Hayrapetyan, A.Tumasyan, A.Adam, W.Andrejkovic, J. W.Benato, L.Bergauer, T.Druzhkin, D.2026-03-102026-03-1020251748-0221https://doi.org/10.1088/1748-0221/20/11/P11006https://hdl.handle.net/20.500.13091/13052Tuominiemi, Jorma/0000-0003-0386-8633; Alessano, Calani/0000-0001-7774-0099; Cavallo, Nicola/0000-0003-1327-9058; Wadud, Mohammad Abrar/0000-0002-0653-0761; Bandyopadhyay, Hirak/0000-0001-9726-4915; Lehti, Sami/0000-0003-1370-5598; Myllymäki, Mikael/0000-0003-0510-3810; Jang, Woojin/0000-0002-1571-9072; Lampén, Tapio/0000-0002-8398-4249; Rossi Tisbeni, Simone/0000-0001-6776-285X; Xie, Wei/0000-0003-1430-9191; Lehti, Sami/0000-0003-1370-5598; Bartek, Rachel/0000-0002-1686-2882; Rantanen, Milla-Maarit/0000-0002-6764-0016; Sastre, Javier/0000-0002-1654-2846; Lampén, Tapio/0000-0002-8398-4249Measurements in the highly Lorentz-boosted regime provoke increased interest in probing the Higgs boson properties and in searching for particles beyond the standard model at the LHC. In the CMS Collaboration, various boosted-object tagging algorithms, designed to identify hadronic jets originating from a massive particle decaying to b (b) over bar or c (c) over bar, have been developed and deployed across a range of physics analyses. This paper highlights their performance on simulated events, and summarizes novel calibration techniques using proton-proton collision data collected at root s = 13 TeV during the 2016-2018 LHC data-taking period. Three dedicated methods are used for the calibration in multijet events, leveraging either machine learning techniques, the presence of muons within energetic boosted jets, or the reconstruction of hadronically decaying high-energy Z bosons. The calibration results, obtained through a combination of these approaches, are presented and discussed.eninfo:eu-repo/semantics/openAccessPattern RecognitionCluster FindingCalibration and Fitting MethodsPerformance of High Energy Physics DetectorsArticle10.1088/1748-0221/20/11/P11006