Speakers
Description
The LUX-ZEPLIN (LZ) dark matter direct detection experiment searches directly for dark matter particle interactions in a 10-tonne liquid xenon target. LZ collects about 50 MBytes per second of detector data across its 494 photomultiplier tubes (PMTs), or around 1.5 PBytes (1.5 million GB) per year. The utility of this data naturally becomes constrained by its scale–to efficiently analyze this quantity of information, we need to achieve compressed, rich representations of the data. Artificial neural networks (ANNs) have long been applied as an effective way to classify, model, and reason over large quantities of data due to their ability to learn over vast hyperspaces. Autoencoders (AEs), deep neural networks composed of encoder and decoder components, are one method used in particular for the task of compressing data. In this module you will learn about the formulation, benefits, and limitations of AEs and leverage them for applications to LZ data compression, which also can be useful in other areas of research.
