Machine Learning-Based Denoising for Single-Photon Detection With MKIDs

Viktor Makhrinov, Michael Arena, Aled Cuda, Mehdi Bagheri, Akhan Almagambetov, Aziret-Sultan Kiyazov, R. D. Martin, B. Grossan, George F. Smoot, Mehdi Shafiee

2026

Full text Open access: Green Metadata (CSL-JSON)
DOI 10.1109/tasc.2026.3718447
Data confidence 70 medium
How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

Microwave Kinetic Inductance Detectors (MKIDs) are superconducting sensors primarily used in astrophysics and quantum measurements for their energy- and time-resolution of single-photon detection. However, their sensitivity and precision are limited by various noise sources. Conventional denoising approaches enhance the signal-to-noise ratio (SNR) but often blur signal features and are ineffective with non-stationary noise. This work explores the use of machine learning (ML) denoising techniques for MKID signals in steady state resonators and in photon-induced transients. Different denoising techniques are evaluated on a dataset of frequency-domain S21 measurements and time-domain phase pulses with illumination of 660 nm, 780 nm, and 1310 nm wavelengths. Traditional filtering approaches (moving average, median, Wiener, Savitzky-Golay, wavelet, Kalman, total variation denoising) were evaluated in comparison with ML models including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and gated recurrent units (GRUs). Due to absence of clean reference signals in experimental settings, a Noise2Noise training approach was used for self-supervised learning from noisy data. The models were assessed using mean squared error (MSE), signal-to-noise ratio (SNR), variation in peak depth and optical resolution metrics. Findings of this thesis show that machine learning models achieve substantially better denoising performance compared to conventional methods across all metrics. GRU models outperformed other models when denoising steady-state resonator signals, as they captured local features of the spectral response, and they also were the most effective at denoising transient photon signals due to their capacity to capture temporal dynamics. Conventional filters, while being computationally efficient, were found to distort the signal and were not as effective in retaining low-amplitude photon-induced features. In summary, this study demonstrates that machine learning denoising is a robust and versatile method for improving MKID signals without prior knowledge of noise properties. The results also demonstrate the opportunity to enhance superconducting detector readouts by combining data-driven approaches, leading to improved sensitivity and resolution for applications such as astrophysical imaging and quantum sensing.