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20162022
most citedNeural Embedding: Learning the Embedding of the Manifold of Physics Data

17 citations · 30 across the 4 of their papers we have counts for

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Showing 2019Show all

5 papers · 1 filter

hep-ph2019

On the ATLAS Top Mass Measurements and the Potential for Stealth Stop Contamination

Timothy Cohen, Stephanie Majewski, Bryan Ostdiek +1

The discovery of the stop - the Supersymmetric partner of the top quark - is a key goal of the physics program enabled by the Large Hadron Collider. Although much of the accessible…

hep-ph2019

Mass Agnostic Jet Taggers

Layne Bradshaw, Rashmish K. Mishra, Andrea Mitridate +1

Searching for new physics in large data sets needs a balance between two competing effects---signal identification vs background distortion. In this work, we perform a systematic s…

astro-ph.GA2019

Chasing Accreted Structures within Gaia DR2 using Deep Learning

Lina Necib, Bryan Ostdiek, Mariangela Lisanti +3

In previous work, we developed a deep neural network classifier that only relies on phase-space information to obtain a catalog of accreted stars based on the second data release o…

astro-ph.GA2019

Evidence for a Vast Prograde Stellar Stream in the Solar Vicinity

Lina Necib, Bryan Ostdiek, Mariangela Lisanti +6

Massive dwarf galaxies that merge with the Milky Way on prograde orbits can be dragged into the disk plane before being completely disrupted. Such mergers can contribute to an accr…

astro-ph.GA2019

Cataloging Accreted Stars within Gaia DR2 using Deep Learning

Bryan Ostdiek, Lina Necib, Timothy Cohen +6

The goal of this study is to present the development of a machine learning based approach that utilizes phase space alone to separate the Gaia DR2 stars into two categories: those…