arXiv subjects

C. S. Moon

Publications and source records attributed to C. S. Moon.

4 recordsLinked to original sources

arXiv2023

Pulse shape discrimination using a convolutional neural network for organic liquid scintillator signals

A convolutional neural network (CNN) architecture is developed to improve the pulse shape discrimination (PSD) power of the gadolinium-loaded organic liquid scintillation detector to reduce the fast neutron background in the inverse beta decay candidate events of the NEOS-II data. A power spectrum of an event is constructed using a fast Fourier transform of the time domain raw waveforms and put into CNN. An early data set is evaluated by CNN after it is trained using low energy $β$ and $α$ events. The signal-to-background ratio averaged over 1-10 MeV visible energy range is enhanced by more than 20% in the result of the CNN method compared to that of an existing conventional PSD method, and the improvement is even higher in the low energy region.

physics.ins-det↗

arXiv2020

Pulse Shape Discrimination of Fast Neutron Background using Convolutional Neural Network for NEOS II

Pulse shape discrimination plays a key role in improving the signal-to-background ratio in NEOS analysis by removing fast neutrons. Identifying particles by looking at the tail of the waveform has been an effective and plausible approach for pulse shape discrimination, but has the limitation in sorting low energy particles. As a good alternative, the convolutional neural network can scan the entire waveform as they are to recognize the characteristics of the pulse and perform shape classification of NEOS data. This network provides a powerful identification tool for all energy ranges and helps to search unprecedented phenomena of low-energy, a few MeV or less, neutrinos.

physics.ins-det↗

arXiv2019

Advancing LHC Probes of Dark Matter from the Inert 2-Higgs Doublet Model with the Mono-jet Signal

The inert 2-Higgs Doublet Model (i2HDM) is a well-motivated minimal consistent Dark Matter (DM) model, but it is rather challenging to test at the Large Hadron Collider (LHC) in the parameter space allowed by relic density and DM direct detection constraints. This is especially true when considering the latest XENON 1T data on direct DM searches which we use here to present the best current combined limit on the i2HDM parameter space. In this analysis, we present prospects to advance the exploitation of DM mono-jet signatures from the i2HDM at the LHC, by emphasising that a shape analysis of the missing transverse momentum distribution allows one to sizably improve the LHC discovery potential. As a key element of our analysis, we explore the validity of using an effective vertex, $ggH$, for the coupling of the Higgs boson to gluons using a full one-loop computation. We have found sizeable differences between the two approaches, especially in the high missing transverse momentum region, and incorporated the respective K-factors to obtain the correct kinematical distributions. As a result, we delineate a realistic search strategy and present the improved current and projected LHC sensitivity to the i2HDM parameter space.

hep-ph↗

arXiv2013

Silicon Avalanche Pixel Sensor for High Precision Tracking

The development of an innovative position sensitive pixelated sensor to detect and measure with high precision the coordinates of the ionizing particles is proposed. The silicon avalanche pixel sensors (APiX) is based on the vertical integration of avalanche pixels connected in pairs and operated in coincidence in fully digital mode and with the processing electronics embedded on the chip. The APiX sensor addresses the need to minimize the material budget and related multiple scattering effects in tracking systems requiring a high spatial resolution in the presence of a large occupancy. The expected operation of the new sensor features: low noise, low power consumption and suitable radiation tolerance. The APiX device provides on-chip digital information on the position of the coordinate of the impinging charged particle and can be seen as the building block of a modular system of pixelated arrays, implementing a sparsified readout. The technological challenges are the 3D integration of the device under CMOS processes and integration of processing electronics.

physics.ins-det↗