Related papers: AI based Scintillation Detector Calibration
We describe the automated calibration system for the antineutrino detectors in the Daya Bay Neutrino Experiment. This system consists of 24 identical units instrumented on 8 identical 20-ton liquid scintillator detectors. Each unit is a…
We numerically examine the spatial evolution of the structure of coherent and partially coherent laser beams, including the optical vortices, propagating in turbulent atmospheres. The influence of beam fragmentation and wandering relative…
In CLEAN (Cryogenic Low Energy Astrophysics with Noble gases), a proposed neutrino and dark matter detector, background discrimination is possible if one can determine the location of an ionizing radiation event with high accuracy. We…
One emerging application of machine learning methods is the inference of galaxy cluster masses. In this note, machine learning is used to directly combine five simulated multiwavelength measurements in order to find cluster masses. This is…
We describe a new concept to correct for scintillation noise on high-precision photometry in large and extremely large telescopes using telemetry data from adaptive optics (AO) systems. Most wide-field AO systems designed for the current…
The advancements in the state of the art of generative Artificial Intelligence (AI) brought by diffusion models can be highly beneficial in novel contexts involving Earth observation data. After introducing this new family of generative…
This research investigates the separation of Cherenkov and Scintillation light signals within a simulated Water-based Liquid Scintillator (WbLS) detector, utilizing the XGBoost machine learning algorithm. The simulation data were gathered…
AI and machine learning based approaches are becoming ubiquitous in almost all engineering fields. Control engineering cannot escape this trend. In this paper, we explore how AI tools can be useful in control applications. The core tool we…
Recent works have established that AI models introduce spectral artifacts into generated images and propose approaches for learning to capture them using labeled data. However, the significant differences in such artifacts among different…
Noble liquid detectors have become an attractive option for exploring physics beyond the standard model. Current experiments are using these detectors to search for dark matter interactions, neutrinoless double beta decay, and other…
The calibration of (low-cost) inertial sensors has become increasingly important over the past years since their use has grown exponentially in many applications going from unmanned aerial vehicle navigation to 3D-animation. However, this…
Organic scintillators are widely used for fast neutron detection and spectroscopy. Several effects complicate the interpretation of results from detectors based upon these materials. First, fast neutrons will often leave a detector before…
The use of machine learning algorithms is an attractive way to produce very fast detector simulations for scattering reactions that can otherwise be computationally expensive. Here we develop a factorised approach where we deal with each…
Analyzing vibration data using deep neural network algorithms is an effective way to detect damages in rotating machinery at an early stage. However, the black-box approach of these methods often does not provide a satisfactory solution…
This report represents a roadmap for integrating Artificial Intelligence (AI)-based image analysis algorithms into existing Radiology workflows such that: (1) radiologists can significantly benefit from enhanced automation in various…
Scintillators are important materials for radiographic imaging and tomography (RadIT), when ionizing radiations are used to reveal internal structures of materials. Since its invention by R\"ontgen, RadIT now come in many modalities such as…
In the evaluation of novel scintillators, it is important to ensure that the spectrum of the light emitted by the scintillator is well matched to the response of the photomultiplier. In attempting to measure this spectrum using radioactive…
Bringing artificial intelligence (AI) alongside next-generation X-ray imaging detectors, including CCDs and DEPFET sensors, enhances their sensitivity to achieve many of the flagship science cases targeted by future X-ray observatories,…
Connecting multiple machine learning models into a pipeline is effective for handling complex problems. By breaking down the problem into steps, each tackled by a specific component model of the pipeline, the overall solution can be made…
An overview of some tools and techniques being developed for data conditioning (regression of instrumental and environmental artifacts from the data channel), detector design evaluation (modeling the science ``reach'' of alternative…