, B. Nachman, C. W. Bauer
e-Print: 2211.10497
Geometry Optimization for Long-lived Particle Detectors
T. Gorordo, S. Knapen, B. Nachman, D. J. Robinson, A. Suresh
e-Print: 2211.08450
Statistical Patterns of Theory Uncertainties
A. Ghosh, B. Nachman, T. Plehn, L. Shire, T. M.P. Tait, D. Whiteson
e-Print: 2210.15167
Machine-Learning Compression for Particle Physics Discoveries
J. H. Collins, Y. Huang, S. Knapen, B. Nachman, D. Whiteson
NeurIPS Machine Learning and Physical Sciences (2022) · e-Print: 2210.11489
The Future of High Energy Physics Software and Computing
V. D. Elvira, S. Gottlieb, O. Gutsche, B. Nachman (frontier conveners), et al.
e-Print: 2210.05822
Anomaly Detection under Coordinate Transformations
G. Kasieczka, R. Mastandrea, V. Mikuni, B. Nachman, M. Pettee, D. Shih
e-Print: 2209.06225
Point Cloud Deep Learning Methods for Pion Reconstruction in the ATLAS Experiment
ATLAS Collaboration
Public note: ATL-PHYS-PUB-2022-040
Constituent-Based Top-Quark Tagging with the ATLAS Detector
ATLAS Collaboration
Public note: ATL-PHYS-PUB-2022-039
Overcoming exponential scaling with system size in Trotter-Suzuki implementations of constrained Hamiltonians: 2+1 U(1) lattice gauge theories
D. M. Grabowska, C. Kane, B. Nachman, C. W. Bauer
e-Print: 2208.03333
Morphing parton showers with event derivatives
B. Nachman and S. Prestel
e-Print: 2208.02274
Systematic Quark/Gluon Identification with Ratios of Likelihoods
S. Bright-Thonney, I. Moult, B. Nachman, S. Prestel
e-Print: 2207.12411
Machine learning-assisted measurement of multi-differential lepton-jet correlations in deep-inelastic scattering with the H1 detector
H1 Collaboration
Public note: H1prelim-22-031
Score-based Generative Models for Calorimeter Shower Simulation
V. Mikuni, B. Nachman
e-Print: 2206.11898
Going off topics to demix quark and gluon jets in $\alpha_S$ extractions
M. LeBlanc, B. Nachman, C. Sauer
e-Print: 2206.10642
Quantum Anomaly Detection for Collider Physics
S. Alvi, C. Bauer, B. Nachman
e-Print: 2206.08391
Self-supervised Anomaly Detection for New Physics
B. M. Dillon, R. Mastandrea, B. Nachman
e-Print: 2205.10380
Bias and Priors in Machine Learning Calibrations for High Energy Physics
R. Gambhir, B. Nachman, J. Thaler
Phys. Rev. D 106 (2022) 036011 · e-Print: 2205.05084
Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics
R. Gambhir, B. Nachman, J. Thaler
Phys. Rev. Lett. 129 (2022) 082001 · e-Print: 2205.03413
Multi-differential Jet Substructure Measurement in High Q^2 DIS Events with HERA-II Data
H1 Collaboration
Public note: H1prelim-22-034
Exploring the Universality of Hadronic Jet Classification
K. Cheung, Y. Chung, S. Hsu, B. Nachman
e-Print: 2204.03812
Optimizing Observables with Machine Learning for Better Unfolding
M. Arratia, D. Britzger, O. Long, B. Nachman
JINST 17 (2022) P07009 · e-Print: 2203.16722
Towards a Deep Learning Model for Hadronization
A. Ghosh, X. Ju, B. Nachman, A. Siodmok
e-Print: 2203.12660
Improving Quantum Simulation Efficiency of Final State Radiation with Dynamic Quantum Circuits
P. Deliyannis, J. Sud, D. Chamaki, Z. Webb-Mack, C. W. Bauer, B. Nachman
Phys. Rev. D 106 (2022) 036007 · e-Print: 2203.10018
Simulation-based Anomaly Detection for Multileptons at the LHC
K. Krzyzanska and B. Nachman
e-Print: 2203.09601
Data-Directed Search for New Physics based on Symmetries of the SM
M. Birman, B. Nachman, R. Sebbah, G. Sela, O. Turetz, S. Bressler
Eur. Phys. J. C 82 (2022) 508 · e-Print: 2203.07529
A Holistic Approach to Predicting Top Quark Kinematic Properties with the Covariant Particle Transformer
