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J. Searcy | Researchclopedia

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J. Searcy

University of Oregon ORCID

966

Works

81,119

Citations

133

h-index

786

i10-index

Research Topics

Particle physics theoretical and experimental studies High-Energy Particle Collisions Research Particle Detector Development and Performance

Affiliations

Northern Illinois University2016–2019

Universidade Federal do Rio de Janeiro2013–2013

Uppsala University2016–2016

Rutherford Appleton Laboratory2011–2013

Fermi National Accelerator Laboratory2015–2015

Top Works

Measurement of inclusive jet charged-particle fragmentation functions in Pb+Pb collisions at <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.gif" overflow="scroll"><mml:msqrt><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant="normal">NN</mml:mi></mml:mrow></mml:msub></mml:msqrt><mml:mo>=</mml:mo><mml:mn>2.76</mml:mn><mml:mtext> </mml:mtext><mml:mtext>TeV</mml:mtext></mml:math> with the ATLAS detector

G. Aad,B. Abbott,et al.

2014155 citations

Quantum Chromodynamics and Particle Interactions

Dark Matter and Cosmic Phenomena

Technical Design Report for the Phase-I Upgrade of the ATLAS TDAQ System

G. Aad,A. Kupčo,et al.

2013125 citations

Early Warning for Great Earthquakes From Characterization of Crustal Deformation Patterns With Deep Learning

Jiun‐Ting Lin,Diego Melgar,et al.

202157 citations

Determination of the<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>W</mml:mi><mml:mi>W</mml:mi></mml:math>polarization fractions in<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>p</mml:mi><mml:mi>p</mml:mi><mml:mo stretchy="false">→</mml:mo><mml:msup><mml:mi>W</mml:mi><mml:mo>±</mml:mo></mml:msup><mml:msup><mml:mi>W</mml:mi><mml:mo>±</mml:mo></mml:msup><mml:mi>j</mml:mi><mml:mi>j</mml:mi></mml:math>using a deep machine learning technique

J. Searcy,Lillian Huang,et al.

201647 citations

Simultaneous measurements of the<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>t</mml:mi><mml:mover accent="true"><mml:mi>t</mml:mi><mml:mo accent="true" stretchy="false">¯</mml:mo></mml:mover></mml:math>,<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msup><mml:mi>W</mml:mi><mml:mo>+</mml:mo></mml:msup><mml:msup><mml:mi>W</mml:mi><mml:mo>−</mml:mo></mml:msup></mml:math>, and<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>Z</mml:mi><mml:mo>/</mml:mo><mml:msup><mml:mrow><mml:mi>γ</mml:mi></mml:mrow><mml:mrow><mml:mo>*</mml:mo></mml:mrow></mml:msup><mml:mo stretchy="false">→</mml:mo><mml:mi>τ</mml:mi><mml:mi>τ</mml:mi></mml:mrow></mml:math>production cross-sections in<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>p</mml:mi><mml:mi>p</mml:mi></mml:math>collisions at<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msqrt><mml:mi>s</mml:mi></mml:msqrt><mml:mo>=</mml:mo><mml:mn>7</mml:mn><mml:mtext> </mml:mtext><mml:mtext> </mml:mtext><mml:mi>TeV</mml:mi></mml:math>with the ATLAS detector

G. Aad,B. Abbott,et al.

201529 citations

Effectiveness of a COVID-19 Testing Outreach Intervention for Latinx Communities

David S. DeGarmo,Stephanie De Anda,et al.

202229 citations

Learning source, path and site effects: CNN-based on-site intensity prediction for earthquake early warning

Hongcai Zhang,Diego Melgar,et al.

202229 citations

How Useful Are Tax Disclosures in Predicting Effective Tax Rates? A Machine Learning Approach

David A. Guenther,Kyle Peterson,et al.

202326 citations

Identification of Low‐Frequency Earthquakes on the San Andreas Fault With Deep Learning

Amanda M. Thomas,Asaf Inbal,et al.

202124 citations