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09/12/2023
Flows for Flows: Morphing one Dataset into another with Maximum Likelihood Estimation
Many components of data analysis in high energy physics and beyond requi...
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08/23/2023
Improving Generative Model-based Unfolding with Schrödinger Bridges
Machine learning-based unfolding has enabled unbinned and high-dimension...
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07/10/2023
Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation
Score based generative models are a new class of generative models that ...
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06/06/2023
High-dimensional and Permutation Invariant Anomaly Detection
Methods for anomaly detection of new physics processes are often limited...
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05/17/2023
Learning Likelihood Ratios with Neural Network Classifiers
The likelihood ratio is a crucial quantity for statistical inference in ...
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05/12/2023
ELSA – Enhanced latent spaces for improved collider simulations
Simulations play a key role for inference in collider physics. We explor...
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05/05/2023
Weakly-Supervised Anomaly Detection in the Milky Way
Large-scale astrophysics datasets present an opportunity for new machine...
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02/10/2023
Unbinned Profiled Unfolding
Unfolding is an important procedure in particle physics experiments whic...
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12/20/2022
Resonant Anomaly Detection with Multiple Reference Datasets
An important class of techniques for resonant anomaly detection in high ...
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10/20/2022
Machine-Learning Compression for Particle Physics Discoveries
In collider-based particle and nuclear physics experiments, data are pro...
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05/10/2022
Bias and Priors in Machine Learning Calibrations for High Energy Physics
Machine learning offers an exciting opportunity to improve the calibrati...
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04/08/2022
Exploring the Universality of Hadronic Jet Classification
The modeling of jet substructure significantly differs between Parton Sh...
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03/15/2022
New directions for surrogate models and differentiable programming for High Energy Physics detector simulation
The computational cost for high energy physics detector simulation in fu...
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12/07/2021
Machine Learning in the Search for New Fundamental Physics
Machine learning plays a crucial role in enhancing and accelerating the ...
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11/27/2021
Learning from learning machines: a new generation of AI technology to meet the needs of science
We outline emerging opportunities and challenges to enhance the utility ...
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11/11/2021
Online-compatible Unsupervised Non-resonant Anomaly Detection
There is a growing need for anomaly detection methods that can broaden t...
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09/16/2021
A Cautionary Tale of Decorrelating Theory Uncertainties
A variety of techniques have been proposed to train machine learning cla...
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07/06/2021
New Methods and Datasets for Group Anomaly Detection From Fundamental Physics
The identification of anomalous overdensities in data - group or collect...
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06/01/2021
Latent Space Refinement for Deep Generative Models
Deep generative models are becoming widely used across science and indus...
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05/10/2021
Scaffolding Simulations with Deep Learning for High-dimensional Deconvolution
A common setting for scientific inference is the ability to sample from ...
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04/05/2021
Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection
Anomaly detection techniques are growing in importance at the Large Hadr...
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02/02/2021
A Living Review of Machine Learning for Particle Physics
Modern machine learning techniques, including deep learning, are rapidly...
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01/18/2021
E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once
There have been a number of recent proposals to enhance the performance ...
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12/08/2020
Beyond 4D Tracking: Using Cluster Shapes for Track Seeding
Tracking is one of the most time consuming aspects of event reconstructi...
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09/04/2020
Simulation-Assisted Decorrelation for Resonant Anomaly Detection
A growing number of weak- and unsupervised machine learning approaches t...
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09/03/2020
DCTRGAN: Improving the Precision of Generative Models with Reweighting
Significant advances in deep learning have led to more widely used and p...
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08/14/2020
GANplifying Event Samples
A critical question concerning generative networks applied to event gene...
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01/14/2020
Simulation Assisted Likelihood-free Anomaly Detection
Given the lack of evidence for new particle discoveries at the Large Had...
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01/14/2020
Anomaly Detection with Density Estimation
We leverage recent breakthroughs in neural density estimation to propose...
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11/20/2019
OmniFold: A Method to Simultaneously Unfold All Observables
Collider data must be corrected for detector effects ("unfolded") to be ...
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10/18/2019
AI Safety for High Energy Physics
The field of high-energy physics (HEP), along with many scientific disci...
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07/18/2019
Neural Networks for Full Phase-space Reweighting and Parameter Tuning
Precise scientific analysis in collider-based particle physics is possib...
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01/30/2018
Learning to Classify from Impure Samples
A persistent challenge in practical classification tasks is that labelle...
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08/09/2017
Classification without labels: Learning from mixed samples in high energy physics
Modern machine learning techniques can be used to construct powerful mod...
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07/26/2017
Pileup Mitigation with Machine Learning (PUMML)
Pileup involves the contamination of the energy distribution arising fro...
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05/05/2017
CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
Simulation is a key component of physics analysis in particle physics an...
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02/01/2017
Weakly Supervised Classification in High Energy Physics
As machine learning algorithms become increasingly sophisticated to expl...
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01/20/2017
Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
We provide a bridge between generative modeling in the Machine Learning ...
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11/16/2015
Jet-Images -- Deep Learning Edition
Building on the notion of a particle physics detector as a camera and th...
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09/07/2015
