TIDMAD: Time Series Dataset for Discovering Dark Matter with AI Denoising
This provides a dataset and framework for physicists and AI researchers to advance fundamental science in dark matter detection, though it is incremental as it builds on existing experimental data without new algorithmic breakthroughs.
The paper tackles the problem of detecting dark matter by releasing the TIDMAD dataset from the ABRACADABRA experiment, which includes ultra-long time-series data and tools to enable AI algorithms to extract potential dark matter signals, resulting in a community-standard analysis framework for physics research.
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 -- a comprehensive data release from the ABRACADABRA experiment including three key components: an ultra-long time series dataset divided into training, validation, and science subsets; a carefully-designed denoising score for direct model benchmarking; and a complete analysis framework which produces a community-standard dark matter search result suitable for publication as a physics paper. This data release enables core AI algorithms to extract the dark matter signal and produce real physics results thereby advancing fundamental science. The data downloading and associated analysis scripts are available at https://github.com/jessicafry/TIDMAD