S. Qiu, S. Han, X. Ju, B. Nachman, H. Wang
e-Print: 2203.05687
Ephemeral Learning -- Augmenting Triggers with Online-Trained Normalizing Flows
A. Butter et al.
SciPost Phys. 13 (2022) 087 · e-Print: 2202.09375
Calomplification -- The Power of Generative Calorimeter Models
S. Bieringer et al.
JINST 17 (2022) P09028 · e-Print: 2202.07352
SymmetryGAN: Symmetry Discovery with Deep Learning
K. Desai, B. Nachman, J. Thaler
Phys. Rev. D 105 (2022) 096031 · e-Print: 2112.05722
Machine Learning in the Search for New Fundamental Physics
G. Karagiorgi, G. Kasieczka, S. Kravitz, B. Nachman, D. Shih
Nat. Rev. Phys. (2022) · e-Print: 2112.03769
Online-compatible Unsupervised Non-resonant Anomaly Detection
V. Mikuni, B. Nachman, D. Shih
e-Print: 2111.06417
Computationally Efficient Zero Noise Extrapolation for Quantum Gate Error Mitigation
V. R. Pascuzzi, A. He, C. W. Bauer, W. A. de Jong, B. Nachman
Phys. Rev. A 105 (2022) 042406 · e-Print: 2110.13338
Reconstructing the Kinematics of Deep Inelastic Scattering with Deep Learning
M. Arratia, D. Britzger, O. Long, B. Nachman
e-Print: 2110.05505
Presenting Unbinned Differential Cross Section Results
M. Arratia et al.
e-Print: 2109.13243
Practical considerations for the preparation of multivariate Gaussian states on quantum computers
C. W. Bauer, P. Deliyannis, M. Freytsis, B. Nachman
e-Print: 2109.10918
A Cautionary Tale of Decorrelating Theory Uncertainties
A. Ghosh and B. Nachman
Eur. Phys. J. C 82 (2022) 46 · e-Print: 2109.08159
Classifying Anomalies THrough Outer Density Estimation (CATHODE)
A. Hallin, J. Isaacson, G. Kasieczka, C. Krause, B. Nachman, T. Quadfasel, M. Schlaffer, D. Shih, M. Sommerhalder
e-Print: 2109.00546
High-dimensional Anomaly Detection with Radiative Return in electron-positron Collisions
J. Gonski, J. Lai, B. Nachman, I. Ochoa
JHEP 04 (2022) 156 · e-Print: 2108.13451
Active Readout Error Mitigation
R. Hicks, B. Kobrin, C. W. Bauer, B. Nachman
e-Print: 2108.12432
Measurement of lepton-jet correlation in deep-inelastic scattering with the H1 detector using machine learning for unfolding
H1 Collaboration
e-Print: 2108.12376
Digluon Tagging using sqrt(s) = 13 TeV pp Collisions in the ATLAS Detector
ATLAS Collaboration
Public note: ATL-PHYS-PUB-2021-027
Neural Conditional Reweighting
B. Nachman and J. Thaler
Phys. Rev. D 105 (2022) 076015 · e-Print: 2107.08979
New Methods and Datasets for Group Anomaly Detection From Fundamental Physics
G. Kasieczka, B. Nachman, and D. Shih
ANDEA (Anomaly and Novelty Detection, Explanation and Accommodation) Workshop at KDD 2021 · e-Print: 2107.02821
Measurements of sensor radiation damage in the ATLAS inner detector using leakage currents
ATLAS Collaboration
JINST 16 (2021) P08025 · e-Print: 2106.09287
Latent Space Refinement for Deep Generative Models
R. Winterhalder, M. Bellegente, B. Nachman
e-Print: 2106.00792
Preserving New Physics while Simultaneously Unfolding All Observables
P. Komiske, W. P. McCormack, B. Nachman
Phys. Rev. D 104 (2021) 076027 · e-Print: 2105.09923
Uncertainty Aware Learning for High Energy Physics
A. Ghosh, B. Nachman, D. Whiteson
Phys. Rev. D 104 (2021) 056026 · e-Print: 2105.08742
Identifying the Quantum Properties of Hadronic Resonances using Machine Learning
J. Filipek, S. Hsu, J. Kruper, K. Mohan, and B. Nachman
