ML-based waveform decomposition enables real-time Cherenkov/scintillation separation in dual-readout crystal calorimeters with reduced sampling requirements and FPGA-compatible latency.
First demonstration of agentic-AI driven detector optimization using vertically-integrated bilevel framework for dual-readout calorimeters.
A demonstration of ML-based cluster counting for drift chambers is presented.
In this paper, we present a simple formula for the resolution of a dual readout calorimeter, which we verify with a toy simulation and with full simulation results. This formula can help those new to dual readout calorimetry understand its strengths and limitations.
Our simulations demonstrates the effect of inclusion of Cherenkov light in the reconstruction of energies for sampling and homogeneous calorimeters.
We designed a detector for the quantitative assessment of membrane materials’ radon blocking capabilities.
In this study, we focus on the impact of a pair of adjacent planets with a narrower separation on the orbit crossing time, without altering the separation of other planets.