Comp F3 White Paper planning

Wednesday 19 Jan 2022, 08:00 → 10:00 US/Pacific

Zoom: https://mit.zoom.us/j/92827341251 

08:00 → 08:05 Introduction/WP schedule

08:05 → 08:10 WP update

Speaker: Brian Nord (Fermilab)

08:10 → 08:15 GNN/Instance Segmentation WPs

Speaker: Savannah Thais (Princeton University)

08:15 → 08:20 WP on Detector Simulation

Speaker: Mikuni Vinicius (LBNL)

WP ML for sim.pdf

08:20 → 08:25 Symmetry Group Equivariant Architectures

Speaker: Mariel Pettee (Yale University)

08:30 → 08:35 Data Science/ML education in HEP

Speaker: Mark Neubauer (University of Illinois at Urbana-Champaign)

Plans for CompF3 WP_ Data Science and Machine Learning in Education.pdf

08:40 → 08:45 Self-Driving System for Physics

Speaker: Cecilia Tosciri

22_01_19_SnowmassWPstatus.pdf

08:45 → 08:50 Applications of Machine Learning to Lattice Quantum Field Theory

Speakers: Daniel Hackett (University of Colorado, Boulder), Daniel Hackett

snowmass-ml-for-lqft-wp-update.pdf

08:50 → 08:55 Model Independent Searches for New Physics using Machine Learning

Our main idea is to assess the performance of different semi-supervised or unsupervised methods using different benchmark data sets. Besides commonly explored multi-jet signatures, we also want to consider multi-lepton processes. In order to compare the performance of different methods, we also consider several types of data input, e.g., kinematic variables and image data sets. This will provide a comparison of different methods on the same footing.

Speaker: Charanjit Kaur

08:55 → 09:00 Community Needs, Tools and Resources

Speaker: Dylan Rankin (Massachusetts Institute of Technology)

snowmass_compf3_wp_19jan21.pdf