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.

Featured Researchers

João Caldeira
8 publications
Lindsey Gray
5 publications
Brian Nord
5 publications
B. Nord
5 publications
G. N. Perdue
4 publications
Oliver Gutsche
3 publications
Matteo Cremonesi
3 publications
D. Kafkes
2 publications
Joshua Isaacson
2 publications
Yuyi Guo
1 publication
M. Mooney
1 publication

Inferring Convolutional Neural Networks' accuracies from their architectural characterizations

Convolutional Neural Networks (CNNs) have shown strong promise for analy...
01/07/2020 ∙ by D. Hoang, et al. ∙ 27 ∙ share

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

In astronomy, neural networks are often trained on simulation data with ...
03/02/2021 ∙ by A. Ćiprijanović, et al. ∙ 4 ∙ share

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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...
08/22/2018 ∙ by MicroBooNE collaboration, et al. ∙ 2 ∙ share

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CMS Analysis and Data Reduction with Apache Spark

Experimental Particle Physics has been at the forefront of analyzing the...
10/31/2017 ∙ by Oliver Gutsche, et al. ∙ 0 ∙ share

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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...
01/11/2018 ∙ by Valentin Kuznetsov, et al. ∙ 0 ∙ share

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Using Big Data Technologies for HEP Analysis

The HEP community is approaching an era were the excellent performances ...
01/22/2019 ∙ by Matteo Cremonesi, et al. ∙ 0 ∙ share

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A Dynamic Reduction Network for Point Clouds

Classifying whole images is a classic problem in machine learning, and g...
03/18/2020 ∙ by Lindsey Gray, et al. ∙ 0 ∙ share

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

We present a comparison of methods for uncertainty quantification (UQ) i...
04/22/2020 ∙ by João Caldeira, et al. ∙ 0 ∙ share

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Bayesian model averaging for analysis of lattice field theory results

Statistical modeling is a key component in the extraction of physical re...
08/03/2020 ∙ by William I. Jay, et al. ∙ 0 ∙ share

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

In astronomy, neural networks are often trained on simulated data with t...
11/06/2020 ∙ by A. Ćiprijanović, et al. ∙ 0 ∙ share

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Beyond 4D Tracking: Using Cluster Shapes for Track Seeding

Tracking is one of the most time consuming aspects of event reconstructi...
12/08/2020 ∙ by Patrick J. Fox, et al. ∙ 0 ∙ share

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