activity
20062026
most citedFirst measurement of the total proton-proton cross section at the LHC energy of {\surd} s =7 TeV

282 citations · 1.7k across the 76 of their papers we have counts for

collaborators

85 papers

physics.ins-det2026

Optical readout of MPGDs with solid wavelength shifters

F. M. Brunbauer, A. Cools, M. Cortesi +20

Optical readout of MicroPattern Gaseous Detectors (MPGDs) makes use of the high granularity of imaging sensors to achieve good spatial resolution for radiation imaging and particle…

physics.ins-det2026

Timing performance of large prototype based on $\upmu$RWELL- PICOSEC detector technology with active area

A. Pandey, K. Gnanvo, B. Kross +60

The $\upmu$RWELL-PICOSEC detector, which combines a $\upmu$RWELL gaseous amplification structure with a Cherenkov radiator and photocathode, is a novel approach to acheive fast and…

physics.ins-det2026

Advances in photocathode development for PICOSEC Micromegas precise-timing detectors

M. Lisowska, F. Guerra, A. Gurpinar +46

The PICOSEC Micromegas detector is a~precise-timing gaseous detector that combines a Cherenkov radiator, a~semi-transparent photocathode and a Micromegas amplification stage, targe…

physics.ins-det2025

Design, simulation and performance of the resistive-anode PICOSEC Micromegas detector

Djunes Janssens, Antonija Utrobicic, Marinko Kovacic +52

The PICOSEC Micromegas detector is a Micro-Pattern Gaseous Detector concept developed to achieve tens of picosecond timing resolution for charged particle detection by combining a…

physics.ins-det2025

Design and Performance of a 96-channel Resistive PICOSEC Micromegas Detector for ENUBET

A. Kallitsopoulou, S. Aune, Y. Angelis +54

The PICOSEC-Micromegas (PICOSEC-MM) detector is a fast gaseous detector that achieves picosecond-level timing by coupling a Cherenkov radiator, typically an MgF2 crystal, to a Micr…

hep-ex2025

Overlap-aware segmentation for topological reconstruction of obscured objects

J. Schueler, H. M. Araújo, S. N. Balashov +30

The separation of overlapping objects presents a significant challenge in scientific imaging. While deep learning segmentation-regression algorithms can predict pixel-wise intensit…