output
20002026
most citedA principal component analysis of 39 scientific impact measures

586 citations

Showing 2020Show all

14 papers · 1 filter

physics.geo-ph2020★ 114 cited

Autonomous Extraction of Millimeter-scale Deformation in InSAR Time Series Using Deep Learning

Bertrand Rouet-Leduc, Romain Jolivet, Manon Dalaison +2

Systematic characterization of slip behaviours on active faults is key to unraveling the physics of tectonic faulting and the interplay between slow and fast earthquakes. Interfero…

physics.ins-det2020★ 9 cited

Measurement of the Atmospheric Muon Rate with the MicroBooNE Liquid Argon TPC

MicroBooNE collaboration, C. Adams, M. Alrashed +187

MicroBooNE is a near-surface liquid argon (LAr) time projection chamber (TPC) located at Fermilab. We measure the characterisation of muons originating from cosmic interactions in…

hep-ph2020★ 21 cited

New searches at the reactor experiments based on the dark axion portal

Patrick deNiverville, Hye-Sung Lee, Young-Min Lee

A nuclear reactor is a powerful tool to study neutrinos and light dark sector particles. Some of the reactor experiments have proven to be extremely useful already. Considering the…

physics.ins-det2020★ 1 cited

Neutrino Event Selection in the MicroBooNE Liquid Argon Time Projection Chamber using Wire-Cell 3-D Imaging, Clustering, and Charge-Light Matching

MicroBooNE collaboration, P. Abratenko, M. Alrashed +182

An accurate and efficient event reconstruction is required to realize the full scientific capability of liquid argon time projection chambers (LArTPCs). The current and future neut…

astro-ph.HE2020★ 23 cited

Fermi-type particle acceleration from magnetic reconnection at the termination shock of a relativistic striped wind

Yingchao Lu, Fan Guo, Patrick Kilian +3

An oblique-rotating pulsar generates a relativistic striped wind in a pulsar wind nebula (PWN). The termination shock of the PWN compresses the Poynting-flux-dominated flow and dri…

physics.comp-ph2020★ 12 cited

A machine learning approach for efficient multi-dimensional integration

Boram Yoon

We propose a novel multi-dimensional integration algorithm using a machine learning (ML) technique. After training a ML regression model to mimic a target integrand, the regression…