Papers
Topics
Authors
Recent
AI Research Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 62 tok/s
Gemini 2.5 Pro 47 tok/s Pro
GPT-5 Medium 12 tok/s Pro
GPT-5 High 10 tok/s Pro
GPT-4o 91 tok/s Pro
Kimi K2 139 tok/s Pro
GPT OSS 120B 433 tok/s Pro
Claude Sonnet 4 31 tok/s Pro
2000 character limit reached

New results from fast timing iLGAD sensor on Timepix4 (2509.09308v1)

Published 11 Sep 2025 in physics.ins-det

Abstract: With the High-Luminosity Large Hadron Collider (HL-LHC) the number of collisions per bunch crossing increases. To cope with these high rates in the pixel trackers, per-pixel time measurements are required, which implies the need for fast sensors. The inverse Low-Gain Avalanche Detector (iLGAD) is one of the fast sensor options that is being investigated. This paper will show the results of an inverse Low-Gain Avalanche Detector (iLGAD) with a pitch of 55 \textmu m, a thickness of 250~\textmu m and a large-area (2~cm$2$), bump bonded to a Timepix4 ASIC. Timepix4 has 195~ps time binning on each pixel and therefore an excellent ASIC to test the sensor. The sensor is characterised with radio-active source measurements in the lab, and during beam test at the CERN SPS North Area H8 beamline, where the Timepix4 telescope was used. The telescope has a time reference of 12~ps and a pointing resolution of 2.4 $\pm$ 0.1~\textmu m. The iLGAD shows an almost uniform gain of approximately 4 and an efficiency of 99.6 $\pm$ 0.1\%. Without any corrections the obtained time resolution is about 750~ps. After timewalk and clock corrections the time resolution becomes 377 $\pm$ 7~ps. Grazing angle measurements have been done, which allow to measure the time resolution as function of depth of the charge deposition in the sensor. This provides more insight for the perpendicular time resolution.

Summary

We haven't generated a summary for this paper yet.

Lightbulb On Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Authors (1)

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube