no code implementations • 1 Apr 2020 • Timothy Cohen, Joel Doss, Marat Freytsis
We examine the robustness of collider phenomenology predictions for a dark sector scenario with QCD-like properties.
High Energy Physics - Phenomenology
no code implementations • 15 Jul 2019 • Bryan Ostdiek, Lina Necib, Timothy Cohen, Marat Freytsis, Mariangela Lisanti, Shea Garrison-Kimmel, Andrew Wetzel, Robyn E. Sanderson, Philip F. Hopkins
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 accreted onto the Milky Way from those that are in situ.
no code implementations • 28 Sep 2017 • Spencer Chang, Timothy Cohen, Bryan Ostdiek
Applications of machine learning tools to problems of physical interest are often criticized for producing sensitivity at the expense of transparency.
1 code implementation • 28 Jun 2017 • Timothy Cohen, Marat Freytsis, Bryan Ostdiek
In this paper, we compare the standard "fully supervised" approach (that relies on knowledge of event-by-event truth-level labels) with a recent proposal that instead utilizes class ratios as the only discriminating information provided during training.