Search Results for author: Benjamin Nachman

Found 55 papers, 33 papers with code

Designing Observables for Measurements with Deep Learning

1 code implementation12 Oct 2023 Owen Long, Benjamin Nachman

Many analyses in particle and nuclear physics use simulations to infer fundamental, effective, or phenomenological parameters of the underlying physics models.

Flows for Flows: Morphing one Dataset into another with Maximum Likelihood Estimation

no code implementations12 Sep 2023 Tobias Golling, Samuel Klein, Radha Mastandrea, Benjamin Nachman, John Andrew Raine

We propose a protocol called flows for flows for training normalizing flows to morph one dataset into another even if the underlying probability density of neither dataset is known explicitly.

MORPH

Improving Generative Model-based Unfolding with Schrödinger Bridges

1 code implementation23 Aug 2023 Sascha Diefenbacher, Guan-Horng Liu, Vinicius Mikuni, Benjamin Nachman, Weili Nie

Machine learning-based unfolding has enabled unbinned and high-dimensional differential cross section measurements.

Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation

1 code implementation10 Jul 2023 Fernando Torales Acosta, Vinicius Mikuni, Benjamin Nachman, Miguel Arratia, Bishnu Karki, Ryan Milton, Piyush Karande, Aaron Angerami

Score based generative models are a new class of generative models that have been shown to accurately generate high dimensional calorimeter datasets.

High-dimensional and Permutation Invariant Anomaly Detection

1 code implementation6 Jun 2023 Vinicius Mikuni, Benjamin Nachman

Methods for anomaly detection of new physics processes are often limited to low-dimensional spaces due to the difficulty of learning high-dimensional probability densities.

Anomaly Detection Density Estimation

Learning Likelihood Ratios with Neural Network Classifiers

no code implementations17 May 2023 Shahzar Rizvi, Mariel Pettee, Benjamin Nachman

The likelihood ratio is a crucial quantity for statistical inference in science that enables hypothesis testing, construction of confidence intervals, reweighting of distributions, and more.

ELSA -- Enhanced latent spaces for improved collider simulations

1 code implementation12 May 2023 Benjamin Nachman, Ramon Winterhalder

We explore various approaches for enhancing the precision of simulations using machine learning, including interventions at the end of the simulation chain (reweighting), at the beginning of the simulation chain (pre-processing), and connections between the end and beginning (latent space refinement).

Weakly-Supervised Anomaly Detection in the Milky Way

1 code implementation5 May 2023 Mariel Pettee, Sowmya Thanvantri, Benjamin Nachman, David Shih, Matthew R. Buckley, Jack H. Collins

Large-scale astrophysics datasets present an opportunity for new machine learning techniques to identify regions of interest that might otherwise be overlooked by traditional searches.

Supervised Anomaly Detection Weakly-supervised Anomaly Detection

Unbinned Profiled Unfolding

1 code implementation10 Feb 2023 Jay Chan, Benjamin Nachman

We propose a new machine learning-based unfolding method that results in an unbinned differential cross section and can profile nuisance parameters.

Resonant Anomaly Detection with Multiple Reference Datasets

no code implementations20 Dec 2022 Mayee F. Chen, Benjamin Nachman, Frederic Sala

An important class of techniques for resonant anomaly detection in high energy physics builds models that can distinguish between reference and target datasets, where only the latter has appreciable signal.

Anomaly Detection

Machine-Learning Compression for Particle Physics Discoveries

no code implementations20 Oct 2022 Jack H. Collins, Yifeng Huang, Simon Knapen, Benjamin Nachman, Daniel Whiteson

In collider-based particle and nuclear physics experiments, data are produced at such extreme rates that only a subset can be recorded for later analysis.

Score-based Generative Models for Calorimeter Shower Simulation

1 code implementation17 Jun 2022 Vinicius Mikuni, Benjamin Nachman

Score-based generative models are a new class of generative algorithms that have been shown to produce realistic images even in high dimensional spaces, currently surpassing other state-of-the-art models for different benchmark categories and applications.