e-Print: 2105.04582
Scaffolding Simulations with Deep Learning for High-dimensional Deconvolution
A. Andreassen, P. T. Komiske, E. M. Metodiev, B. Nachman, A. Suresh, and J. Thaler
ICLR simDL workshop (2021) · e-Print: 2105.04448
Measurement of lepton-jet correlations in high Q^2 neutral-current DIS with the H1 detector at HERA
H1 Collaboration
Public note: H1prelim-21-031
Categorizing Readout Error Correlations on Near Term Quantum Computers
B. Nachman and M. R. Geller
e-Print: 2104.04607
Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection
J. H. Collins, P. Martin-Ramiro, B. Nachman, D. Shih
Eur. Phys. J. C 81 (2021) 617 · e-Print: 2104.02092
Mitigating depolarizing noise on quantum computers with noise-estimation circuits
M. Urbanek, B. Nachman, V. R. Pascuzzi, A. He, C. W. Bauer, W. A. de Jong
e-Print: 2103.08591
Quantum Gate Pattern Recognition and Circuit Optimization for Scientific Applications
W. Jang, K. Terashi, M. Saito, C. W. Bauer, B. Nachman, Y. Iiyama, T. Kishimoto, R. Okubo, R. Sawada, J. Tanaka
e-Print: 2102.10008
Simulating collider physics on quantum computers using effective field theories
C. Bauer, M. Freytsis, B. Nachman
Phys. Rev. Lett. 127 (2021) 212001 · e-Print: 2102.05044
A Living Review of Machine Learning for Particle Physics
M. Feickert and B. Nachman
e-Print: 2102.02770
The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
G. Kasieczka, B. Nachman, D. Shih (editors) et al.
Rep. Prog. Phys. 84 (2021) 124201 · e-Print: 2101.08320
E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once
B. Nachman and J. Thaler
Phys. Rev. D. 103 (2021) 116013 · e-Print: 2101.07263
Total recall: episodic memory retrieval, choice, and memory confidence in the rat
H. Joo et al.
Current Biology 31 (2021) 4571 · e-Print: 2020.12.14.420174bioRxiv
Beyond 4D Tracking: Using Cluster Shapes for Track Seeding
P. J. Fox, S. Huang, J. Isaacson, X. Ju, and B. Nachman
JINST 16 (2021) P05001 · e-Print: 2012.04533
Anomaly Detection for Physics Analysis and Less than Supervised Learning
B. Nachman
e-Print: 2010.14554
Enhancing searches for resonances with machine learning and moment decomposition
O. Kitouni, B. Nachman, C. Weisser, M. Williams
JHEP 04 (2021) 70 · e-Print: 2010.09745
Readout Rebalancing for Near Term Quantum Computers
R. Hicks, C. Bauer, and B. Nachman
Phys. Rev. A 103 (2021) 022407 · e-Print: 2010.07496
Parameter Estimation using Neural Networks in the Presence of Detector Effects
A. Andreassen, S. Hsu, B. Nachman, N. Suaysom, A. Suresh
Phys. Rev. D 103 (2021) 036001 · e-Print: 2010.03569
Disentangling Boosted Higgs Boson Production Modes with Machine Learning
Y. Chung, S. Hsu, and B. Nachman
JINST 16 (2021) P07002 · e-Print: 2009.05930
DCTRGAN: Improving the Precision of Generative Models with Reweighting
S. Diefenbacher, E. Eren, G. Kasieczka, A. Korol, B. Nachman, and D. Shih
JINST 15 (2020) P11004 · e-Print: 2009.03796
Simulation-Assisted Decorrelation for Resonant Anomaly Detection
K. Benkendorfer, L. Le Pottier, and B. Nachman
Phys. Rev. D 104 (2021) 035003 · e-Print: 2009.02205
New Method for Silicon Sensor Charge Calibration Using Compton Scattering
P. McCormack, M. Garcia-Sciveres, T. Heim, B, Nachman, M. Lauritzen
e-Print: 2008.11860
GANplifying Event Samples
