This repository contains the ongoing development of a machine learning–based framework for the analysis of microwave transmission data obtained from superconducting resonances. The overall goal is to explore the applicability of modern machine learning techniques to improve data analysis pipelines in superconducting detector research. The project is currently in beta stage.
Currelntly, the project includes tools for generating synthetic data including artificial white noise, neural network training and evaluation, and auxiliary utilities supporting real experimental data load and analysis.
This project is being developed by Sergio Pajuelo, student in Computer Engineering at the University of Alcalá, as part of his academic internship at the Centro de Astrobiología (CAB, CSIC–INTA), under the supervision of Víctor Rollano, within the Superconducting Devices research group.