EPE ML meeting - Jessica Nicole Howard

Thursday 25 Feb 2021, 17:00 → 18:00 US/Pacific

Jessica Howard is a Ph.D. candidate in particle physics at UC Irvine and a National Science Foundation Graduate Research Fellow. She received her B.S. in physics with a minor in mathematics from UC Davis in 2017. A primary aspect of her research aims to use machine learning to help solve outstanding particle physics problems. To date, this has manifested in applying machine learning tools to a variety of collider experiment problems such as improving signal-background classification and creating faster particle simulations. In the future, she is also interested in applying machine learning tools to help solve problems in theoretical and phenomenological particle physics.

 

Zoom: https://washington.zoom.us/j/2634450494

17:00 → 17:30 Foundations of a Fast, Data-Driven, Machine-Learned Simulator

We introduce a novel strategy for machine-learning-based fast simulators, which is the first that can be trained in an unsupervised manner using observed data samples to learn a predictive model of detector response and other difficult-to-model transformations. Across the physical sciences, a barrier to interpreting observed data is the lack of knowledge of a detector’s imperfect resolution, which transforms and obscures the unobserved latent data. Modeling this detector response is an essential step for statistical inference, but closed-form models are often unavailable or intractable, requiring the use of computationally expensive, ad-hoc numerical simulations. Using particle physics detectors as an illustrative example, we describe a novel strategy for a fast, predictive simulator called Optimal Transport based Unfolding and Simulation (OTUS), which uses a probabilistic autoencoder to learn this transformation directly from observed data, rather than from existing simulations. Unusually, the probabilistic autoencoder’s latent space is physically meaningful, such that the decoder becomes a fast, predictive simulator for a new latent sample, and has the potential to replace Monte Carlo simulators. We provide proof-of-principle results for Z-boson and top-quark decays, but stress that our approach can be widely applied to other physical science fields.

Speaker: Jessica Nicole Howard (University of California Irvine (US))

arXiv:2101.08944

Howard_Jessica_UWSeminar.pdf