Related papers: Pulse Shape Simulation and Discrimination using Ma…
Superconducting nanowire single-photon detectors (SNSPDs) can enable photon-number resolution (PNR) based on accurate measurements of the detector's response time to few-photon optical pulses. In this work we investigate the impact of the…
In many signal processing applications, including communications, sonar, radar, and localization, a fundamental problem is the detection of a signal of interest in background noise, known as signal detection [1] [2]. A simple version of…
We present a novel two-stage approach which combines unsupervised and supervised machine learning to automatically identify and classify single pulses in radio pulsar search data. In the first stage, we identify astrophysical pulse…
We propose a feature-extraction procedure based on the statistical characterization of waveforms, applied as a fast pre-processing stage in a pattern recognition task using simple artificial neural network models. This procedure involves…
Particle velocimetry is essential in solid fuel combustion studies, however, the accurate detection and tracking of particles in high Particle Number Density (PND) combustion scenario remain challenging. The current study advances the…
Perceptual sound matching (PSM) aims to find the input parameters to a synthesizer so as to best imitate an audio target. Deep learning for PSM optimizes a neural network to analyze and reconstruct prerecorded samples. In this context, our…
Detecting Beyond Standard Model (BSM) signals in high-energy particle collisions presents significant challenges due to complex data and the need to differentiate rare signal events from Standard Model (SM) backgrounds. This study…
Picosecond laser pulses have been used as a surface colouring technique for noble metals, where the colours result from plasmonic resonances in the metallic nanoparticles created and redeposited on the surface by ablation and deposition…
Ultra-fast and multi-dimensional spectroscopy gives a powerful looking glass into the dynamics of molecular systems. In particular two-dimensional electronic spectroscopy (2DES) provides a probe of coherence and the flow of energy within…
We present a convolutional autoencoder to denoise pulses from a p-type point contact high-purity germanium detector similar to those used in several rare event searches. While we focus on training procedures that rely on detailed detector…
Regular object detection methods output rectangle bounding boxes, which are unable to accurately describe the actual object shapes. Instance segmentation methods output pixel-level labels, which are computationally expensive for real-time…
We present a detailed characterization of the thermal neutron sensitive transparent glass scintillator SG101, benchmarked against the conventional LiF ZnS(Ag)based scintillator EJ426. The detection efficiency, energy resolution, and pulse…
A successfull application of Geiger-mode multipixel avalanche diodes (GMAPDs) for pulse-shape discrimination in alpha-beta spectrometry using organic liquid scintillator is described in this paper. Efficient discrimination of alpha and beta…
The cascade training technique which was developed during our work on the MiniBooNE particle identification has been found to be a very efficient way to improve the selection performance, especially when very low background contamination…
Discriminative pattern mining is an essential task of data mining. This task aims to discover patterns which occur more frequently in a class than other classes in a class-labeled dataset. This type of patterns is valuable in various…
Performance of a simple method of particle identification via pulse rise time discrimination is demonstrated for slow pulses from charge-sensitive preamplifiers with rise times ranging from 10 ns to 500 ns. The method is based on a…
In the past few years, there are several researches on Parkinson's disease (PD) recognition using single-photon emission computed tomography (SPECT) images with deep learning (DL) approach. However, the DL model's complexity usually results…
Significant challenges exist in efficient data analysis of most advanced experimental and observational techniques because the collected signals often include unwanted contributions--such as background and signal distortions--that can…
Phase-sensitive detection (PSD) is an important experimental technique that allows signals to be extracted from noisy data. PSD is also used in modulation spectroscopy and is used in the stabilization of optical sources. Commercial lock-in…
Accurate particle size distribution (PSD) measurement is important in industries such as mining, pharmaceuticals, and fertilizer manufacturing, significantly influencing product quality and operational efficiency. Traditional PSD methods…