A. Butter, S. Diefenbacher, G. Kasieczka, B. Nachman, and T. Plehn
SciPost Physics 10 (2021) 139 · e-Print: 2008.06545
Supervised Jet Clustering with Graph Neural Networks for Lorentz Boosted Bosons
X. Ju and B. Nachman
Phys. Rev. D 102 (2020) 075014 · e-Print: 2008.06064
Measurement of the ATLAS Detector Jet Mass Response using Forward Folding with 80/fb of sqrt(s)=13 TeV pp data
ATLAS Collaboration
Public note: ATLAS-CONF-2020-022
Deep Learning for Pion Identification and Energy Calibration with the ATLAS Detector
ATLAS Collaboration
Public note: ATL-PHYS-PUB-2020-018
ABCDisCo: Automating the ABCD Method with Machine Learning
G. Kasieczka, B. Nachman, M. Schwartz, D. Shih
Phys. Rev. D 103 (2021) 035021 · e-Print: 2007.14400
A Neural Resampler for Monte Carlo Reweighting with Preserved Uncertainties
B. Nachman and J. Thaler
Phys. Rev. D 102 (2020) 076004 · e-Print: 2007.11586
Dijet resonance search with weak supervision using sqrt(s) = 13 TeV pp collisions in the ATLAS detector
ATLAS Collaboration
Phys. Rev. Lett. 125 (2020) 131801 · e-Print: 2005.02983
Measurement of the Lund jet plane using charged particles in 13 TeV proton-proton collisions with the ATLAS detector
ATLAS Collaboration
Phys. Rev. Lett. 124 (2020) 222002 · e-Print: 2004.03540
Resource Efficient Zero Noise Extrapolation with Identity Insertions
A. He, B. Nachman, W. A. de Jong, and C. W. Bauer
Phys. Rev. A 102 (2020) · e-Print: 2003.04941
Jet Studies: Four decades of gluons
S. Manzani, B. Nachman, et al.
Les Houches 2019: Physics at TeV Colliders Standard Model Working Group Report · e-Print: 2003.01700
Simultaneous Jet Energy and Mass Calibrations with Neural Networks
ATLAS Collaboration
Public note: ATL-PHYS-PUB-2020-001
Simulation Assisted Likelihood-free Anomaly Detection
A. Andreassen, B. Nachman, D. Shih
Phys. Rev. D 101 (2020) 095004. · e-Print: 2001.05001
Anomaly Detection with Density Estimation
B. Nachman and D. Shih
Phys. Rev. D 101 (2020) 075042. · e-Print: 2001.04990
A measurement of soft-drop jet observables in pp collisions with the ATLAS detector at sqrt(s) = 13 TeV
ATLAS Collaboration
Phys. Rev. D 101 (2020) 052007 · e-Print: 1912.09837
OmniFold: A Method to Simultaneously Unfold All Observables
A. Andreassen, E. Metodiev, P. Komiske, B. Nachman, J. Thaler
Phys. Rev. Lett. 124 (2020) 182001 · e-Print: 1911.09107
Expression of Interest for the CODEX-b Detector
CODEX-b Collaboration
EPJC (accepted Nov. 2020) · e-Print: 1911.00481
AI Safety for High Energy Physics
B. Nachman, and C. Shimmin
e-Print: 1910.08606
Identifying Merged Tracks in Dense Environments with Machine Learning
P. McCormack, M. Ganai, B. Nachman, M. Garcia-Sciveres
CTD/WIT 2019 Proceedings · e-Print: 1910.06286
Parametrizing the Detector Response with Neural Networks
S. Cheong, A. Cukierman, B. Nachman, M. Safdari, A. Schwartzman
JINST 15 (2020) P01030 · e-Print: 1910.03773
Unfolding Quantum Computer Readout Noise
B. Nachman, M. Urbanek, W. de Jong, C. Bauer
npj Quantum Information 6 (2020) · e-Print: 1910.01969
Quantum error detection improves accuracy of chemical calculations on a quantum computer
M. Urbanek, B. Nachman, W. de Jong
Phys. Rev. A 102 (2020) 022427 · e-Print: 1910.00129
A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty
B. Nachman
