Neutrino Physics and Machine Learning (NPML)

Session

Day 4 Afternoon

Jul 22, 2020, 1:00 PM

Conveners

Day 4 Afternoon

• Kazuhiro Terao (SLAC)
• Patrick de Perio (TRIUMF)

8. Machine learning techniques in ANNIE

Dr Evangelia Drakopoulou (University of Edinburgh)

7/22/20, 1:00 PM

A collaboration/project summary talk

The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) is a 26-ton Gd-doped water Cherenkov detector installed in the Booster Neutrino Beam (BNB) at Fermilab. The experiment aims to make a unique measurement of neutron yield from neutrino-nucleus interactions and to perform R&D for the next generation of water-based neutrino detectors. To realise these goals the ANNIE collaboration...

37. GPU as a Service for Accelerating Machine Learning Applications in the Reconstruction Workflows of Neutrino Experiments

Tingjun Yang (Fermilab)

7/22/20, 1:40 PM

Individual talk

The employment of machine learning (ML) techniques has now become commonplace in the offline reconstruction workflows of modern neutrino experiments. Since such workflows are typically run on CPU-based high-througput computing (HTC) clusters with limited or no access to ML accelerators like GPU or FPGA coprocessors, the ML algorithms, for which CPUs are not the best suited platform, tend to...

49. Scalable, Distributed Machine Learning¶

Corey Adams (Argonne National Laboratory)

7/22/20, 2:05 PM

A collaboration/project summary talk

Machine learning in neutrino physics leverages many tools and techniques from the more mainstream areas of computer vision, but also brings new and interesting challenges. Notably, neutrino experiments have large images, typically with very high resolution, and often sparse or irregular data. In this talk I'll present several techniques that are successfully shown to accelerate machine...

42. Scalable, Distributed Machine Learning

Corey Adams (Argonne National Laboratory)

A collaboration/project summary talk

Machine learning in neutrino physics leverages many tools and techniques from the more mainstream areas of computer vision, but also brings new and interesting challenges. Notably, neutrino experiments have large images, typically with very high resolution, and often sparse or irregular data. In this talk I'll present several techniques that are successfully shown to accelerate machine...