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

2024-06-05

arXiv.org

AI Summary

Detecting weak sinusoidal dark matter signals in ultra-long time-series data (10 MHz sampling rate) from the ABRA-CADABRA experiment remains a significant challenge. Method: We introduce TIDMAD—the first benchmark dataset for dark matter searches—comprising training, validation, and scientific analysis subsets. We propose a physically interpretable, differentiable denoising scoring mechanism and develop an end-to-end analysis framework integrating deep temporal models (WaveNet/TCN), frequency-domain feature enhancement, and Bayesian statistical inference to directly output particle-physics-compliant upper limits on coupling strength. Contribution/Results: Our framework substantially improves sensitivity to faint sinusoidal signals, enables reproducible analyses and optimized exclusion limits, and is fully open-sourced—including data and code—already adopted by multiple AI-for-science teams.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Interdisciplinary science discovery with web data mining
• Security and Privacy: Data transparency and provenance

Abstract

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 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

Problem

• Detecting dark matter signals in ultra-long time series data
• Denoising experimental data to extract sinusoidal oscillation patterns
• Providing benchmark datasets for AI algorithms in physics research

Innovation

• AI denoising for ultra-long time series data
• Benchmarking score for direct model evaluation
• Complete analysis framework for physics publication