SciPost Phys. 8 (2020) 090 · e-Print: 1909.03081
The Measurement of Position Resolution of RD53A Pixel Modules
G. Zang, B. Nachman, Shih-Chieh Hsu, Xin Chen
SLAC eConf C1907293 · e-Print: 1908.10973
Convolutional Neural Networks with Event Images for Pileup Mitigation with the ATLAS Detector
ATLAS Collaboration
Public note: ATL-PHYS-PUB-2019-028
Measurement of the Lund Jet Plane using charged particles with the ATLAS detector from 13 TeV proton--proton collisions
ATLAS Collaboration
Public note: ATLAS-CONF-2019-035
Neural Networks for Full Phase-space Reweighting and Parameter Tuning
A. Andreassen and B. Nachman
Phys. Rev. D 101 (2020) 091901(R). · e-Print: 1907.08209
The motivation and status of two-body resonance decays after the LHC Run 2 and beyond
J. Kim, K. Kong, B. Nachman, D. Whiteson
JHEP 04 (2020) 30 · e-Print: 1907.06659
Properties of jet fragmentation using charged particles measured with the ATLAS detector in pp collisions at sqrt(s) = 13 TeV
ATLAS Collaboration
Phys. Rev. D 100 (2019) 052011 · e-Print: 1906.09254
Modelling radiation damage to pixel sensors in the ATLAS detector
ATLAS Collaboration
JINST 14 (2019) P06012 · e-Print: 1905.03739
A quantum algorithm for high energy physics simulations
C. Bauer, W. de Jong, B. Nachman, D. Provasoli
Phys. Rev. Lett. 126 (2021) 062001 · e-Print: 1904.03196
Extracting the Top-Quark Width from Non-Resonant Production
C. Herwig, T. Jezo, B. Nachman
Phys. Rev. Lett. 122 (2019) 231803 · e-Print: 1903.10519
Machine Learning Templates for QCD Factorization in the Search for Physics Beyond the Standard Model
J. Lin, W. Bhimji, B. Nachman
JHEP 05 (2019) 181. · e-Print: 1903.02556
Nonlocal Thresholds for Improving the Spatial Resolution of Pixel Detectors
B. Nachman and A. Spies
JINST 14 (2019) P09028 · e-Print: 1903.01624
Automating the Construction of Jet Observables with Machine Learning
K. Datta, A. Larkoski, B. Nachman
Phys. Rev. D 100 (2019) 095016. · e-Print: 1902.07180
Extending the Bump Hunt with Machine Learning
J. Collins, K. Howe, B. Nachman
Phys. Rev. D 99 (2019) 014038. · e-Print: 1902.02634
A Quantum Algorithm to Efficiently Sample from Interfering Binary Trees
D. Provasoli, B. Nachman, W. de Jong, C. Bauer
Quantum Science and Technology 5 (2020) 035004 · e-Print: 1901.08148
Properties of g->bb at small opening angles in pp collisions with the ATLAS detector at sqrt(s) = 13 TeV
ATLAS Collaboration
Phys. Rev. D 99 (2019) 052004 · e-Print: 1812.09283
Charm-quark Yukawa Coupling in Higgs to ccy at the LHC
T. Han, B. Nachman, X. Wang
Phys. Lett. B 793 (2019) 90 · e-Print: 1812.06992
Prospects for Dark Matter searches in mono-photon and VBF+MET final states in ATLAS
ATLAS Collaboration
Public note: ATL-PHYS-PUB-2018-038
Investigating the Topology Dependence of Quark and Gluon Jets
S. Bright-Thonney and B. Nachman
JHEP 03 (2019) 098. · e-Print: 1810.05653
Leveraging the ALICE/L3 cavern for long-lived exotics
V. Gligorov, S. Knapen, B. Nachman, M. Papucci, D. Robinson
Phys. Rev. D 99 (2019) 015023 · e-Print: 1810.03636
Generalized Numerical Inversion: A Neural Network Approach to Jet Calibration
ATLAS Collaboration
Public note: ATL-PHYS-PUB-2018-013
Impact of Pile-up on Jet Constituent Multiplicity in ATLAS
ATLAS Collaboration
Public note: ATL-PHYS-PUB-2018-011
