15–19 Sept 2025
CERN
Europe/Zurich timezone

Super resolution models for offline-like reconstruction in the scouting stream

16 Sept 2025, 15:25
5m
40/S2-A01 - Salle Anderson (CERN)

40/S2-A01 - Salle Anderson

CERN

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2. Optimal AI deployment for Online Data Processing Optimal AI deployment for Online Data Processing

Speaker

Sebastian Wuchterl (CERN)

Description

CMS is investing resources in the scouting stream but its use so far has been limited to a few applications, mostly with jets and muons. Scouting has more potential than that, particularly with its extension at L1, being investigated in Run 3 and to reach its best in HL-LHC. One of the limiting factor towards a broad use of scouting is the resolution loss online also due to resource constraints, with respect to the offline reconstruction. We propose to address this issue using super-resolution models, per-object (jet, leptons, etc) resolution and object identification, upscaling models similar to what is done in data analysis to unfold high-level features to generator-level view of the event. This naturally extends to the standard L1T and HLT streams and reconstruction.

CERN group/ Experiment

EP-CMG

Working area Area 2: Optimal AI deployment for Online Data Processing
Project goals Make scouting resolution closer to offline-like for Run3, while preparing for HL-LHC. Deliver a first HLT/HLT Scouting model.
Timeline 3 years
Available person power 0
Additional person power request 2 PhD students
Is this an already ongoing activity? No
Indicative hardware resources needs Access to a GPU cluster with LCG-like software stack and cvmfs access with fast storage facilities across the full duration of the project

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