Fermilab

∙ Batavia , United States ∙ 0 followers

Fermi National Accelerator Laboratory, located just outside Batavia, Illinois, near Chicago, is a United States Department of Energy national laboratory specializing in high-energy particle physics.

research

∙ 12/28/2021

DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification

Data processing and analysis pipelines in cosmological survey experiment...

Aleksandra Ćiprijanović, et al. ∙

research

∙ 01/07/2020

Inferring Convolutional Neural Networks' accuracies from their architectural characterizations

Convolutional Neural Networks (CNNs) have shown strong promise for analy...

D. Hoang, et al. ∙

research

∙ 03/15/2022

Machine Learning and Cosmology

Methods based on machine learning have recently made substantial inroads...

Cora Dvorkin, et al. ∙

research

∙ 03/02/2021

DeepMerge II: Building Robust Deep Learning Algorithms for Merging Galaxy Identification Across Domains

In astronomy, neural networks are often trained on simulation data with ...

A. Ćiprijanović, et al. ∙

research

∙ 03/16/2022

Learning Representation for Bayesian Optimization with Collision-free Regularization

Bayesian optimization has been challenged by datasets with large-scale, ...

Fengxue Zhang, et al. ∙

research

∙ 08/22/2018

A Deep Neural Network for Pixel-Level Electromagnetic Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber

We have developed a convolutional neural network (CNN) that can make a p...

MicroBooNE collaboration, et al. ∙

research

∙ 10/31/2017

CMS Analysis and Data Reduction with Apache Spark

Experimental Particle Physics has been at the forefront of analyzing the...

Oliver Gutsche, et al. ∙

research

∙ 01/11/2018

The archive solution for distributed workflow management agents of the CMS experiment at LHC

The CMS experiment at the CERN LHC developed the Workflow Management Arc...

Valentin Kuznetsov, et al. ∙

research

∙ 01/22/2019

Using Big Data Technologies for HEP Analysis

The HEP community is approaching an era were the excellent performances ...

Matteo Cremonesi, et al. ∙

research

∙ 03/18/2020

A Dynamic Reduction Network for Point Clouds

Classifying whole images is a classic problem in machine learning, and g...

Lindsey Gray, et al. ∙

research

∙ 04/22/2020

Deeply Uncertain: Comparing Methods of Uncertainty Quantification in Deep Learning Algorithms

We present a comparison of methods for uncertainty quantification (UQ) i...

João Caldeira, et al. ∙

research

∙ 08/03/2020

Bayesian model averaging for analysis of lattice field theory results

Statistical modeling is a key component in the extraction of physical re...

William I. Jay, et al. ∙

research

∙ 11/06/2020

Domain adaptation techniques for improved cross-domain study of galaxy mergers

In astronomy, neural networks are often trained on simulated data with t...

A. Ćiprijanović, 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...

Patrick J. Fox, et al. ∙

research

∙ 11/01/2021

Robustness of deep learning algorithms in astronomy – galaxy morphology studies

Deep learning models are being increasingly adopted in wide array of sci...

A. Ćiprijanović, et al. ∙

research

∙ 12/06/2021

Autoencoders for Semivisible Jet Detection

The production of dark matter particles from confining dark sectors may ...

Florencia Canelli, et al. ∙

research

∙ 05/17/2022

The HEP Software Foundation Community

The HEP Software Foundation was founded in 2014 to tackle common problem...

Graeme A. Stewart, et al. ∙

Fermilab

Featured Researchers

Brian Nord

Sandeep Madireddy

João Caldeira

Kevin Pedro

Lindsey Gray

B. Nord

G. N. Perdue

Oliver Gutsche

Matteo Cremonesi

D. Kafkes

Joshua Isaacson

DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification

Inferring Convolutional Neural Networks' accuracies from their architectural characterizations

Machine Learning and Cosmology

DeepMerge II: Building Robust Deep Learning Algorithms for Merging Galaxy Identification Across Domains

Learning Representation for Bayesian Optimization with Collision-free Regularization

A Deep Neural Network for Pixel-Level Electromagnetic Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber

CMS Analysis and Data Reduction with Apache Spark

The archive solution for distributed workflow management agents of the CMS experiment at LHC

Using Big Data Technologies for HEP Analysis

A Dynamic Reduction Network for Point Clouds

Deeply Uncertain: Comparing Methods of Uncertainty Quantification in Deep Learning Algorithms

Bayesian model averaging for analysis of lattice field theory results

Domain adaptation techniques for improved cross-domain study of galaxy mergers

Beyond 4D Tracking: Using Cluster Shapes for Track Seeding

Robustness of deep learning algorithms in astronomy – galaxy morphology studies

Autoencoders for Semivisible Jet Detection

The HEP Software Foundation Community