Jet Charge at the LHC

 

Based on Phys.Rev.Lett. 101 (2013) 212001 (arXiv:1209.2421) by David Krohn, Tongyan Lin, Matthew D. Schwartz and Wouter Waalewijn.

In this paper, we showed that the charge of an underlying quark can be extracted from the distribution of charged particles in a jet. The key is to use a energy-weighted charged distribution:

Here is an example of the distribution of this variable for jets originating from different partons

Measuring the charge of a jet would be essential to determining the quantum numbers of new physics, should it be seen at the LHC.

This plot shows that jet charge can distinguish a hadronically decaying W-prime from a Z-prime at the LHC. With 50 events, we can get a 2 sigma distinction.

 

Even in the standard model, jet charge is interesting. For example, in dijet evens, at low pT, the energy fraction x for both partons is small. That is x1, x2 << 1. For small x, gluon PDFs dominate. At high pT, one of the x's can be large. Thus we can pull out a valence quark, which implies there should be quark-gluon final states. These plots, using truth level information confirm these observations

Truth-level composition of dijet charges

Correlations bewten charges of two partons for different energy jets.

Here 0 = anti-up, 1 = anti-down, 2= gluon, 3 = down, 4= up.

At low-pT, gluon PDFs, and gluon jets dominate. At high pT, the jets are dominantly up and gluon.

So, if we can measure jet charge, we can see the onset of the valence quarks in the PDFs. An observable based on this is simply the sum of the jet charges of the two jets in dijet events. Remarkably, less than 11 months after we proposed this, both CMS and ATLAS had made jet charge measurments. ATLAS' result is shown below

Expected value of total jet charge for dijet events in simulation, from our paper Measured value of dijet charge by ATLAS, from this note.

 

 

Finally, in this paper, we devised a way that the evolution of the average jet charge could be computed using pertubative QCD. An example prediction is shown here.

 

Our next step will be to try it out on data (once the unfolded data is made public).