Publications (90)
Parameterized Machine Learning for High-Energy Physics
Pierre Baldi, Kyle Cranmer, Taylor Faucett +2
Natural Priors, CMSSM Fits and LHC Weather Forecasts
Ben C Allanach, Kyle Cranmer, Christopher G Lester +1
AI for Science: An Emerging Agenda
Philipp Berens, Kyle Cranmer, Neil D. Lawrence +2
QCD-Aware Recursive Neural Networks for Jet Physics
Gilles Louppe, Kyunghyun Cho, Cyril Becot +1
Modeling Smooth Backgrounds and Generic Localized Signals with Gaussian Processes
Meghan Frate, Kyle Cranmer, Saarik Kalia +2
Data and Analysis Preservation, Recasting, and Reinterpretation
Stephen Bailey, Christian Bierlich, Andy Buckley +15
Exploring the Quantum Universe: Pathways to Innovation and Discovery in Particle Physics
Shoji Asai, Amalia Ballarino, Tulika Bose +29
Simulation-based inference methods for particle physics
Johann Brehmer, Kyle Cranmer
Neural Quasiprobabilistic Likelihood Ratio Estimation with Negatively Weighted Data
Matthew Drnevich, Stephen Jiggins, Judith Katzy +1
Statistical Challenges for Searches for New Physics at the LHC
Kyle Cranmer
Publishing statistical models: Getting the most out of particle physics experiments
Kyle Cranmer, Sabine Kraml, Harrison B. Prosper +30
Adversarial Variational Optimization of Non-Differentiable Simulators
Gilles Louppe, Joeri Hermans, Kyle Cranmer
Aspects of scaling and scalability for flow-based sampling of lattice QCD
Ryan Abbott, Michael S. Albergo, Aleksandar Botev +10
Configurable calorimeter simulation for AI applications
Francesco Armando Di Bello, Anton Charkin-Gorbulin, Kyle Cranmer +9
Constraining Effective Field Theories with Machine Learning
Johann Brehmer, Kyle Cranmer, Gilles Louppe +1
Yadage and Packtivity - analysis preservation using parametrized workflows
Kyle Cranmer, Lukas Heinrich
Flow-based sampling for fermionic lattice field theories
Michael S. Albergo, Gurtej Kanwar, Sébastien Racanière +6
Multimodal Datasets with Controllable Mutual Information
Raheem Karim Hashmani, Garrett W. Merz, Helen Qu +2
Equivariant flow-based sampling for lattice gauge theory
Gurtej Kanwar, Michael S. Albergo, Denis Boyda +5
Machine Learning in High Energy Physics Community White Paper
Kim Albertsson, Piero Altoe, Dustin Anderson +125
A deep search for decaying dark matter with XMM-Newton blank-sky observations
Joshua W. Foster, Marius Kongsore, Christopher Dessert +4
Power-Constrained Limits
Glen Cowan, Kyle Cranmer, Eilam Gross +1
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
Atılım GüneŠBaydin, Lei Shao, Wahid Bhimji +14
Simulation Intelligence: Towards a New Generation of Scientific Methods
Alexander Lavin, David Krakauer, Hector Zenil +21
Learning to Pivot with Adversarial Networks
Gilles Louppe, Michael Kagan, Kyle Cranmer
A Roadmap for HEP Software and Computing R&D for the 2020s
Johannes Albrecht, Antonio Augusto Alves, Guilherme Amadio +307
Maximum Significance at the LHC and Higgs Decays to Muons
Kyle Cranmer, Tilman Plehn
A Guide to Constraining Effective Field Theories with Machine Learning
Johann Brehmer, Kyle Cranmer, Gilles Louppe +1
Analysis Facilities for HL-LHC
Doug Benjamin, Kenneth Bloom, Brian Bockelman +9
HEP Software Foundation Community White Paper Working Group - Data Analysis and Interpretation
Lothar Bauerdick, Riccardo Maria Bianchi, Brian Bockelman +26
Backdrop: Stochastic Backpropagation
Siavash Golkar, Kyle Cranmer
Machine learning and the physical sciences
Giuseppe Carleo, Ignacio Cirac, Kyle Cranmer +5
Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning
Abhijith Gandrakota, Lily Zhang, Aahlad Puli +4
EFT Workshop at Notre Dame
Nick Smith, Daniel Spitzbart, Jennet Dickinson +27
Scaling MadMiner with a deployment on REANA
Irina Espejo, Sinclert Pérez, Kenyi Hurtado +2
Extending RECAST for Truth-Level Reinterpretations
Alex Schuy, Lukas Heinrich, Kyle Cranmer +1
Building an AI-native Research Ecosystem for Experimental Particle Physics: A Community Vision
Thea Klaeboe Aarrestad, Alaa Abdelhamid, Haider Abidi +457
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
Atılım GüneŠBaydin, Lukas Heinrich, Wahid Bhimji +12
Effective LHC measurements with matrix elements and machine learning
Johann Brehmer, Kyle Cranmer, Irina Espejo +3
Decoupling Theoretical Uncertainties from Measurements of the Higgs Boson
Kyle Cranmer, Sven Kreiss, David Lopez-Val +1
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators
Mario Lezcano Casado, Atilim Gunes Baydin, David Martinez Rubio +8
Status Report of the DPHEP Study Group: Towards a Global Effort for Sustainable Data Preservation in High Energy Physics
Z. Akopov, Silvia Amerio, David Asner +87
Statistical Challenges of Global SUSY Fits
Roberto Trotta, Kyle Cranmer
Broadening the scope of Education, Career and Open Science in HEP
Sudhir Malik, David DeMuth, Sijbrand de Jong +10
Semi-parametric -ray modeling with Gaussian processes and variational inference
Siddharth Mishra-Sharma, Kyle Cranmer
