18th International Workshop on Boosted Object Phenomenology, Reconstruction, Measurements, and Searches at Colliders

Session

Jet reconstruction, calibration and performance

13 Jul 2026, 14:00

71. Progress in reconstructing, classifying and calibrating hadronic objects in ATLAS

Fang-Ying Tsai (Stony Brook University (US))

13/07/2026, 14:00

Talk

Hadronic objects reconstruction, classification & calibration are key ingredients of many physics analysis in ATLAS. The collaboration is continuously improving their performance by refining various aspects of the related procedures such as statistical methods, data-driven approach or cutting-edge machine learning techniques. This contribution presents highlights of the work and its impact of...

38. Jets and missing transverse momentum performance in Run 3 of the CMS experiment

Theodoros Chatzistavrou (Helsinki Institute of Physics(FI))

13/07/2026, 14:20

Talk

The LHC managed to deliver more than 200/fb of integrated luminosity to ATLAS + CMS in 2024 and 2025, and with this exceeded the expectations for Run 3. Data were collected with an average of 60 pileup interactions happening at the same bunch crossing, and thus challenging the CMS detector and jet reconstruction. We present the latest developments in pileup mitigation, jet calibration, and jet...

41. Particle Flow reconstruction for CMS phase 2

Andreas Hinzmann (Deutsches Elektronen-Synchrotron (DE))

13/07/2026, 14:40

Talk

Ongoing developments of the particle-flow reconstruction for the Phase-2 upgrade of the CMS experiment are presented, with emphasis on algorithmic changes relevant for extreme pileup conditions. The contribution focuses on the integration of new detector inputs, in particular high-granularity calorimetry and precision timing, and their use in particle reconstruction and pileup mitigation....

7. Machine Learning Approaches to the Calibration of the Signals and their Classification as Pile-up in the ATLAS Calorimeters

Mr Buddhadeb Mondal (Czech Academy of Sciences (CZ))

13/07/2026, 15:00

Talk

Employing machine-learned local calibrations for the basic calorimeter signals in the ATLAS experiment at the Large Hadron Collider (LHC), which are formed by clustering topologically connected cell signals (topo-clusters), shows indications of significant performance improvements in terms of accuracy and precision. The most successfully trained model so far is a dense neural network (DNN)...