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arXiv:2609.30382 · Physical Review Applied

Selective state-space model for photon-energy estimation in microwave kinetic inductance detectors

Anonymous

Published Sep 23, 2026• 0 citations
arXivHTMLPDF

Abstract

Microwave Kinetic Inductance Detectors (MKIDs) are superconducting photon counting detectors that simultaneously measure the energy and arrival time of individual photons and are suitable for large-format arrays. The energy resolving power R = E/$Δ$E is the primary figure of merit for spectrophotometric applications. It is limited in practice by the assumptions underlying the Wiener optimal filter used to estimate photon energy: stationary noise, linear response, and energy independent pulse shapes, all of which MKIDs structurally violate. We present Venom (Very Efficient Neural Optimal-filter for MKIDs), a selective state space model based on the Mamba architecture that replaces the coordinate transform and optimal filter, operating directly on raw in-phase/quadrature timestream data, carrying 3,314 trainable parameters, and targeting deployment on the MKIDGen3 and future system-on-chip readout platforms. To deal with the small calibration datasets typical of MKID experiments, we train on synthetic pulses drawn from a streaming principal component analysis that samples photon energy continuously between calibration wavelengths. All numbers are reported on a stratified 15% held-out validation set, with R measured from a kernel density estimate. On an InHf bilayer resonator (ten wavelengths, 254-1310nm), Venom reaches a mean R of 26.4 versus 24.2 for the published per-wavelength optimal filter baseline, a 9% improvement from one model in place of ten per-laser filter templates. The R of 38.6 at 254 nm is the highest ever recorded for an ultraviolet-to-near-infrared MKID suitable for use in a dense array. On a PtSi resonator (five wavelengths, 808-1310nm), where the published analysis used one shared 920nm template, Venom matches the optimal filter, with a mean R of 9.1 versus 8.8 at the three interior wavelengths.