Self-Supervised Learning Strategies for Jet Physics
Patrick Rieck, Kyle Cranmer, Etienne Dreyer +5
The frontier of simulation-based inference
Kyle Cranmer, Johann Brehmer, Gilles Louppe
Approximating Likelihood Ratios with Calibrated Discriminative Classifiers
Kyle Cranmer, Juan Pavez, Gilles Louppe
Sampling using gauge equivariant flows
Denis Boyda, Gurtej Kanwar, Sébastien Racanière +5
Inferring the quantum density matrix with machine learning
Kyle Cranmer, Siavash Golkar, Duccio Pappadopulo
Flow-based sampling for multimodal and extended-mode distributions in lattice field theory
Daniel C. Hackett, Chung-Chun Hsieh, Sahil Pontula +7
MadMiner: Machine learning-based inference for particle physics
Johann Brehmer, Felix Kling, Irina Espejo +1
A neural simulation-based inference approach for characterizing the Galactic Center -ray excess
Siddharth Mishra-Sharma, Kyle Cranmer
Normalizing Flows on Tori and Spheres
Danilo Jimenez Rezende, George Papamakarios, Sébastien Racanière +4
PhysicsGP: A Genetic Programming Approach to Event Selection
Kyle Cranmer, R. Sean Bowman
Mining gold from implicit models to improve likelihood-free inference
Johann Brehmer, Gilles Louppe, Juan Pavez +1
The RooStats Project
Lorenzo Moneta, Kevin Belasco, Kyle Cranmer +6
Reframing Jet Physics with New Computational Methods
Kyle Cranmer, Matthew Drnevich, Sebastian Macaluso +1
Discovering Symbolic Models from Deep Learning with Inductive Biases
Miles Cranmer, Alvaro Sanchez-Gonzalez, Peter Battaglia +4
Asymptotic distribution for two-sided tests with lower and upper boundaries on the parameter of interest
Glen Cowan, Kyle Cranmer, Eilam Gross +1
Recurrent Features of Amplitudes in Planar Super Yang-Mills Theory
Tianji Cai, François Charton, Kyle Cranmer +3
Likelihood-free inference with an improved cross-entropy estimator
Markus Stoye, Johann Brehmer, Gilles Louppe +2
Data Structures & Algorithms for Exact Inference in Hierarchical Clustering
Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath +5
Asymptotic formulae for likelihood-based tests of new physics
Glen Cowan, Kyle Cranmer, Eilam Gross +1
Introduction to Normalizing Flows for Lattice Field Theory
Michael S. Albergo, Denis Boyda, Daniel C. Hackett +5
The 200 Gbps Challenge: Imagining HL-LHC analysis facilities
Alexander Held, Sam Albin, Garhan Attebury +22
Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning
Johann Brehmer, Siddharth Mishra-Sharma, Joeri Hermans +2
Better Higgs Measurements Through Information Geometry
Johann Brehmer, Kyle Cranmer, Felix Kling +1
Efficient Estimation of Unfactorizable Systematic Uncertainties
Alexis Romero, Kyle Cranmer, Daniel Whiteson
Transforming the Bootstrap: Using Transformers to Compute Scattering Amplitudes in Planar N = 4 Super Yang-Mills Theory
Tianji Cai, Garrett W. Merz, François Charton +4
RECAST: Extending the Impact of Existing Analyses
Kyle Cranmer, Itay Yavin
Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions
Ryan Abbott, Michael S. Albergo, Denis Boyda +9
Set2Graph: Learning Graphs From Sets
Hadar Serviansky, Nimrod Segol, Jonathan Shlomi +4
Secondary Vertex Finding in Jets with Neural Networks
Jonathan Shlomi, Sanmay Ganguly, Eilam Gross +5
Practical Statistics for the LHC
Kyle Cranmer
Hamiltonian Graph Networks with ODE Integrators
Alvaro Sanchez-Gonzalez, Victor Bapst, Kyle Cranmer +1
Machine Learning and LHC Event Generation
Anja Butter, Tilman Plehn, Steffen Schumann +48
Flows for simultaneous manifold learning and density estimation
Johann Brehmer, Kyle Cranmer
Reconstructing conformal field theoretical compositions with Transformers
Haotian Cao, Garrett Merz, Kyle Cranmer +1
Quasiprobabilistic Density Ratio Estimation with a Reverse Engineered Classification Loss Function
Matthew Drnevich, Stephen Jiggins, Kyle Cranmer
Deep Learning and its Application to LHC Physics
Dan Guest, Kyle Cranmer, Daniel Whiteson
Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics
Kyle Cranmer, Gurtej Kanwar, Sébastien Racanière +2
Exact and Approximate Hierarchical Clustering Using A*
Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath +6
Observing Ultra-High Energy Cosmic Rays with Smartphones
Daniel Whiteson, Michael Mulhearn, Chase Shimmin +3
Flow-based sampling in the lattice Schwinger model at criticality
Michael S. Albergo, Denis Boyda, Kyle Cranmer +7
Normalizing flows for lattice gauge theory in arbitrary space-time dimension
Ryan Abbott, Michael S. Albergo, Aleksandar Botev +11
Sampling QCD field configurations with gauge-equivariant flow models
Ryan Abbott, Michael S. Albergo, Aleksandar Botev +11
The Quantum Trellis: A classical algorithm for sampling the parton shower with interference effects
Sebastian Macaluso, Kyle Cranmer
Hierarchical clustering in particle physics through reinforcement learning
Johann Brehmer, Sebastian Macaluso, Duccio Pappadopulo +1
10 Simple Rules for the Care and Feeding of Scientific Data
Alyssa Goodman, Alberto Pepe, Alexander W. Blocker +12