Benjamin Nachman

research

∙ 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...

0 Tobias Golling, et al. ∙

research

∙ 08/23/2023

Improving Generative Model-based Unfolding with Schrödinger Bridges

Machine learning-based unfolding has enabled unbinned and high-dimension...

0 Sascha Diefenbacher, et al. ∙

research

∙ 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 ...

0 Fernando Torales Acosta, et al. ∙

research

∙ 06/06/2023

High-dimensional and Permutation Invariant Anomaly Detection

Methods for anomaly detection of new physics processes are often limited...

0 Vinicius Mikuni, et al. ∙

research

∙ 05/17/2023

Learning Likelihood Ratios with Neural Network Classifiers

The likelihood ratio is a crucial quantity for statistical inference in ...

0 Shahzar Rizvi, et al. ∙

research

∙ 05/12/2023

ELSA – Enhanced latent spaces for improved collider simulations

Simulations play a key role for inference in collider physics. We explor...

0 Benjamin Nachman, et al. ∙

research

∙ 05/05/2023

Weakly-Supervised Anomaly Detection in the Milky Way

Large-scale astrophysics datasets present an opportunity for new machine...

0 Mariel Pettee, et al. ∙

research

∙ 02/10/2023

Unbinned Profiled Unfolding

Unfolding is an important procedure in particle physics experiments whic...

0 Jay Chan, et al. ∙

research

∙ 12/20/2022

Resonant Anomaly Detection with Multiple Reference Datasets

An important class of techniques for resonant anomaly detection in high ...

0 Mayee F. Chen, et al. ∙

research

∙ 10/20/2022

Machine-Learning Compression for Particle Physics Discoveries

In collider-based particle and nuclear physics experiments, data are pro...

0 Jack H. Collins, et al. ∙

research

∙ 05/10/2022

Bias and Priors in Machine Learning Calibrations for High Energy Physics

Machine learning offers an exciting opportunity to improve the calibrati...

0 Rikab Gambhir, et al. ∙

research

∙ 04/08/2022

Exploring the Universality of Hadronic Jet Classification

The modeling of jet substructure significantly differs between Parton Sh...

0 Kingman Cheung, et al. ∙

research

∙ 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...

0 Andreas Adelmann, et al. ∙

research

∙ 12/07/2021

Machine Learning in the Search for New Fundamental Physics

Machine learning plays a crucial role in enhancing and accelerating the ...

0 Georgia Karagiorgi, et al. ∙

research

∙ 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 ...

26 Luca Pion-Tonachini, et al. ∙

research

∙ 11/11/2021

Online-compatible Unsupervised Non-resonant Anomaly Detection

There is a growing need for anomaly detection methods that can broaden t...

0 Vinicius Mikuni, et al. ∙

research

∙ 09/16/2021

A Cautionary Tale of Decorrelating Theory Uncertainties

A variety of techniques have been proposed to train machine learning cla...

0 Aishik Ghosh, et al. ∙

research

∙ 07/06/2021

New Methods and Datasets for Group Anomaly Detection From Fundamental Physics

The identification of anomalous overdensities in data - group or collect...

0 Gregor Kasieczka, et al. ∙

research

∙ 06/01/2021

Latent Space Refinement for Deep Generative Models

Deep generative models are becoming widely used across science and indus...

0 Ramon Winterhalder, et al. ∙

research

∙ 05/10/2021

Scaffolding Simulations with Deep Learning for High-dimensional Deconvolution

A common setting for scientific inference is the ability to sample from ...

0 Anders Andreassen, et al. ∙

research

∙ 04/05/2021

Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection

Anomaly detection techniques are growing in importance at the Large Hadr...

0 Jack H. Collins, et al. ∙

research

∙ 02/02/2021

A Living Review of Machine Learning for Particle Physics

Modern machine learning techniques, including deep learning, are rapidly...

0 Matthew Feickert, et al. ∙

research

∙ 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 ...

0 Benjamin Nachman, et al. ∙

research

∙ 12/08/2020

Beyond 4D Tracking: Using Cluster Shapes for Track Seeding

Tracking is one of the most time consuming aspects of event reconstructi...

0 Patrick J. Fox, et al. ∙

research

∙ 09/04/2020

Simulation-Assisted Decorrelation for Resonant Anomaly Detection

A growing number of weak- and unsupervised machine learning approaches t...

0 Kees Benkendorfer, et al. ∙

research

∙ 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...

0 Sascha Diefenbacher, et al. ∙

research

∙ 08/14/2020

GANplifying Event Samples

A critical question concerning generative networks applied to event gene...

0 Anja Butter, et al. ∙

research

∙ 01/14/2020

Simulation Assisted Likelihood-free Anomaly Detection

Given the lack of evidence for new particle discoveries at the Large Had...

10 Anders Andreassen, et al. ∙

research

∙ 01/14/2020

Anomaly Detection with Density Estimation

We leverage recent breakthroughs in neural density estimation to propose...

94 Benjamin Nachman, et al. ∙

research

∙ 11/20/2019

OmniFold: A Method to Simultaneously Unfold All Observables

Collider data must be corrected for detector effects ("unfolded") to be ...

0 Anders Andreassen, et al. ∙

research

∙ 10/18/2019

AI Safety for High Energy Physics

The field of high-energy physics (HEP), along with many scientific disci...

0 Benjamin Nachman, et al. ∙

research

∙ 07/18/2019

Neural Networks for Full Phase-space Reweighting and Parameter Tuning

Precise scientific analysis in collider-based particle physics is possib...

0 Anders Andreassen, et al. ∙

research

∙ 01/30/2018

Learning to Classify from Impure Samples

A persistent challenge in practical classification tasks is that labelle...

0 Patrick T. Komiske, et al. ∙

research

∙ 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...

0 Eric M. Metodiev, et al. ∙

research

∙ 07/26/2017

Pileup Mitigation with Machine Learning (PUMML)

Pileup involves the contamination of the energy distribution arising fro...

0 Patrick T. Komiske, et al. ∙

research

∙ 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...

0 Michela Paganini, et al. ∙

research

∙ 02/01/2017

Weakly Supervised Classification in High Energy Physics

As machine learning algorithms become increasingly sophisticated to expl...

0 Lucio Mwinmaarong Dery, et al. ∙

research

∙ 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 ...

0 Luke de Oliveira, et al. ∙

research

∙ 11/16/2015

Jet-Images -- Deep Learning Edition

Building on the notion of a particle physics detector as a camera and th...

0 Luke de Oliveira, et al. ∙

research

∙ 09/07/2015

Fuzzy Jets

Collimated streams of particles produced in high energy physics experime...

0 Lester Mackey, et al. ∙

Benjamin Nachman

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