Bias and Priors in Machine Learning Calibrations for High Energy Physics

1 code implementation10 May 2022 Rikab Gambhir, Benjamin Nachman, Jesse Thaler

Machine learning offers an exciting opportunity to improve the calibration of nearly all reconstructed objects in high-energy physics detectors.

BIG-bench Machine Learning Vocal Bursts Intensity Prediction

Machine Learning in the Search for New Fundamental Physics

no code implementations7 Dec 2021 Georgia Karagiorgi, Gregor Kasieczka, Scott Kravitz, Benjamin Nachman, David Shih

Machine learning plays a crucial role in enhancing and accelerating the search for new fundamental physics.

BIG-bench Machine Learning

Online-compatible Unsupervised Non-resonant Anomaly Detection

1 code implementation11 Nov 2021 Vinicius Mikuni, Benjamin Nachman, David Shih

There is a growing need for anomaly detection methods that can broaden the search for new particles in a model-agnostic manner.

Anomaly Detection

A Cautionary Tale of Decorrelating Theory Uncertainties

no code implementations16 Sep 2021 Aishik Ghosh, Benjamin Nachman

A variety of techniques have been proposed to train machine learning classifiers that are independent of a given feature.

New Methods and Datasets for Group Anomaly Detection From Fundamental Physics

no code implementations6 Jul 2021 Gregor Kasieczka, Benjamin Nachman, David Shih

The identification of anomalous overdensities in data - group or collective anomaly detection - is a rich problem with a large number of real world applications.

Group Anomaly Detection

Latent Space Refinement for Deep Generative Models

1 code implementation1 Jun 2021 Ramon Winterhalder, Marco Bellagente, Benjamin Nachman

Deep generative models are becoming widely used across science and industry for a variety of purposes.

Scaffolding Simulations with Deep Learning for High-dimensional Deconvolution

no code implementations10 May 2021 Anders Andreassen, Patrick T. Komiske, Eric M. Metodiev, Benjamin Nachman, Adi Suresh, Jesse Thaler

A common setting for scientific inference is the ability to sample from a high-fidelity forward model (simulation) without having an explicit probability density of the data.

Vocal Bursts Intensity Prediction

Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection

no code implementations5 Apr 2021 Jack H. Collins, Pablo Martín-Ramiro, Benjamin Nachman, David Shih

We examine the ability of the two methods to identify a new physics signal at different cross sections in a fully hadronic resonance search.

Anomaly Detection

Quantum Gate Pattern Recognition and Circuit Optimization for Scientific Applications

1 code implementation19 Feb 2021 Wonho Jang, Koji Terashi, MasaHiko Saito, Christian W. Bauer, Benjamin Nachman, Yutaro Iiyama, Tomoe Kishimoto, Ryunosuke Okubo, Ryu Sawada, Junichi Tanaka

The first ingredient is a technique to recognize repeated patterns of quantum gates, opening up the possibility of future hardware co-optimization.

Quantum Physics

A Living Review of Machine Learning for Particle Physics

1 code implementation2 Feb 2021 Matthew Feickert, Benjamin Nachman

Modern machine learning techniques, including deep learning, are rapidly being applied, adapted, and developed for high energy physics.

BIG-bench Machine Learning

E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once

no code implementations18 Jan 2021 Benjamin Nachman, Jesse Thaler

There have been a number of recent proposals to enhance the performance of machine learning strategies for collider physics by combining many distinct events into a single ensemble feature.

Beyond 4D Tracking: Using Cluster Shapes for Track Seeding

no code implementations8 Dec 2020 Patrick J. Fox, Shangqing Huang, Joshua Isaacson, Xiangyang Ju, Benjamin Nachman

The shape of clusters provides an additional dimension for track seeding that can significantly reduce the combinatorial challenge of track finding.

