Identifying environmentally induced calibration changes in cryogenic RF axion detector systems using deep neural...

Area of Science:

• * Particle Physics
• * Astrophysics
• * Quantum Chromodynamics

Background:

• * The axion is a hypothetical particle proposed to solve the strong CP problem and explain dark matter.
• * Current axion detection relies on haloscopes using high Q cavities in magnetic fields, targeting 1-10 GHz.
• * Increasing axion mass requires smaller cavities, reducing signal-to-noise and necessitating multi-cavity arrays.

Purpose of the Study:

• * To investigate the use of off-resonant data from vector network analyzer scans for diagnosing axion haloscope experiments.
• * To explore the application of artificial intelligence (AI) techniques for managing the increased complexity of multi-cavity axion haloscope searches.
• * To determine if off-resonant data can identify equipment failures, anomalies, and measure physical conditions like temperature and magnetic field.

Main Methods:

• * Analyzing off-resonant scattering parameter data from vector network analyzer scans.
• * Employing neural networks to process and interpret the off-resonant data.
• * Demonstrating a proof-of-concept for AI-driven diagnostics in axion haloscope experiments.

Main Results:

• * Off-resonant data, typically unused, contain valuable information for diagnosing experimental conditions.
• * AI techniques can effectively analyze this data to identify equipment anomalies and measure physical parameters.
• * This approach offers a viable method to manage the operational complexity of multi-detector axion haloscope searches.

Conclusions:

• * AI-powered analysis of off-resonant data presents a novel solution for diagnosing axion haloscope experiments.
• * This method can help mitigate the challenges associated with scaling up axion detection to multi-cavity systems.
• * The findings pave the way for more robust and efficient axion dark matter searches.