DOI
10.1103/hz16-xh69

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Microwave Kinetic Inductance Detectors (MKIDs) are superconducting photon counting detectors that simultaneously measure the energy and arrival time of individual photons and are suitable for large-format arrays. The energy resolving power R = E/ΔΔΔE is the primary figure of merit for spectrophotometric applications. It is limited in practice by the assumptions underlying the Wiener optimal filter used to estimate photon energy: stationary noise, linear response, and energy independent pulse shapes, all of which MKIDs structurally violate. We present Venom (Very Efficient Neural Optimal-filter for MKIDs), a selective state space model based on the Mamba architecture that replaces the coordinate transform and optimal filter, operating directly on raw in-phase/quadrature timestream data, carrying 3,314 trainable parameters, and targeting deployment on the MKIDGen3 and future system-on-chip readout platforms. To deal with the small calibration datasets typical of MKID experiments, we train on synthetic pulses drawn from a streaming principal component analysis that samples photon energy continuously between calibration wavelengths. All numbers are reported on a stratified 15% held-out validation set, with R measured from a kernel density estimate. On an InHf bilayer resonator (ten wavelengths, 254-1310nm), Venom reaches a mean R of 26.4 versus 24.2 for the published per-wavelength optimal filter baseline, a 9% improvement from one model in place of ten per-laser filter templates. The R of 38.6 at 254 nm is the highest ever recorded for an ultraviolet-to-near-infrared MKID suitable for use in a dense array. On a PtSi resonator (five wavelengths, 808-1310nm), where the published analysis used one shared 920nm template, Venom matches the optimal filter, with a mean R of 9.1 versus 8.8 at the three interior wavelengths.
Microwave kinetic inductance detectors (MKIDs) sensitive to light in the ultraviolet to near-infrared wavelengths are superconducting microresonators that are capable of measuring photon arrival times to microsecond precision and estimating each photon's energy. The resolving power of nonmembrane MKIDs has remained stubbornly around 10 at 1 μm despite significant improvements in the system noise. Here we show that the resolving power can be roughly doubled with a simple bilayer design without needing to place the device on a membrane, avoiding a significant increase in fabrication complexity. Based on modeling of the phonon propagation, we find that the majority of the improvement comes from the inability of high energy phonons to enter the additional layer due to the lack of available phonon states.
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.
Noise is one of the main factors affecting the performance index of weak signal detection devices, and the optimal filtering algorithm is an effective method to adaptively extract various useful weak signals from the white noise background. In order to improve the performance of single photon detector (especially the photon number resolution ability), one mainly focuses on the optimization of detector hardware such as the optimization of photosensitive materials and the technology of device fabrication. However, in this paper the performance of microwave kinetic Inductance detector (MKID) in the way of data processing is improved. Considering the fact that the template of light pulse signal in the optimal filtering algorithm is obtained by taking the average, we replace the noise model in the original optimal filtering algorithm with the white noise model and the whitening noise model. Then we process the photon response data that are detected by the MKID in an extremely low temperature environment. The results show that the energy resolution (one of the main performance indexes of single photon detector) of MKID is improved by about 15%, and we achieve an infrared single photon energy resolution of 0.26 eV. In this paper, the application and development trends of superconducting single photon detector are briefed. Then, how the MKID responds to weak coherent optical signal in low temperature environment, and the process of signal conversion, acquisition and output are explained in detail. According to the optimal filtering algorithm, we use different noise models to analyze the results of the signals detected by MKID. After that, we count the optimal amplitude multiple, perform the Gaussian fitting analysis on the statistical graph, and compare the energy resolution with the photon number resolution of the optimal filtering algorithm under different noise models. As a result, we find that under the white noise model, the optimal filtering algorithm is used to obtain the best result for MKID processing, and high energy resolution can be achieved.
In this paper, we investigate the single photon response from the reflection of the microwave kinetic inductance detector (MKID) array. Reflection measurements are carried out using two configurations: one is measured simultaneously with the transmission, and the other is obtained with a single-ended MKID array terminated with an open load. Compared with the transmission, reflection measurements significantly reduce the readout noise of the single-ended MKID array. This is also reflected in the improvement of the median energy resolving power by around 20%–30% under pulsed photon illumination at λ = 405 nm, mainly due to an increase in the size of the resonance circle on the IQ plane. This method has the potential to be used to read out large MKID arrays.
Astrophysical photons with a sub-millimetre wavelength are of great value to research star and galaxy formation. Superconductors have a small energy gap with which these sub-millimetre photons can be measured. Superconductors can absorb photons so that quasiparticles are created, which will in turn change the complex conductivity: incoming photons change the electrical response of the superconductor. This principle can be used to realise a detector. This is done with the Microwave Kinetic Inductance Detectors (MKIDs) that consist of a superconducting film embedded in a resonant circuit, so that photons falling in will change the resonant frequency. This can be measured with a readout signal. To enhance the sensitivity of the MKID, we want to increase the responsivity of an MKID as much as possible.A previous study has proposed a figure of merit regarding the material properties of a superconductor to achieve a high responsivity. However, the readout frequency used is not included in this figure. The goal of this work is to extend the figure of merit so that the readout frequency of the MKID can be optimised for a higher responsivity.
We measure the photon arrival timing jitter of three superconducting Microwave Kinetic Inductance Detectors (MKIDs) within a large array of 20,440 pixels, spanning resonating frequencies from 4 to 8 GHz. We analyze arrival time distributions for photon number n = 1 and n = 2 events and find the distribution full-width at half-maximum ranging from 132 to 796 ns across the three detectors. In combination with nearly zero dark counts and multiplexed readout schemes, sub-microsecond jitter can be leveraged for high-time resolved astronomy, quantum, and bio-physics applications. The measured system jitter serves as an upper limit for MKIDs, with optimized performance likely reaching tens of nanoseconds. We discuss how to improve upon the detector and readout performance.