Enhancing searches for resonances with machine learning and moment decomposition

1 code implementation19 Oct 2020 Ouail Kitouni, Benjamin Nachman, Constantin Weisser, Mike Williams

A key challenge in searches for resonant new physics is that classifiers trained to enhance potential signals must not induce localized structures.

High Energy Physics - Phenomenology High Energy Physics - Experiment Data Analysis, Statistics and Probability

Readout Rebalancing for Near Term Quantum Computers

1 code implementation15 Oct 2020 Rebecca Hicks, Christian W. Bauer, Benjamin Nachman

These X gates are placed so that the expected number of qubits in the 1 state is minimized.

Quantum Physics

Parameter Estimation using Neural Networks in the Presence of Detector Effects

1 code implementation7 Oct 2020 Anders Andreassen, Shih-Chieh Hsu, Benjamin Nachman, Natchanon Suaysom, Adi Suresh

Histogram-based template fits are the main technique used for estimating parameters of high energy physics Monte Carlo generators.

Simulation-Assisted Decorrelation for Resonant Anomaly Detection

1 code implementation4 Sep 2020 Kees Benkendorfer, Luc Le Pottier, Benjamin Nachman

A growing number of weak- and unsupervised machine learning approaches to anomaly detection are being proposed to significantly extend the search program at the Large Hadron Collider and elsewhere.

Anomaly Detection BIG-bench Machine Learning

DCTRGAN: Improving the Precision of Generative Models with Reweighting

2 code implementations3 Sep 2020 Sascha Diefenbacher, Engin Eren, Gregor Kasieczka, Anatolii Korol, Benjamin Nachman, David Shih

We introduce a post-hoc correction to deep generative models to further improve their fidelity, based on the Deep neural networks using the Classification for Tuning and Reweighting (DCTR) protocol.

GANplifying Event Samples

no code implementations14 Aug 2020 Anja Butter, Sascha Diefenbacher, Gregor Kasieczka, Benjamin Nachman, Tilman Plehn

A critical question concerning generative networks applied to event generation in particle physics is if the generated events add statistical precision beyond the training sample.

Supervised Jet Clustering with Graph Neural Networks for Lorentz Boosted Bosons

1 code implementation13 Aug 2020 Xiangyang Ju, Benjamin Nachman

By labeling simulated final state hadrons as descending from an uncolored particle, it is possible to train a supervised learning method to create boson jets.

High Energy Physics - Phenomenology High Energy Physics - Experiment Data Analysis, Statistics and Probability

Anomaly Detection with Density Estimation

1 code implementation14 Jan 2020 Benjamin Nachman, David Shih

By estimating the probability density of the data in a signal region and in sidebands, and interpolating the latter into the signal region, a likelihood ratio of data vs. background can be constructed.

Density Estimation Unsupervised Anomaly Detection

Simulation Assisted Likelihood-free Anomaly Detection

1 code implementation14 Jan 2020 Anders Andreassen, Benjamin Nachman, David Shih

For potential signals that are resonant in one known feature, this new method first learns a parameterized reweighting function to morph a given simulation to match the data in sidebands.

Anomaly Detection MORPH

OmniFold: A Method to Simultaneously Unfold All Observables

2 code implementations20 Nov 2019 Anders Andreassen, Patrick T. Komiske, Eric M. Metodiev, Benjamin Nachman, Jesse Thaler

Collider data must be corrected for detector effects ("unfolded") to be compared with many theoretical calculations and measurements from other experiments.

AI Safety for High Energy Physics

no code implementations18 Oct 2019 Benjamin Nachman, Chase Shimmin

The field of high-energy physics (HEP), along with many scientific disciplines, is currently experiencing a dramatic influx of new methodologies powered by modern machine learning techniques.

Vocal Bursts Intensity Prediction

Error detection on quantum computers improves accuracy of chemical calculations

no code implementations30 Sep 2019 Miroslav Urbanek, Benjamin Nachman, Wibe Albert de Jong

A major milestone of quantum error correction is to achieve the fault-tolerance threshold beyond which quantum computers can be made arbitrarily accurate.

