Cluster Tower Distribution Study

Define a new variable, N(live), which we expect to discriminate between jets and di-taus. The definition is as follows:

  • Extrapolate tracks in the track/pi0 cluster to the calorimeter
  • Count the number of towers that have a track from the cluster extrapolating to the OR a pi0 identified in them AND have total energy > 100 MeV. This number is N(live).

Below is the plot of N(live):

Nlive

The signal sample has more probability to have 1-3 towers live compared to W+jets. ttbar tends to have more “live” towers in the cluster than the other samples.

Next we look in each bin where the Higgs signal is dominant, N(live)=1,2,3

daDefine a new variable, N(live), which we expect to discriminate between jets and di-taus. The definition is as follows:

  • Extrapolate tracks in the track/pi0 cluster to the calorimeter
  • Count the number of towers that have a track from the cluster extrapolating to the OR a pi0 identified in them AND have total energy > 100 MeV. This number is N(live).

Below is the plot of N(live):

Nlive

The signal sample has more probability to have 1-3 towers live compared to W+jets. ttbar tends to have more “live” towers in the cluster than the other samples.

Next we look in each bin where the Higgs signal is dominant, N(live)=1,2,3

NtrkvNpi0_Nlive1
NtrkvNpi0_Nlive2
NtrkvNpi0_Nlive3

It looks like we can cut clusters have large number of tracks and pi0s in all the above cases. The cut should depend on the N(live) multiplicity.