Related papers: Detector signal characterization with a Bayesian n…
In this work we demonstrate the use of neural networks for rapid extraction of signal parameters of discretely sampled signals. In particular, we use dense autoencoder networks to extract the parameters of interest from exponentially…
The present operation of the ground-based network of gravitational-wave laser interferometers in "enhanced" configuration brings the search for gravitational waves into a regime where detection is highly plausible. The development of…
In addition to serve as platforms for dynamic spectrum access, cognitive radios can also serve as a method for improving the performance of wireless communication systems by smartly adjusting their operating parameters according to the…
The NIO1 experiment hosts a flexible RF H- ion source, developed by INFN-LNL and Consorzio RFX to improve the present concepts for the production and acceleration of negative ions. The source is also used to benchmark the instrumentation…
We extend the study of the performance of a prototype two-phase liquid xenon WIMP dark matter detector to recoil energies below 20 keV. We demonstrate a new method for obtaining the best estimate of the energies of events using a calibrated…
In this paper, a wavelet-based neural network (WNN) classifier for recognizing EEG signals is implemented and tested under three sets EEG signals (healthy subjects, patients with epilepsy and patients with epileptic syndrome during the…
Fault monitoring and diagnostics are important to ensure reliability of electric motors. Efficient algorithms for fault detection improve reliability, yet development of cost-effective and reliable classifiers for diagnostics of equipment…
Wearable electrocardiogram (ECG) measurement using dry electrodes has a problem with high-intensity noise distortion. Hence, a robust noise reduction method is required. However, overlapping frequency bands of ECG and noise make noise…
Neural networks are used extensively in classification problems in particle physics research. Since the training of neural networks can be viewed as a problem of inference, Bayesian learning of neural networks can provide more optimal and…
The novel approach was developed for multilevel signal detection in channels with impulsive non-Gaussian noise. This approach consists of using morphological nonlinear image filtration principles for two dimensional signals. It is a new…
Gravitational wave data from ground-based detectors is dominated by instrument noise. Signals will be comparatively weak, and our understanding of the noise will influence detection confidence and signal characterization. Mis-modeled noise…
We revisit the detection of luminous dark matter in direct detection experiments. In this scenario, dark matter scatters endothermically to produce an excited state, which decays to produce a photon. We explore ways in which the electron…
As liquid xenon TPCs increase in target mass while pursuing the direct detection of WIMP dark matter, the technical challenges arising due to their size call for new solutions and open the discussion on alternative detector concepts.…
Waveform sampling systems are used pervasively in the design of front end electronics for radiation detection. The introduction of new feature extraction algorithms (eg. neural networks) to waveform sampling has the great potential to…
We propose a dark matter model in which the signal in direct detection experiments arises from electromagnetic, not nuclear, energy deposition. This can provide a novel explanation for DAMA while avoiding many direct detection constraints.…
The imaging performance of a high-resolution preclinical microPET system employing liquid xenon as the gamma ray detection medium was simulated. The arrangement comprises a ring of detectors consisting of trapezoidal LXe time projection…
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 study a new class of signals where fermionic dark matter is absorbed by bound electron targets. Fermionic absorption signals in direct detection and neutrino experiments are sensitive to dark matter with sub-MeV mass, probing a region of…
This paper will discuss the microphysical simulation of interactions in liquid xenon, the active detector medium in many leading rare-event searches for new physics, and describe experimental observables useful for understanding detector…
A Convolutional Neural Network architecture was used to classify various isotopes of time-sequenced gamma-ray spectra, a typical output of a radiation detection system of a type commonly fielded for security or environmental measurement…