Nothing Special   »   [go: up one dir, main page]

zu Theenhausen et al., 2023 - Google Patents

Neural-network-based level-1 trigger upgrade for the SuperCDMS experiment at SNOLAB

zu Theenhausen et al., 2023

View PDF
Document ID
16793881975809217479
Author
zu Theenhausen H
von Krosigk B
Wilson J
Publication year
Publication venue
Journal of Instrumentation

External Links

Snippet

The extended physics program of the SuperCDMS SNOLAB dark matter search experiment aims to maximize the sensitivity to low-mass dark matter. To realize this, an upgrade of the existing level-1 trigger of the data acquisition system is proposed by making use of a …
Continue reading at iopscience.iop.org (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations
    • G06F17/18Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50Computer-aided design
    • G06F17/5009Computer-aided design using simulation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F7/00Methods or arrangements for processing data by operating upon the order or content of the data handled
    • G06F7/58Random or pseudo-random number generators
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F3/00Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01Input arrangements or combined input and output arrangements for interaction between user and computer
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V1/00Seismology; Seismic or acoustic prospecting or detecting
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R13/00Arrangements for displaying electric variables or waveforms

Similar Documents

Publication Publication Date Title
De et al. Tidal deformabilities and radii of neutron stars from the observation of GW170817
Rapp et al. Dead time compensation for high-flux ranging
Ellis et al. Robust limits on Lorentz violation from gamma-ray bursts
Ismail et al. Neutral current neutrino interactions at FASER ν
US20030115017A1 (en) Method and apparatus for analyzing a disribution
Larson Simulation and identification of non-Poissonian noise triggers in the IceCube neutrino detector
Guidorzi et al. Individual power density spectra of Swift gamma-ray bursts
Johnson et al. A solar cycle dependence of nonlinearity in magnetospheric activity
Iess et al. LSTM and CNN application for core-collapse supernova search in gravitational wave real data
Ristic et al. Interpolating detailed simulations of kilonovae: Adaptive learning and parameter inference applications
Angloher et al. Towards an automated data cleaning with deep learning in CRESST
Ruzmaikin et al. Distribution and clustering of fast coronal mass ejections
zu Theenhausen et al. Neural-network-based level-1 trigger upgrade for the SuperCDMS experiment at SNOLAB
Theenhausen et al. Neural-network-based level-1 trigger upgrade for the SuperCDMS experiment at SNOLAB
Kafaee et al. Pile-up correction by genetic algorithm and artificial neural network
Gesualdi et al. Estimation of the number of counts on a particle counter detector with full time resolution
Lennox et al. Assessing and minimizing contamination in time of flight basedvalidation data
Wilson et al. The level-1 trigger for the SuperCDMS experiment at SNOLAB
Barat et al. A bimodal Kalman smoother for nuclear spectrometry
Tindale et al. Solar wind plasma parameter variability across solar cycles 23 and 24: From turbulence to extremes
US20100030721A1 (en) Physics-Based, Bayesian Sequential Detection Method and System for Radioactive Contraband
Kallitsopoulou Development of a simulation model and precise timing techniques for PICOSEC-micromegas detectors
WO2021075345A1 (en) Signal processing method, learning model generation method, signal processing device, radiation detection device, and computer program
Martín et al. Wilcoxon signed-rank-based technique for the pulse-shape analysis of HPGe detectors
Medina Reconstruction of Xmax and Energy from 3--100 PeV using 5 Years of Data From IceTop and IceCube and its Applications