Our methods to track down reconstruction issues using JEC workflows are beyond the state-of-the-art of past calibrations, and have truly shown their power in Run 3, where we have already been able to find, understand and fix four critical reconstruction issues, one of which was mission critical. These issues were causing a loss up of to 50% of multi-TeV scale jets, or affected their response by up to 10-15%. In the case of forward region, the miscalibration of a fraction of jet energy at high jet |eta| reached up to 300%. Thanks to our rolling calibration, the impact of these issues was limited and/or can be addressed with re-reconstruction of data.
Working on Run 2 data, our novel methods go beyond the state-of-the-art for flavor-JES, as documented in one PhD and two MSc theses:
1) quark and gluon jet tagging scale factors for the first time at CMS as a function of pT and including advanced machine learning taggers, made possible by working-point based approach,
2) novel method to measures gluon jet JES relative to light quark jet JES using dijet events only, extending gluon jet JES to TeV scale jets for the first time,
3) novel method measures strange-quark jet JES relative to charm-quark jet JES and both relative to ud-quark jet JES in W>qq' events, marking the first time strange-jet JES is directly measured in data.
These novel methods are particularly important because they extend the direct data-based flavor-JES measurements to two previously uncovered corners of phase space: TeV scale for gluon jet JES and strange-flavor for light quark jets. These new windows to flavor JES could be critical for finding the fundamental cause of bad flavor response modelling in simulation.
Our third result beyond state-of-the-art is an early internal demonstration of a workflow that would enable daily calibrations within 48h of collecting data, which we call JEC4Prompt. The level of automatisation and speed that this represents for JEC would be unprecedented, with Run 2 JEC analyses typically taking months and teams of 10-20 people. Most importantly, the monolithic software would crystallise best practices and document accumulated knowledge permanently in code.