arXiv subjects

Ayse Bat

Publications and source records attributed to Ayse Bat.

3 recordsLinked to original sources

arXiv2026

Building an AI-native Research Ecosystem for Experimental Particle Physics: A Community Vision

Experimental particle physics seeks to understand the universe by probing its fundamental particles and forces and exploring how they govern the large-scale processes that shape cosmic evolution. This whitepaper presents a vision for how Artificial Intelligence (AI) can accelerate discovery in this field. We outline grand challenges that must be addressed to enable transformative breakthroughs and describe how current and planned experimental facilities can implement this vision to advance our understanding of the vast and complex physical world from the smallest to the largest scales. We show how facilities currently under construction, such as the HL-LHC, DUNE and soon EIC, can both benefit from and serve as proving grounds for this vision, while also enabling a longer-term goal for how future experiments -- like FCC-ee at CERN, IceCube-Gen2, a Muon Collider in the U.S., and smaller to mid-scale projects -- can be fully AI-native. We describe how a truly national-scale collaboration, jointly managed across large funding partners, and involving both DOE laboratories and universities, can make this happen.

hep-ex↗

arXiv2024

Using Machine Learning to Separate Cherenkov and Scintillation Light in Hybrid Neutrino Detector

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 using the Rat-Pac software, which was built on the Geant4 architecture. The use of the WbLS medium has the capability to generate both Scintillation and Cherenkov light inside a single detector. To show the separation power of these two physics events, we will use the supervised learning approach. The assessment utilized a confusion matrix, classification report, and ROC curve, with the ROC curve indicating a performance result of $0.96 \pm 1.2\times 10^{-4}$. The research also aimed to identify essential parameters for effectively distinguishing these physics events through machine learning. For this, the study also introduced the SHAP methodology, utilizing game theory to assess feature contributions. The findings demonstrated that the number of hits has a significant effect on the trained model, while the mean hit time has a somewhat smaller impact. This research advances the utilization of AI and simulation data for accurate Cherenkov and Scintillation light separation in neutrino detectors

physics.ins-det↗

arXiv2021

Low Energy Neutrino Detection with a Portable Water-based Liquid Scintillator Detector

In this study, the conceptual design and physics simulations of a near-field Water-based Liquid Scintillator (WbLS) detector placed 100 meters from the Akkuyu Nuclear Power Plant (ANPP), currently under construction and aiming at being Turkey's first nuclear power plant, is presented. The ANPP is an excellent opportunity for neutrino studies and the development of an R&D program for neutrino detectors in Turkey. The Reactor Neutrino Experiments of Turkey (RNET) program includes a compact and portable detector with a 2.5-ton volume of WbLS and a ~30% photo-coverage, and the program is planned to be expanded with a medium-size 30-ton detector that will be an international testbed for low energy neutrino studies for WbLS and new detector technologies. In the following, the focus will be on the smaller ~2.5-ton detector, instrumented with 8-inch high quantum efficiency PMTs and two layers of cosmic veto paddles, covering all sides of the detector, to track and veto cosmic particles. Inverse Beta Decay (IBD) events from electronic antineutrinos generated in the reactor core are simulated using the RAT-PAC simulation package and several liquids with different percentages of Liquid Scintillator (LS) and Gadolinium (Gd) are investigated.

physics.ins-det↗