arXiv subjects

Sten Astrand

Publications and source records attributed to Sten Astrand.

2 recordsLinked to original sources

arXiv2026

Impact of Cold Noise on the tracking performance of ATLAS ITk short strip barrel modules using a charged particle beam

The inner tracking system of the ATLAS experiment will be upgraded to a full silicon detector in 2030 for HL-LHC. The new tracking system is called ITk, the Inner Tracker. It is required to be operable with efficiency higher than 99\% and noise hit occupancy smaller than 0.1\%. During the pre-production phase of the ITk project, many short-strip modules were observed to exhibit so-called "Cold Noise (CN)", wherein clusters of strips displayed very high noise when the modules were operated at temperatures below~$-35\degree$C. To investigate the CN impact and ensure the quality of module production, huge amount of effort have been put in by the collaboration. This paper focuses on the impact of CN on the tracking performance by examining two short strip modules that exhibit CN: one is non-irradiated, while the other one has been irradiated to the maximum expected end-of-lifetime fluence. For each module, the global and single strip tracking performance are evaluated.

physics.ins-det↗

arXiv2025

Review of Machine Learning for Real-Time Analysis at the Large Hadron Collider experiments ALICE, ATLAS, CMS and LHCb

The field of high energy physics (HEP) has seen a marked increase in the use of machine learning (ML) techniques in recent years. The proliferation of applications has revolutionised many aspects of the data processing pipeline at collider experiments including the Large Hadron Collider (LHC). In this whitepaper, we discuss the increasingly crucial role that ML plays in real-time analysis (RTA) at the LHC, namely in the context of the unique challenges posed by the trigger systems of the large LHC experiments. We describe a small selection of the ML applications in use at the large LHC experiments to demonstrate the breadth of use-cases. We continue by emphasising the importance of collaboration and engagement between the HEP community and industry, highlighting commonalities and synergies between the two. The mutual benefits are showcased in several interdisciplinary examples of RTA from industrial contexts. This whitepaper, compiled by the SMARTHEP network, does not provide an exhaustive review of ML at the LHC but rather offers a high-level overview of specific real-time use cases.

hep-ex↗