Conference on Science at the Sanford Underground Research Facility

Session

Advanced Data Analysis

May 15, 2024, 4:20 PM

CB 204 W, SD Mines

Conveners

Advanced Data Analysis: Parallel

• Matthias Plum (South Dakota School of Mines and Technology)

Description

Zoom Login Information:
https://sdsmt.zoom.us/j/91834094771

81. Machine learning-based waveform analysis for pile-up events

Laxman Sharma Paudel (University of South Dakota)

5/15/24, 4:20 PM

Advanced Data Analysis

Oral

Machine learning (ML) techniques are increasingly being used in the analysis of data in particle physics as well as in neutrinoless double-decay experiments. ML approach is often suitable to discriminate between signal and background events in cases where signal and background spectrum are well-known and when the spectra can be fed into ML algorithms for training. Also, various ML-based pulse...

63. Predictive Impurity Profiling in Germanium Crystals through Machine Learning and Hall Effect Measurements

Pramod Acharya (The University of South Dakota)

5/15/24, 4:45 PM

Advanced Data Analysis

Oral

This abstract introduces Hall effect measurements for evaluating Germanium crystal properties and proposes using machine learning to improve accuracy amidst challenges like equipment failures and sample fluctuations. It focuses on high-purity Germanium for rare event detection. Traditional methods for assessing impurity levels are limited, prompting the exploration of machine learning. The...

38. Application of machine learning for anomaly detection and background discrimination in LZ data

Maris Arthurs (Postdoctoral associate)

5/15/24, 5:10 PM

Advanced Data Analysis

Oral

LUX-ZEPLIN (LZ) is a dark matter direct detection experiment using a dual-phase xenon time projection chamber with a 7-ton active volume, which recently set a world leading limit for spin-independent scattering at 36 GeV/c2, rejecting cross sections above 9.2×10−48 cm2 at the 90% confidence level. Machine learning techniques have been explored at various stages of data analysis, for...

108. Laying the groundwork for trust in dark matter results

Amy Roberts (University of Colorado Denver)

5/15/24, 5:35 PM

Advanced Data Analysis

Oral

As new dark matter detectors turn on, it’s useful to think about what dark matter discovery will look like.

This talk will discuss two aspects of building trust in dark matter results: (1) data blinding through adding “salt,” or fake signal, and (2) the possibilities in combining underground detector information to further constrain backgrounds.

Several dark matter experiments (this talk...