Quantum Physics Chemical Physics

Neural Networks for Full Phase-space Reweighting and Parameter Tuning

3 code implementations18 Jul 2019 Anders Andreassen, Benjamin Nachman

We therefore introduce Deep neural networks using Classification for Tuning and Reweighting (DCTR), a neural network-based approach to reweight and fit simulations using all kinematic and flavor information -- the full phase space.

Anomaly Detection for Resonant New Physics with Machine Learning

1 code implementation7 May 2018 Jack H. Collins, Kiel Howe, Benjamin Nachman

Despite extensive theoretical motivation for physics beyond the Standard Model (BSM) of particle physics, searches at the Large Hadron Collider (LHC) have found no significant evidence for BSM physics.

High Energy Physics - Phenomenology High Energy Physics - Experiment

The Optimal Use of Silicon Pixel Charge Information for Particle Identification

1 code implementation23 Mar 2018 Harley Patton, Benjamin Nachman

Particle identification using the energy loss in silicon detectors is a powerful technique for probing the Standard Model (SM) as well as searching for new particles beyond the SM.

Instrumentation and Detectors

CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks

3 code implementations21 Dec 2017 Michela Paganini, Luke de Oliveira, Benjamin Nachman

The precise modeling of subatomic particle interactions and propagation through matter is paramount for the advancement of nuclear and particle physics searches and precision measurements.

Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters

no code implementations23 Nov 2017 Luke de Oliveira, Michela Paganini, Benjamin Nachman

High-precision modeling of subatomic particle interactions is critical for many fields within the physical sciences, such as nuclear physics and high energy particle physics.

Attribute Generative Adversarial Network

Classification without labels: Learning from mixed samples in high energy physics

1 code implementation9 Aug 2017 Eric M. Metodiev, Benjamin Nachman, Jesse Thaler

In this paper, we introduce the paradigm of classification without labels (CWoLa) in which a classifier is trained to distinguish statistical mixtures of classes, which are common in collider physics.

General Classification Vocal Bursts Intensity Prediction

Pileup Mitigation with Machine Learning (PUMML)

1 code implementation26 Jul 2017 Patrick T. Komiske, Eric M. Metodiev, Benjamin Nachman, Matthew D. Schwartz

Pileup involves the contamination of the energy distribution arising from the primary collision of interest (leading vertex) by radiation from soft collisions (pileup).

BIG-bench Machine Learning

Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters

4 code implementations5 May 2017 Michela Paganini, Luke de Oliveira, Benjamin Nachman

Physicists at the Large Hadron Collider (LHC) rely on detailed simulations of particle collisions to build expectations of what experimental data may look like under different theory modeling assumptions.

Weakly Supervised Classification in High Energy Physics

no code implementations1 Feb 2017 Lucio Mwinmaarong Dery, Benjamin Nachman, Francesco Rubbo, Ariel Schwartzman

As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations.

BIG-bench Machine Learning Binary Classification +4

Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis

3 code implementations20 Jan 2017 Luke de Oliveira, Michela Paganini, Benjamin Nachman

We provide a bridge between generative modeling in the Machine Learning community and simulated physical processes in High Energy Particle Physics by applying a novel Generative Adversarial Network (GAN) architecture to the production of jet images -- 2D representations of energy depositions from particles interacting with a calorimeter.

Generative Adversarial Network

Jet-Images -- Deep Learning Edition

1 code implementation16 Nov 2015 Luke de Oliveira, Michael Kagan, Lester Mackey, Benjamin Nachman, Ariel Schwartzman

Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning architectures to identify highly boosted W bosons.

Jet Tagging

Fuzzy Jets

no code implementations7 Sep 2015 Lester Mackey, Benjamin Nachman, Ariel Schwartzman, Conrad Stansbury

Collimated streams of particles produced in high energy physics experiments are organized using clustering algorithms to form jets.

Clustering Jet Tagging

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