Related papers: Improving the energy resolution of photon counting…

We present a method using principal component analysis (PCA) to process x-ray pulses with severe shape variation where traditional optimal filter methods fail. We demonstrate that PCA is able to noise-filter and extract energy information…

We apply principal component analysis (PCA) to a set of electrical output signals from a commercially available superconducting nanowire single-photon detector (SNSPD) to investigate their photon-number-resolving capability. We find that…

We demonstrate position and energy-resolved phonon-mediated detection of particle interactions in a silicon substrate instrumented with an array of microwave kinetic inductance detectors (MKIDs). The relative magnitude and delay of the…

We demonstrate photon counting at 1550 nm wavelength using microwave kinetic inductance detectors (MKIDs) made from TiN/Ti/TiN trilayer films with superconducting transition temperature Tc ~ 1.4 K. The detectors have a lumped-element design…

A principal component analysis (PCA) of clean microcalorimeter pulse records can be a first step beyond statistically optimal linear filtering of pulses towards a fully non-linear analysis. For PCA to be practical on spectrometers with…

The energy resolution of a single photon counting Microwave Kinetic Inductance Detector (MKID) can be degraded by noise coming from the primary low temperature amplifier in the detector's readout system. Until recently, quantum limited…

This paper describes the energy resolution of Microwave Kinetic Inductance Detectors (MKIDs), and models some limiting factors to it. Energy resolution is a measure of the smallest possible difference in energy of the impinging photons,…

Photon-number resolved detection with superconducting nanowire single-photon detectors (SNSPDs) attracts increasing interest, but lacks a systematic framework for interpreting and benchmarking this capability. In this work, we combine…

Microwave Kinetic Inductance detectors (MKIDs) have been recognized as a powerful new tool for single photon detection. These highly multiplexed superconducting devices give timing and energy measurement for every detected photon. However,…

The Principal Component Analysis (PCA) is a data dimensionality reduction technique well-suited for processing data from sensor networks. It can be applied to tasks like compression, event detection, and event recognition. This technique is…

We use Principal Component Analysis (PCA) to study the gas dynamics in numerical simulations of typical MCs. Our simulations account for the non-isothermal nature of the gas and include a simplified treatment of the time-dependent gas…

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…

Selected for the next generation of adaptive optics (AO) systems, the pyramid wavefront sensor (PWFS) is recognised for its closed AO loop performance. As new technologies are emerging, it is necessary to explore new methods to improve it.…

We demonstrate photon noise limited performance in both phase and amplitude readout in microwave kinetic inductance detectors (MKIDs) consisting of NbTiN and Al, down to 100 fW of optical power. We simulate the far field beam pattern of the…

We present a new straightforward principal component analysis (PCA) method based on the diagonalization of the weighted variance-covariance matrix through two spectral decomposition methods: power iteration and Rayleigh quotient iteration.…

Real-time or near real-time hyperspectral detection and identification are extremely useful and needed in many fields. These data sets can be quite large, and the algorithms can require numerous computations that slow the process down. A…

Model-independent analysis (MIA) methods are generally useful for analysing complex systems in which relationships between the observables are non-trivial and noise is present. Principle Component Analysis (PCA) is one of MIA methods…

Principal component analysis (PCA) is traditionally implemented through a covariance or kernel matrix, leading-eigenvector extraction, and hard rank-$k$ projection. These steps can be computationally costly in high-dimensional and…

Principal Component Analysis (PCA) is an efficient tool to optimize the multiparameter tests of general relativity (GR) where one tests for simultaneous deviations in multiple post-Newtonian (PN) phasing coefficients by introducing…

The surface impedance of a superconductor changes when energy is absorbed and Cooper pairs are broken to produce single electron (quasiparticle) excitations. This change may be sensitively measured using a thin-film resonant circuit called…