Two analyses, look for a signal, meaure the fractions of
1/9 and 4/9 MIP events to 1 MIP events.
To look for the signal, need to see the "Bumps" in
uncorrected distributions of the average MIP signal
from the WITNESS counters. May need some cuts, but data is pretty
To quote the fractions, need 3 numbers from the DATA samples
These numbers could be the number of events seen in windows near +/- 0.1
MIPs around the 1/9, 4/9 and 1.0 bins.
- Number of 1/9 MIP events
- Number of 4/9 MIP events
- Number of 1 MIP events corrected for the single MIP trigger efficiency
To measure the integrated luminosity in single MIP events
Use the scalers which counted the T1&T2&T3&T4 counts.
As the cut off for the DATA events eats in to the MIP signal - we can
get the number of MIPs from the scalers. The T1&T2&T3&T4 scaler was
recorded per spill (and per run). This count is proportional to the number
of MIPs seen by the experiment. The constant of proportionality can be
measured for the MIP runs and applied to the DATA runs to measure the
number of MIPs seen per run.
We can also fold in the computer busy to this luminosity.
Simply plot the average of the WITNESS counters in MIPS for all the
data runs. There are no "bad events".
Do we need to define the fiducial volume, cut on the "hole" scintillator ?
(asking for a signal in each of the WITNESS counters is enough).
- All WITNESS scintillators must be > .05 MIPS (to remove noise)
whats the probablity of getting 0 for a MIP ? Counter efficiency is the
probability of a Poisson fluctuation of the number of photo-electrons
down to 0.
- Only take counters < 1.2 into the average- otherwisre the overflows
can be seen as 7 distinct bumps in the average plot.
|Total Number of DATA events||
|7 Witness Counters > 0.05 MIP||
|Average of WITNESS counters between 0.90 and 1.20||
Method to extract the Luminosity in MIPS
155,231 Events, Mean 1.038.
417,108 events, Mean=0.499
- Explain tail on MIPs plot - photon showers ?
- Explain bumps on DATA plot - detector posn dependent ?
- Most of the PASS events are from the 1st 2 runs, before the
detector was moved down 15'' from the beam line
- The last run was exceptionally clean - when detector
was furthest from the beam line
- Group the Data runs together to find where the bumps are coming from.
- Calculate # MIPs in MIPs plot
- Sum all values to get conversion factors - normalised MIPs
- Why arent the T1&T2&T3&T4 numbers the same as the W/O Busy numbers in the
MIPs runs ?
DATA runs for runs over 17
121,594 events, Mean=0.528
Removing the first 2 runs - when the detector was moved down, away
from the beam line, there are fewer events which pass the cuts
compared to the number of events in the runs.
The distribution now has fewer bumps in it - mainly a bump at 1MIP,
a central mound at 0.5 MIPs, and a small shoulder at .1 MIP.