Related papers: Deep Learning Based Event Reconstruction for Cyclo…
The Cyclotron Radiation Emission Spectroscopy (CRES) technique pioneered by Project 8 measures electromagnetic radiation from individual electrons gyrating in a background magnetic field to construct a highly precise energy spectrum for…
Cyclotron Radiation Emission Spectroscopy (CRES) is a technique for precision measurement of the energies of charged particles, which is being developed by the Project 8 Collaboration to measure the neutrino mass using tritium beta-decay…
Cyclotron Radiation Emission Spectroscopy (CRES) is an approach to measuring the energy of an electron trapped in an externally applied magnetic field. The bare electron can come from different interactions, including photoelectric…
The recently developed technique of Cyclotron Radiation Emission Spectroscopy (CRES) uses frequency information from the cyclotron motion of an electron in a magnetic bottle to infer its kinetic energy. Here we derive the expected radio…
Cyclotron Radiation Emission Spectroscopy (CRES) is a novel technique for the precise measurement of relativistic electron energy. This technique is being employed by the Project~8 collaboration for measuring a high-precision tritium beta…
Cyclotron Radiation Emission Spectroscopy (CRES) is a modern approach for determining charged particle energies via high-precision frequency measurements of the emitted cyclotron radiation. For CRES experiments with gas within the fiducial…
Project 8 has developed a novel technique, Cyclotron Radiation Emission Spectroscopy (CRES), for direct neutrino mass measurements. A CRES-based experiment on the beta spectrum of tritium has been carried out in a small-volume apparatus. We…
Deep neural networks, albeit their great success on feature learning in various computer vision tasks, are usually considered as impractical for online visual tracking because they require very long training time and a large number of…
Cyclotron Radiation Emission Spectroscopy (CRES) is a technique for measuring the kinetic energy of charged particles through a precision measurement of the frequency of the cyclotron radiation generated by the particle's motion in a…
The most sensitive direct method to establish the absolute neutrino mass is observation of the endpoint of the tritium beta-decay spectrum. Cyclotron Radiation Emission Spectroscopy (CRES) is a precision spectrographic technique that can…
In this study we explore the possibility to use deep learning for the reconstruction of phase images from 4D scanning transmission electron microscopy (4D-STEM) data. The process can be divided into two main steps. First, the complex…
In neutrino experiments, neutrino energy reconstruction is crucial because neutrino oscillations and differential cross-sections are functions of neutrino energy. It is also challenging due to the complexity in the detector response and…
Many experimental methods in physics require understanding radiation from single particles into non-trivial electromagnetic mode structures. Such characterization is critical for Cyclotron Radiation Emission Spectroscopy (CRES), an…
We report the development of deep learning coherent electron diffractive imaging at sub-angstrom resolution using convolutional neural networks (CNNs) trained with only simulated data. We experimentally demonstrate this method by applying…
Speech enhancement has benefited from the success of deep learning in terms of intelligibility and perceptual quality. Conventional time-frequency (TF) domain methods focus on predicting TF-masks or speech spectrum, via a naive convolution…
Machine-learning-based methods can be developed for the reconstruction of clusters in segmented detectors for high energy physics experiments. Convolutional neural networks with autoencoder architecture trained on labeled data from a…
The purpose of this work is to examine the application of a deep learning model in event reconstruction of neutrino interactions. The challenges faced in event reconstruction include the placement of an accurate primary neutrino interaction…
This work proposes a spectral convolutional neural network (CNN) operating on laser induced breakdown spectroscopy (LIBS) signals to learn to (1) disentangle spectral signals from the sources of sensor uncertainty (i.e., pre-process) and…
In this paper, we explore a novel method for tomographic image reconstruction in the field of SPECT imaging. Deep Learning methodologies and more specifically deep convolutional neural networks (CNN) are employed in the new reconstruction…
Project 8 is a tritium endpoint neutrino mass experiment utilizing a phased program to achieve sensitivity to the range of neutrino masses allowed by the inverted mass hierarchy. The Cyclotron Radiation Emission Spectroscopy (CRES)…