Maksut, Zhansaya2024-09-022024-09-022024Maksut, Zh. (2024). Microwave Kinetic Inductance Detector Signal Denoising Using Machine Learning. Nazarbayev University School of Engineering and Digital Scienceshttps://nur.nu.edu.kz/handle/123456789/8249Superconducting micro-resonators are crucial in creating superposition states in quan tum computers and are gaining prominence as quantum technology evolves. Beyond their quantum applications, these resonators are instrumental as ultra-sensitive de tectors in astronomy, particularly Microwave Kinetic Inductance Detectors (MKIDs), which are key for both scientific exploration and industrial use. However, the is sue of electronic noise in these detectors remains a significant hurdle. To address this, our approach integrates machine learning strategies alongside conventional noise reduction methods to improve MKID signal fidelity. Our process involves gathering data and applying denoising models, with a strong emphasis on machine learning techniques. Our findings highlight the strengths of dif ferent denoising techniques, particularly deep learning architectures like Long Short Term Memory (LSTM) networks and Autoencoders, which demonstrate promising results in denoising MKID signals. These models exhibit adaptability to diverse noise sources, proficiency in identifying complex noise patterns, and continuous improve ment with additional data. This research represents a significant advancement in the field and provides es sential insights into improving MKID detectors, which in turn could lead to ground breaking developments in various scientific areas.enAttribution-NonCommercial-ShareAlike 3.0 United StatesType of access: RestrictedSuperconducting micro-resonatorsMicrowave Kinetic In ductance Detectors (MKIDs)denoisingelectronic noisesignal reliabilityMaster's Capstone project