CMSDAS@IIT Hyderabad 2025: Jets and MET Short Exercise: Jet Resources

Jet is a physical object representing hadronic showers interacting with our detectors. A jet is usually associated with the physical representation of quark and gluons, but they can be more than that depending of their origin and the algorithm used to define them.

A jet is defined by its reclustering algorithm and its constituents. In current experiments, jets are reclusted using the anti-kt algorithm. Depending on their constituents, in CMS, we called jets reclustered from genparticles as GenJets, calorimeter clusters as CaloJets, and particle flow candidates as PFJets.

We call pileup to the amount of other processes not coming from the main interaction point. We must mitigates its effects to reduce the amount of noise in our events.

Many event variables help us to learn how different pileup was during the data taking period, compared to the pileup that we use in our simulations. The pileup reweighting procedure help us to calibrate the pileup profile in our simulations.

The so-called jetID is the basic jet quality criteria to remove fake jets.

The energy of jets in data and simulations is different, for many reasons, and in CMS we calibrate them in a series of steps.

Jets are stochastic objects which its content fluctuates a lot. We measure the jet energy resolution to mitigate this effects.

Jet substructure is the field study the internakl structure of high pt jets, usually clustered with a bigger jet radius (AK8).

Grooming algorithms like softdrop, and substructure variables like the nsubjettiness ratio help us to identify the origin of these jets.

Over the years more state-of-the-art taggers involving ML have been implemented in CMS. Those help us indentify more effectively boosted jets.

Weakly interacting neutral particles produced in proton-proton (pp) collisions at the LHC traverse the CMS detector unobserved.

Their presence is inferred from the measurable momentum imbalance in the plane perpendicular to the beam direction when produced alongside electromagnetically charged or neutral particles. This measurable transverse momentum imbalance is referred to as missing transverse momentum (MET).

Precise determination of MET is critical for Standard Model measurements involving final states with neutrinos and searches for physics beyond the SM targeting new weakly interacting particles.

MET reconstruction is sensitive to experimental resolutions and mis-measurements of reconstructed particles, detector artifacts, and the effects of additional pp interactions within the same or nearby bunch crossings (pileup).

Inaccurate MET estimation can result from sources such as non-linearity in the calorimeter’s response to hadrons, minimum energy thresholds in the calorimeters, and pT thresholds or inefficiencies in track reconstruction. These issues are mitigated through calibration procedures discussed in this exercise.

Type-1 MET is the default MET calibration recommended by CMS.

Type-1 smear MET enhances data-MC agreement, and JME POG advises analysts to assess its impact in their studies.

MET is influenced by uncertainties from all contributing objects, including jets, leptons, photons, and unclustered energy. Systematic variations in the scale and resolution of each component must be propagated to the MET calculation to evaluate their impact on the analysis.

The performance of MET is studied in events with a well-measured Z boson (decaying to electrons or muons) or an isolated photon, which should have little to no genuine MET.

Transverse momentum conservation is used to study MET response and resolution along z-axis.

Large MET in an event may be caused by detector noise, cosmic rays, and beam-halo particles. Such MET with uninteresting origins is called false MET, anomalous MET, or fake MET and can be an indication of problematic event reconstruction.

Events with anomalos mets can be rejected using the Noisy event filters.