1. Missing ET resolution Aim:
Stephanie Beauceron
Gregorio Bernardi
LPNHE - Paris
Aim:
Study the missing ET resolution with 3 zero-suppression 1.5, 2 and 2.5
Outline:
Selection of pure Zero Bias sample
Missing ET resolution on positive energy cells
Missing ET resolution on negative energy cells
Missing ET resolution on all energy cells
Data file:
p11.11 run taken/reconstructed at 1.5 online
Notice:
Missing ET is calculated without Coarse Hadronic (Layer 15,16,17).
Zero suppression is made offline using official noise file.

2. Selecting Pure Zero Bias Events
Occupancy for min bias
In a trigger selection of zero bias events, in the zero bias event, one third of them are indeed minimum bias events.
In case of the missing ET studies, a pure sample of zero bias (noise) is required.
Looking at the occupancy of minimum bias events, this kind of event gets a higher occupancy at high ieta.

3. Minimum and Zero Bias Events
In a minimum bias events, the distribution of the occupancy for cells at ieta greater than 26 or 28 have a significant shape compare to the same distribution in zero bias events.
Cut all events with an occupancy higher than 50 at abs(ieta)>=28
2311 events1397 events
The selection reduces the sample by 39.5%
Estimated contamination of 40 minimum bias events
Minimum Bias Events
Zero Bias Events

4. Met vs Set Dependence
The Met versus Set dependence in the zero bias sample has a slope “between” those of the pure zero bias and the minimum bias sample.
Pure zero bias
Minimum bias
2.5 
Every Set bin needs at least 50 entries to be considered in the fit.
Zero bias

5. Missing ET vs Set Dependence From Positive Energy Cells
Calculated on cells with positive energy cells:
If (Ecell>0)_
MEx=  px.cos()
MEy=  py.sin()
MET= (MEx2+MEy2)
SET=  pT
As we are looking at the variation of Met as a function of Set or Set, so we can try to fit Met as a function like :
p0 + p1 x Set + p2 x Set
on pure zero bias, minimum bias and QCD events

6. Missing ET vs Set Dependence in Pure Zero Bias Events
1.5
2.0
Fit Met as a function like:
p0 + p1 x Set + p2 x Set
2.5
1.5
2.0
2.5
Met increase faster with the fit by the polynomial function than with the linear function of Set.

7. Missing ET vs Set Dependence in Minimum Bias Events
Fit with:
p0 + p1 x Set + p2 x Set
1.5
2.0
2.5
1.5
At 2.5, p2 parameter becomes positive for minimum bias events and Met has a dependence more like Set than Set.
2.0
2.5

8. Missing ET vs Set Dependence in QCD Events
Fit with:
p0 + p1 x Set + p2 x Set
1.5
2.0
On QCD events, when we have physics, the Met vs Set dependence is mostly as p2 x Set which corresponds to what we see in minimum bias events at 2.5.
2.5
1.5
2.0
2.5

9. Missing ET From Negative Energy Cells
Calculated on cells with negative energy cells:
If (Ecell<0)
MEx=  -px.cos()
MEy=  -py.sin()
MET= (MEx2+MEy2)
SET=  -pT
Negative sign is just a convention to ease the
comparison with the positive energy cells
As we are looking at the linear dependance Met as a function of Set on pure zero bias, minimum bias events

10. Missing ET From Negative Energy Cells in Pure Zero Bias Events
1.5
2.0
Parameters from the linear fit:
2.5
1.5
No variation of the slope for different zero suppression.
2.0
2.5

11. Missing ET From Negative Energy Cells in Minimum Bias Events
Parameters from the linear fit:
1.5
2.0
Comparison of slopes values:
With a typical error of on the slope.
2.5
1.5
 Same behavior of the noise in minimum bias and in zero bias events.  independence of the zero suppression  gaussian behavior of the noise.
2.0
2.5

12. Missing ET From All Cells
Calculated on cells with all cells:
MEx=  px.cos()
MEy=  py.sin()
MET= (MEx2+MEy2)
SET=  |pT|
MEx and MEy have the “compensation” from the negative energy cells but SET is the absolute energy for all cells
Look at the pure zero bias sample

13. Missing ET/Scalar ET From All Cells in Pure Zero Bias Events
The slope of Met/Set versus Set is constant and zero-suppr. independent… This slope is the same if we look at Met on positive cells at 2.5
 No large influence of negative energy cells at 2.5
1.5
2.0
2.5
1.5
2.5
2.0
2.5
Positive Cells

14. Missing ET vs Scalar ET From All Cells in Pure Zero Bias Events
At 1.5, in the linear extrapolation, Met is equal to zero when Set=130 GeV.
At 2.0, Met is equal to zero when Set=50GeV.
At 2.5, Met is equal to zero when Set is equal to zero…
 2.5 displays a linear behaviour down to low value of Set
1.5
2.0
2.5
1.5
2.0
2.5

15. Conclusion  All this is in a D0 Note 4119
A simple topological way of selecting a pure sample of zero bias events has been presented. This sample has been used to study the missing ET dependence of the noise in zero suppression and a comparison with minimum bias events has been done.
The fit of the missing ET by “a + b x Set + c x Set” gives some interesting results: at 1.5 and 2.0, in minimum bias events, the noise hides the small physics signal but it is visible at 2.5.
The Met vs Set dependence for negative energy is independent of the zero offline suppression and has a gaussian behavior.
There is no evidence for influence of negative energy cells so the calculation can be performed on positive cells only.
 All this is in a D0 Note 4119