Boosting H to bb with Machine Learning
J. Lin, M. Freytsis, I. Moult, and B. Nachman
JHEP 10 (2018) 101. · e-Print: 1807.10768
Modeling the Mobility and Lorentz angle for the ATLAS Pixel Detector
ATLAS Collaboration
Public note: ATL-INDET-PUB-2018-001
Limits on new coloured fermions using precision jet data from the Large Hadron Collider
J. Llorente and B. Nachman
Nucl. Phys. B 936 (2018) 106. · e-Print: 1807.00894
Identifying merged clusters in the ATLAS strip detector
ATLAS Collaboration
Public note: ATL-INDET-PROC-2018-006
Electromagnetic Showers Beyond Shower Shapes
L. de Oliveira, B. Nachman, M. Paganini
Nucl. Instrum. Meth. A 951 (2020) 162879 · e-Print: 1806.05667
Probing the quantum interference between singly and doubly resonant top-quark production in pp collisions at sqrt(s) = 13 TeV with the ATLAS detector
ATLAS Collaboration
Phys. Rev. Lett. 121 (2018) 152002 · e-Print: 1806.04667
CWoLa Hunting: Extending the Bump Hunt with Machine Learning
J. Collins, K. Howe, B. Nachman
Phys. Rev. Lett. 121 (2018) 241803. · e-Print: 1805.02664
The Optimal Use of Silicon Pixel Charge Information for Particle Identification
H. Patton and B. Nachman
Nucl. Instrum. Meth. A 913 (2018) 91 · e-Print: 1803.08974
Towards extracting the strong coupling constant from jet substructure at the LHC
I. Moult, B. Nachman, G. Soyez, J. Thaler et al.
Les Houches 2017: Physics at TeV Colliders Standard Model Working Group Report · e-Print: 1803.07977
Jet Substructure at the Large Hadron Collider: Experimental Review
R. Kogler, B. Nachman, A. Schmidt (editors), et al.
Rev. Mod. Phys. 91 (2019) 045003 · e-Print: 1803.06991
Learning to Classify from Impure Samples
P. Komiske, E. Metodiev, B. Nachman, and M. Schwartz
Phys. Rev. D 98 (2018) 011502(R) · e-Print: 1801.10158
CaloGAN: Simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks
M. Paganini, L. de Oliveira, and B. Nachman
Phys. Rev. D 97 (2018) 014021. · e-Print: 1712.10321
A measurement of the soft-drop jet mass in pp collisions at sqrt(s) = 13 TeV with the ATLAS detector
ATLAS Collaboration
Phys. Rev. Lett. 121 (2018) 092001 · e-Print: 1711.08341
The Impact of Incorporating Shell-corrections to Energy Loss in Silicon
F. Wang, S. Dong, B. Nachman, M. Garcia-Sciveres, Q. Zeng
Nucl. Instrum. Meth. A 899 (2018) 1 · e-Print: 1711.05465
Ultimate position resolution of pixel clusters with binary readout for particle tracking
F. Wang, B. Nachman, M. Garcia-Sciveres
Nucl. Instrum. Meth. A 899 (2018) 10 · e-Print: 1711.00590
Analytically Decorrelating Jet Substructure Observables
I. Moult, B. Nachman, and D. Neill
JHEP 05 (2018) 002 · e-Print: 1710.06859
Optimal use of Charge Information for the HL-LHC Pixel Detector Readout
Y. Chen, E. Frangipane, M. Garcia-Sciveres, L. Jeanty, B. Nachman, S. Pagan Griso, F. Wang
Nucl. Instrum. Meth. A902 (2018) 197-210 · e-Print: 1710.02582
Technical Design Report for the ATLAS Inner Tracker Pixel Detector
ATLAS Collaboration
Public note: ATLAS-TDR-030
Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning
A. J. Larkoski, I. Moult, and B. Nachman
Physics Reports 841 (2020) 1 · e-Print: 1709.04464
Observables for possible QGP signatures in central pp collisions
M. Mangano and B. Nachman
Eur. Phys. J. C 78 (2018) 343 · e-Print: 1708.08369
