Karlsruhe 2024 – scientific programme

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T: Fachverband Teilchenphysik

T 121: Search for Dark Matter 6

T 121.5: Talk

Friday, March 8, 2024, 10:00–10:15, Geb. 30.35: HSI

Antenna alignment of the MADMAX booster system using Machine Learning techniques — •Nabil Salama for the MADMAX collaboration — Institut für Experimentalphysik, Universität Hamburg, Luruper Chaussee149, 22761 Hamburg

The axion is a promising hypothetical dark matter candidate that would also solve the strong CP problem. The MADMAX experiment aims at detecting the axion in a large mass range corresponding to a frequency between 10 and 100 GHz using an array of dielectric disks in a high magnetic field of 9 T which are individually moveable to tune the resonance frequency. I present a method to spatially align the antenna that picks up a potential axion signal as well as the disks in order to compensate for a possible antenna misalignment. This is necessary to maximize the signal power of the system. The possibility of electric field measurements using the so-called bead-pull method for an objective function is investigated. The alignment procedure of antenna and disks involves many degrees of freedom which makes the problem complex, therefore it is approached using Machine Learning techniques. Results yielded by the algorithm are compared to a non-learning algorithm and theoretical expectations from Gaussian beam optics.

Keywords: Axion; RF Physics; Machine Learning; Gaussian Beam Optics