[Seminars] Computations In Science Seminar in KPTC at 12PM

[Ramona Echols]

COMPUTATIONS IN SCIENCE SEMINAR

Wednesday, October 30, 2019 in KPTC 206

Discussion over bag-lunch at 12:00.

Talk begins at 12:15

*** Ben Nachman ***
** Lawrence Berkeley National Laboratory **
****
Exploring hypervariate phase space with likelihood-free and label-free deep learning


Precise scientific analysis in collider-based particle physics is possible because of complex sim-
ulations that connect fundamental theories to observable quantities. These simulations have been
paired with multivariate methods for many years in search of the smallest distance scales in nature. 
Deep learning tools hold great promise to qualitatively change this paradigm by allowing for
holistic analysis of data in its natural hyperdimensionality with thousands or millions of features
instead of up to tens of features. These tools are not yet broadly used for all areas of data analysis
because of the traditional dependence on simulations. In this talk, I will discuss how we can change
this paradigm in order to exploit the new features of deep learning to explore nature at sub-nuclear
distance scales. In particular, I will show how neural networks can be used to (1) overcome the
challenge of intractable hypervariate probability density modeling and (2) learn directly from
(unlabeled) data to perform hypothesis tests that go beyond any existing analysis methods. The
talk will end with a brief discussion of challenges for hypervariate deep learning analysis. While
my examples will be from particle physics, it is likely that these tools have a much broader appli-
cability across fundamental physics and beyond. I will keep the particle physics jargon minimal
in order to facilitate discussions about connections to your area of science!

Warmly, 
 
Ramona Echols,  EFI/HEP Secretary IV
Enrico Fermi Institute 
University of Chicago
933 E 56th Street, MCP 101
Chicago, IL 60637
Phone: 773-702-8113
Rechols06 at uchicago.edu