Simulation of dielectric axion haloscopes with deep neural networks: a proof-of-principle

Autor:	Jung, Philipp Alexander, Santos, Bernardo Ary dos, Bergermann, Dominik, Graulich, Tim, Lohmann, Maximilian, Novák, Andrzej, Öz, Erdem, Riahinia, Ali, Schmidt, Alexander
Rok vydání:	2022
Předmět:	Physics - Instrumentation and Detectors Astrophysics - Cosmology and Nongalactic Astrophysics
Zdroj:	Comput Softw Big Sci 6, 18 (2022)
Druh dokumentu:	Working Paper
DOI:	10.1007/s41781-022-00091-5
Popis:	Dielectric axion haloscopes, such as the MADMAX experiment, are promising concepts for the direct search for dark matter axions. A reliable simulation is a fundamental requirement for the successful realisation of the experiments. Due to the complexity of the simulations, the demands on computing resources can quickly become prohibitive. In this paper, we show for the first time that modern deep learning techniques can be applied to aid the simulation and optimisation of dielectric haloscopes. Comment: 7 pages, 6 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2206.00370 Zobrazit plný text záznamu View this record from Arxiv