Jets: From Energy Deposits to Physics Objects

As a collider physicist, I care a lot about jets. They are fascinating objects that cover the ATLAS and CMS detectors during LHC operation and make event displays look really cool (see Figure 1.) Unfortunately, as interesting as jets are, they’re also somewhat complicated and difficult to measure. A recent paper from the CMS Collaboration details exactly how we reconstruct, simulate, and calibrate these objects.

For the uninitiated, a jet is the experimental signature of quarks or gluons that emerge from a high energy particle collision. Since these colored Standard Model particles cannot exist on their own due to confinement, they cluster or ‘hadronize’ as they move through a detector. The result is a spray of particles coming from the interaction point. This spray can contain mesons, charged and neutral hadrons, basically anything that is colorless as per the rules of QCD.

So what does this mess actually look like in a detector? ATLAS and CMS are designed to absorb most of a jet’s energy by the end of the calorimeters. If the jet has charged constituents, there will also be an associated signal in the tracker. It is then the job of the reconstruction algorithm to combine these various signals into a single object that makes sense. This paper discusses two different reconstructed jet types: calo jets and particle-flow (PF) jets. Calo jets are built only from energy deposits in the calorimeter; since the resolution of the calorimeter gets worse with higher energies, this method can get bad quickly. PF jets, on the other hand, are reconstructed by linking energy clusters in the calorimeters with signals in the trackers to create a complete picture of the object at the individual particle level. PF jets generally enjoy better momentum and spatial resolutions, especially at low energies (see Figure 2).

Once reconstruction is done, we have a set of objects that we can now call jets. But we don’t want to keep all of them for real physics. Any given event will have a large number of pile up jets, which come from softer collisions between other protons in a bunch (in time), or leftover calorimeter signals from the previous bunch crossing (out of time). Being able to identify and subtract pile up considerably enhances our ability to calibrate the deposits that we know came from good physics objects. In this paper CMS reports a pile up reconstruction and identification efficiency of nearly 100% for hard scattering events, and they estimate that each jet energy is enhanced by about 10 GeV due to pileup alone.

Once the pile up is corrected, the overall jet energy correction (JEC) is determined via detector response simulation. The simulation is necessary to simulate how the initial quarks and gluons fragment, and the way in which those subsequent partons shower in the calorimeters. This correction is dependent on jet momentum (since the calorimeter resolution is as well), and jet pseudorapidity (different areas of the detector are made of different materials or have different total thickness.) Figure 3 shows the overall correction factors for several different jet radius R values.

Finally, we turn to data as a final check on how well these calibrations went. An example of such a check is the tag and probe method with dijet events. Here, we take a good clean event with two back-to-back jets, and ask for one low eta jet for a ‘tag’ jet. The other ‘probe’ jet, at arbitrary eta, is then measured using the previously derived corrections. If the resulting pT is close to the pT of the tag jet, we know the calibration was solid (this also gives us info on how calibrations perform as a function of eta.) A similar method known as pT balancing can be done with a single jet back to back with an easily reconstructed object, such as a Z boson or a photon.

This is really a bare bones outline of how jet calibration is done. In real life, there are systematic uncertainties, jet flavor dependence, correlations; the list goes on. But the entire procedure works remarkably well given the complexity of the task. Ultimately CMS reports a jet energy uncertainty of 3% for most physics analysis jets, and as low as 0.32% for some jets—a new benchmark for hadron colliders!

Further Reading:

1. “Jets: The Manifestation of Quarks and Gluons.” Of Particular Significance, Matt Strassler.
2. “Commissioning of the Particle-flow Event Reconstruction with the first LHC collisions recorded in the CMS detector.” The CMS Collaboration, CMS PAS PFT-10-001.
3. “Determination of jet energy calibrations and transverse momentum resolution in CMS.” The CMS Collaboration, 2011 JINST 6 P11002.

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