TIDMAD: Time Series Dataset for Discovering Dark Matter with AI Denoising

Jessica Fry (MIT) · Xinyi Fu (Stanford University) · Zhenghao Fu (Massachusetts Institute of Technology) · Kaliroë Pappas (Columbia University) · Lindley Winslow (Massachusetts Institute of Technology) · Aobo Li (Xidian University)

abracadabraai algorithmsanalysis frameworkdark matterdata releasedenoising scoredetection methodsfundamental sciencemodel benchmarkingphysics community-standardsignal extractionsinusoidal oscillationtidmadtime-series analysisultra-long time series

Dark matter makes up approximately 85\% of total matter in our universe, yet it has never been directly observed in any laboratory on Earth. The origin of dark matter is one of the most important questions in contemporary physics, and a convincing detection of dark matter would be a Nobel-Prize-level breakthrough in fundamental science. The ABRACADABRA experiment was specifically designed to search for dark matter. Although it has not yet made a discovery, ABRACADABRA has produced several dark matter search results widely endorsed by the physics community. The experiment generates ultra-long time-series data at a rate of 10 million samples per second, where the dark matter signal would manifest itself as a sinusoidal oscillation mode within the ultra-long time series. In this paper, we present the TIDMAD