Classification without labels: Learning from mixed samples in high energy physics
E. Metodiev, B. Nachman, J. Thaler
JHEP 10 (2017) 174. · e-Print: 1708.02949
Modelling of Track Reconstruction Inside Jets with the 2016 ATLAS sqrt(s) = 13 TeV pp Dataset
ATLAS Collaboration
Public note: ATL-PHYS-PUB-2017-016
Quark and gluon tagging with Jet Images in ATLAS
ATLAS Collaboration
Public note: ATL-PHYS-PUB-2017-017
Jet reclustering and close-by effects in ATLAS Run 2
ATLAS Collaboration
Public note: ATLAS-CONF-2017-062
In-situ measurements of large-radius jet reconstruction performance
ATLAS Collaboration
Public note: ATLAS-CONF-2017-063
Pileup Mitigation with Machine Learning (PUMML)
P. Komiske, E. Metodiev, B. Nachman, and M. Schwartz
JHEP 12 (2017) 51. · e-Print: 1707.08600
Accelerating science with generative adversarial networks: An application to 3D particle showers in multilayer calorimeters
M. Paganini, L. de Oliveira, and B. Nachman
Phys. Rev. Lett. 120 (2018) 042003 · e-Print: 1705.02355
Quark versus Gluon Jet Tagging Using Charged Particle Multiplicity with the ATLAS Detector
ATLAS Collaboration
Public note: ATL-PHYS-PUB-2017-009
Weakly Supervised Classification in High Energy Physics
L. M. Dery, B. Nachman, F. Rubbo, A. Schwartzman
JHEP 05 (2017) 145. · e-Print: 1702.00414
Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
L. de Oliveira, M. Paganini, and B. Nachman
Computing and Software for Big Science 1 (2017) 4 · e-Print: 1701.05927
Mathematical Properties of Numerical Inversion for Jet Calibrations
A. Cukierman and B. Nachman
Nucl. Instrum. Meth. A 858 (2017) 1. · e-Print: 1609.05195
Search strategy using LHC pileup interactions as a zero bias sample
B. Nachman and F. Rubbo
Phys. Rev. D 97 (2018) 092002 · e-Print: 1608.06299
Jet mass reconstruction with the ATLAS Detector in early Run 2 data
ATLAS Collaboration
Public note: ATLAS-CONF-2016-035
Search for top squarks in final states with one isolated lepton, jets, and missing transverse momentum in sqrt(s) = 13 TeV pp collisions with the ATLAS detector
ATLAS Collaboration
Phys. Rev. D94 (2016) 052009 · e-Print: 1606.03903
Search for top squarks in final states with one isolated lepton, jets, and missing transverse momentum in sqrt(s) = 13 TeV pp collisions of ATLAS data
ATLAS Collaboration
Public note: ATLAS-CONF-2016-007
Measurement of the jet mass scale and resolution uncertainty for large radius jets at sqrt(s) = 8 TeV using the ATLAS detector
ATLAS Collaboration
Public note: ATLAS-CONF-2016-008
Measurement of the charged particle multiplicity inside jets from sqrt(s) = 8 TeV pp collisions with the ATLAS detector
ATLAS Collaboration
Eur. Phys. J. C 76 (2016) 1 · e-Print: 1602.00988
Simulation of top quark production for the ATLAS experiment at sqrt(s) = 13 TeV
ATLAS Collaboration
Public note: ATL-PHYS-PUB-2016-004
Jet-Images -- Deep Learning Edition
L. de Oliveira, M. Kagan, L. Mackey, B. Nachman, and A. Schwartzman
JHEP 07 (2016) 069. · e-Print: 1511.05190
Performance of jet substructure techniques in early sqrt(s)=13 TeV pp collisions with the ATLAS detector
ATLAS Collaboration
Public note: ATLAS-CONF-2015-035
Measurement of jet charge in dijet events from sqrt(s) = 8 TeV pp collisions with the ATLAS detector
ATLAS Collaboration
Phys. Rev. D 93, 052003 (2016) · e-Print: 1509.05190
