The Motivaition

Event by Event Estimation is not so good when R is larger

Jet by Jet Estimation could be a better solution for larger R

Jet by Jet Estimation could be a better way to extent the range to lower pt for R=0.4

We can see that the Jet Reconstruction is not so good when R=0.4 due to

Pythia8 with toy model background

• Pythia8 Setting

Energy to be 2.76TeV

Pt hard bins ={5, 11, 21, 36, 57, 84, 117, 152, -1} GeV /c, 0.5M events per Pt hard bin.

Tune 4C

• Background Setting

The details of the method

The Local Method of background density Estimation

The Three jet Method

• main jet: the jet reconstructed with R=0.6 in each event.
• restricted jet: the leading subjet with R=0.4 in the main jet.
• excluded jet: the subejets with R=0.1 in the main jet and with a condition which have a distance larger than 0.1+0.4

and Then reconstruct the particles inside the 0.6 Jet with R=0.4 here we call the Leading 0.4 Jet inside the main jet as restricted jet

finally estimated the

The donut method

We choose a r=0.4 jet in hybrid event ,then ,we choose the area around the jet which is like a donut, the donut area is defined as 0.4<R<0.6

R is the distance to the jet. We ge the background density and the results show that the donut method have a good mean value to the jet truth background but with a worse mean value compare to event rho.

Increase the pt cut

The different pt cut method

we use the different pt cut on background particles

Results and Conclusions

The donut method results

Event median means get the background density by default event by event median rho estimation

The estimated area truth suggested by a assumption about the donut area bkg density is similar to jet bkg density, and now I only select the bkg particles in the donut area and calculated the pt density.

The jet area method is calculate the pt density in donut area without considering its a pythia or bkg particles.

The jet anti-kt/kt is calculated by getting the median rho from the small jet in(near) the donut area.

pythia + bkg with a pt cut ( > 0.15 GeV /c) and a area cut (0.6*R*R)

The different pt cut method results

In the frist plot, It shows the jet spectrum in different conditions, the black one is the jet which reconstructed in pythia8, the Vacuum + pt,bkg > x GeV /c, it means we use the pythia8 particles with no pt cut and the background particles have a pt cut with larger than x GeV /c.

in the second plot, it shows the jet spectrum ratio with log scale on Y axis.

In this two plots, we show the delta pt mean value and distribution with different transverse momentum cut on background particles.

In this two plots, we show the area mean value and distribution with different transverse momentum cut on background particles