Machine Learning for Jet Physics

Session

Experimental/Practical aspects of learning with jets

Dec 12, 2017, 3:00 PM

2-100 (Lawrence Berkeley National Laboratory)

30. Introduction and Overview (15'+5')

Taylor Childers (ANL)

12/12/17, 3:00 PM

11. Deep Neural Networks for whole, multi-jet event classification and generation (15'+5')

Wahid Bhimji

12/12/17, 3:20 PM

Several studies have had success applying deep convolutional neural nets (CNNs) to a subset of the calorimeter for individual jet classification / tagging. We explore approaches that use the entire calorimeter, combined with track information, for directly conducting multi-jet physics analyses, without the need for any jet reconstruction. We use an existing RPV-Susy analysis as a case study...

4. Jet Response Prediction Using Jet Images (15'+5')

Mr Alexx Perloff (TAMU), Dr Raghav Kunnawalkam Elayavalli (Rutgers University)

12/12/17, 3:40 PM

Understanding and appropriately correcting for the detector response on any observable of interest is an important chore for experimentalists. Such a procedure is ultimately necessary to remove the impact of the finite detector and to facilitate direct comparisons with theoretical predictions. All current experiments take on this major task by generating Monte Carlo samples and running them...

31. The Latest in GANs for Jet/Calo Simulation (15'+5')

Michela Paganini

12/12/17, 4:00 PM

32. Machine Learning and Tracking inside Jets in ATLAS (15'+5')

William Mccormack

12/12/17, 4:20 PM