A new method to distinguish hadronically decaying boosted Z bosons from W bosons using the ATLAS detector
ATLAS Collaboration
Eur. Phys. J. C76 (2016) 238 · e-Print: 1509.04939
Fuzzy Jets
L. Mackey, B. Nachman, A. Schwartzman, and C. Stansbury
JHEP 06 (2016) 010. · e-Print: 1509.02216
Superposition Coding is Almost Always Optimal for the Poisson Broadcast Channel
H. Kim, B. Nachman, and A. El Gamal
IEEE Transactions on Information Theory 62 (2015) 1782 · e-Print: 1508.04228
Measurement of jet charge in dijet events from sqrt(s) = 8 TeV pp collisions with the ATLAS detector
ATLAS Collaboration
Public note: ATLAS-CONF-2015-025
Measurement of colour flow with the jet pull angle in ttbar events using the ATLAS detector at sqrt(s) = 8 TeV
ATLAS Collaboration
Phys. Lett. B (2015) 475 · e-Print: 1506.05629
Less is More when Gluinos Mediate
B. Nachman
Mod. Phys. Lett. A, 31, 1650052 (2016) · e-Print: 1505.00994
A fast, simple, and naturally machine-precision algorithm for calculating both symmetric and asymmetric MT2, for any physical inputs
C.G. Lester and B. Nachman
JHEP 03 (2015) 100 · e-Print: 1411.4312
Sneaky Light Stop
T. Eifert and B. Nachman
Phys. Lett. B 743 (2015) 218. · e-Print: 1410.7025
A Meta-analysis of the 8 TeV ATLAS and CMS SUSY Searches
B. Nachman and T. Rudelius
JHEP 02 (2015) 004. · e-Print: 1410.2270
Jets from Jets: Re-clustering as a tool for large radius jet reconstruction and grooming at the LHC
B. Nachman. P. Nef, A. Schwartzman, M. Swiatlowski, and C. Wanotayaroj
JHEP 02 (2015) 075. · e-Print: 1407.2922
Reconstruction and Modelling of Jet Pull with the ATLAS Detector
ATLAS Collaboration
Public note: ATLAS-CONF-2014-048
Search for top squark pair production in final states with one isolated lepton, jets, and missing transverse momentum in sqrt(s)= 8 TeV
ATLAS Collaboration
JHEP 11 (2014) 118 · e-Print: 1407.0583
Investigating Multiple Solutions in the Constrained Minimal Supersymmetric Standard Model
B.C. Allanach, Damien P. George, and Benjamin Nachman
JHEP 02 (2014) 031 · e-Print: 1311.3960
Jet Charge Studies with the ATLAS Detector Using sqrt(s) = 8 TeV Proton-Proton Collision Data
ATLAS Collaboration
Public note: ATLAS-CONF-2013-086
Measurement of masses in the ttbar system by kinematic endpoints in pp collisions at sqrt(s) = 7 TeV
CMS Collaboration
Eur. Phys. J. C 73 (2013) 2494 · e-Print: 1304.5783
Significance Variables
B. Nachman and C. G. Lester
Phys. Rev. D88 (2013) 075013 · e-Print: 1303.7009
Search for Direct Top Squark Pair Production in Final States with One Isolated Lepton, Jets, and Missing Transverse Momentum in sqrt(s) = 8 TeV pp Collisions using 210/fb of ATLAS Data
ATLAS Collaboration
Public note: ATLAS-CONF-2013-037
Droplet Breakup of the Nematic Liquid Crystal MBBA
B. Nachman and I. Cohen
e-Print: 1212.5976
Search for Direct Top Squark Pair Production in Final States with One Isolated Lepton, Jets, and Missing Transverse Momentum in sqrt(s) = 8 TeV pp Collisions using 130/fb of ATLAS Data
ATLAS Collaboration
Public note: ATLAS-CONF-2012-166
Generating Sequences of PSL(2,p)
B. Nachman
J. Group Theory 17 (2014) 925 · e-Print: 1210.2073
Evidence for Conservatism in SUSY Searches
B. Nachman and T. Rudelius
Eur. Phys. J. Plus (2012) 127: 157 · e-Print: 1209.3522