Summary of Tidmad: Time Series Dataset For Discovering Dark Matter with Ai Denoising, by J. T. Fry et al.
TIDMAD: Time Series Dataset for Discovering Dark Matter with AI Denoising
by J. T. Fry, Aobo Li, Lindley Winslow, Xinyi Hope Fu, Zhenghao Fu, Kaliroe M. W. Pappas
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: High Energy Physics – Experiment (hep-ex)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The ABRACADABRA experiment, designed to search for dark matter, has generated ultra-long time-series data at a rate of 10 million samples per second. The dark matter signal would manifest as a sinusoidal oscillation mode within the dataset. This paper presents TIDMAD, a comprehensive data release including an ultra-long time series dataset, a denoising score, and an analysis framework for benchmarking models and producing community-standard results. By releasing this data, core AI algorithms can extract the signal, produce real physics results, and advance fundamental science. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The ABRACADABRA experiment is trying to find dark matter, which makes up most of our universe. They’ve been collecting lots of data, really fast! If they find something, it would be super important for scientists. This paper is sharing the data with others so that computers can help figure out what’s going on and maybe even discover new things. |
Keywords
» Artificial intelligence » Time series
