8 papers
Machine Learning the 6th Dimension: Stellar Radial Velocities from 5D Phase-Space Correlations
Adriana Dropulic, Bryan Ostdiek, Laura J. Chang +3
The Gaia satellite will observe the positions and velocities of over a billion Milky Way stars. In the early data releases, the majority of observed stars do not have complete 6D p…
Parameter Inference from Event Ensembles and the Top-Quark Mass
Forrest Flesher, Katherine Fraser, Charles Hutchison +2
One of the key tasks of any particle collider is measurement. In practice, this is often done by fitting data to a simulation, which depends on many parameters. Sometimes, when the…
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…
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…
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…
Dark Mesons at the LHC
Graham D. Kribs, Adam Martin, Bryan Ostdiek +1
A new, strongly-coupled dark sector could be accessible to LHC searches now. These dark sectors consist of composites formed from constituents that are charged under the electrowea…