Neutrino Physics and Machine Learning (NPML)

Session

Day 3 Morning

Jul 21, 2020, 10:00 AM

Conveners

Day 3 Morning

• Taritree Wongjirad (Tufts University)
• Patrick de Perio (TRIUMF)

14. DIDACTS (Data-Intensive Discovery Accelerated by Computational Techniques for Science)

Aaron Higuera (Rice University)

7/21/20, 10:00 AM

A collaboration/project summary talk

DIDACTS (Data-Intensive Discovery Accelerated by Computational Techniques for Science) is a collaboration of physics and machine learning experts with an overall goal of incorporate scientific knowledge into machine learning and data science methods in the context of scientific disciplines. As part of DIDACTS’ research program, we are looking into the challenging problem of Dark Matter direct...

11. Demonstration of background rejection using deep neural networks in the NEXT experiment

Dr Marija Kekic (IGFAE)

7/21/20, 10:40 AM

Individual talk

The NEXT (Neutrino Experiment with a Xenon TPC) experiment searches for neutrinoless double-beta decay in 136Xe using a time projection chamber (TPC) filled with enriched xenon gas at high pressure. NEXT can reconstruct the extended ionization tracks left by electrons in the gas. Using this information we can select events with two electrons with a common vertex (double beta decay) from the...

28. NEXT Ton-Scale Sensitivity using Sparse Convolutional Neural Networks

Katherine Woodruff (University of Texas at Arlington)

7/21/20, 11:05 AM

Individual talk

The NEXT Collaboration is currently designing and performing R&D for a ton-scale detector capable of observing neutrinoless double beta decay. NEXT utilizes a high pressure gaseous xenon TPC with an electroluminescent region to amplify the signal from the drift electrons, and has successfully built and collected data with several smaller scale prototypes. The current expected sensitivity of...

33. Simulation and Calibration of light response in nEXO detector using machine learning

Mr Prakash Gautam (Drexel University)

7/21/20, 11:30 AM

Individual talk

nEXO is a proposed 5 tonne liquid xenon experiment which seeks to detect neutrinoless double beta decay $0\nu\beta\beta$ in Xe-136 using Time Projection Chamber (TPC) technology. The experiment will use the combination of scintillation and ionization signals to reconstruct events with an energy resolution of 1\% $\sigma/E$ at the \gls{onbb} Q-value. The scintillation light will be collected...

47. Uncertainty estimation for Deep Learning in Neutrino Physics

Dr Aashwin Mishra (SLAC)

7/21/20, 11:55 AM

Individual talk

Deep neural networks are becoming increasingly pervasive in science and engineering applications. These networks are often treated as high-fidelity models with accurate predictive powers by end users. However, even predictions from a trained neural network may contain significant
errors and uncertainties due to bias, noise and complexity of the data; the volume of the training data; the...