Related papers: Comparative pulse shape discrimination study for C…
In this paper, we propose a multi-scale deep feature learning method for high-resolution satellite image classification. Specifically, we firstly warp the original satellite image into multiple different scales. The images in each scale are…
This work describes a class of liquid scintillators that contain mostly water (>50 wt. % of the entire composition) and can discriminate between interactions induced by neutrons and gamma rays. By balancing the interface interactions…
Well-known quantum machine learning techniques, namely quantum kernel assisted support vector machines (QSVMs) and quantum convolutional neural networks (QCNNs), are applied to the binary classification of pulsars. In this comparitive study…
We classified the decoupled stochastic parallel gradient descent (SPGD) optimization model into two different types: software and hardware decoupling methods. A kind of software decoupling method is then proposed and a kind of hardware…
Particle detectors based on scintillators are widely used in high-energy physics and astroparticle physics experiments, nuclear medicine imaging, industrial and environmental detection, etc. Precisely extracting scintillation signal…
Recent advancements in deep learning have shifted the development of brain imaging analysis. However, several challenges remain, such as heterogeneity, individual variations, and the contradiction between the high dimensionality and small…
Discriminating between Parkinson's Disease (PD) and Progressive Supranuclear Palsy (PSP) is difficult due to overlapping symptoms, especially early on. Saccades (rapid conjugate eye movements between fixation points) are affected by both…
Pulsars have been primarily detected by their narrow pulses or periodicity in time domain data. Interferometric surveys for pulsars are challenging due to the trade-off between beam sensitivity and beam size and the corresponding tradeoff…
In this work, we explore the possibility of using probabilistic learning to identify pulsar candidates. We make use of Deep Gaussian Process (DGP) and Deep Kernel Learning (DKL). Trained on a balanced training set in order to avoid the…
Purpose: To present the performance of a multi-point plastic scintillation detector (mPSD) as a tool in vivo dosimetry in brachytherapy. Methods: A previously optimized three-point sensor system was used for in vivo HDR brachytherapy…
Cs$_2$LiLaBr$_6$:Ce$^{3+}$ (CLLB) is an elpasolite scintillator that offers excellent linearity and gamma-ray energy resolution and sensitivity to thermal neutrons with the ability to perform pulse-shape discrimination (PSD) to distinguish…
A novel beam profiler based on microstructured scintillation resin is presented. The detector consists of a bundle of waveguides, with an active area of 30 x 30 mm$^2$ and a pitch of 400 $\mu$m, obtained by molding a scintillating resin…
High-purity germanium detectors are widely used in rare-event searches due to their excellent energy resolution and extremely high intrinsic (radio)purity. In experiments searching for neutrinoless double beta decay in $^{76}$Ge such as…
The demand for novel detector mediums such as Water-based Liquid Scintillator (WbLS) has increased over the last few decades due to their capability for both low energy particle interactions and higher light yield. Recently, the usage of…
The light output, $\alpha/\beta$ ratio, and pulse shape have been investigated at $-25^\circ$ C with PbWO$_4$ crystal scintillators undoped, and doped by F, Eu, Mo, Gd and S. The fast $0.01-0.06 \mu$s and middle $0.1-0.5 \mu$s components of…
The DEAP-1 low-background liquid argon detector was used to measure scintillation pulse shapes of electron and nuclear recoil events and to demonstrate the feasibility of pulse-shape discrimination (PSD) down to an electron-equivalent…
Among the detectors used for rare event searches, such as neutrinoless Double Beta Decay (0$\nu$DBD) and Dark Matter experiments, bolometers are very promising because of their favorable properties (excellent energy resolution, high…
Machine learning methods find growing application in the reconstruction and analysis of data in high energy physics experiments. A modified convolutional autoencoder model was employed to identify and reconstruct the pulses from…
Change point detection (CPD) methods aim to identify abrupt shifts in the distribution of input data streams. Accurate estimators for this task are crucial across various real-world scenarios. Yet, traditional unsupervised CPD techniques…
In multi-temporal SAR interferometry (MT-InSAR), persistent scatterer (PS) pixels are used to estimate geophysical parameters, essentially deformation. Conventionally, PS pixels are selected on the basis of the estimated noise present in…