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Noise Analysis in NaIAD
Matthew Robinson
Experiment now running reliably therefore time to work on getting the best from the analysis
Experiment now running reliably therefore time to work on getting the best from the analysis
Noise ExperimentMeasurements taken with and without light guides in place.
Taken by Phil and Mike in glove box @ sheffield
Measurements taken with and without light guides in place.
Taken by Phil and Mike in glove box @ sheffield
Anomalous Events
Looks Like Scintillation but isn’t.
Many cut by asymmetry but also many left behind
Looks Like Scintillation but isn’t.
Many cut by asymmetry but also many left behind
Time Constant Distribution
Distribution with light guides
@ 4-6 keV
Energy calibration not possible, typical values used:
20 mV/keV (sum)
Poor fit to power law as shown,
Terrible fit to exponential,
Work on cuts to identify scintillation like noise events in data
Distribution with light guides
@ 4-6 keV
Energy calibration not possible, typical values used:
20 mV/keV (sum)
Poor fit to power law as shown,
Terrible fit to exponential,
Work on cuts to identify scintillation like noise events in data
Time Constant Distribution
Distribution without light guides.
Appears similar, now collecting more data for comparison
Distribution without light guides.
Appears similar, now collecting more data for comparison
Identification of Noise EventsMeasurement
Measurement Region
Now measured from fitted start of event to end of data rather than from 0 ns
Now measured from fitted start of event to end of data rather than from 0 ns
Identification of Noise EventsMeasurement
2
2
)(
end
tt datastart
datafit
data 1d.o.f
Set event by event to achieve
Uncertainty for each fit proportional to amplitude of event. This achieves a reasonable per degree of freedom value for chi squared
Uncertainty for each fit proportional to amplitude of event. This achieves a reasonable per degree of freedom value for chi squared
Identification of Noise Events
short Measurement
Measurement Region
Measured similarly to normal chi squared except over first 1000 ns of event.
Flat part of event contains few photons and is therefore less important
Measured similarly to normal chi squared except over first 1000 ns of event.
Flat part of event contains few photons and is therefore less important
Identification of Noise Events
short Measurement
21000
2
)(
nst
tt data
start
start
datafit
data As for
Identification of Noise EventsSteppiness Measurement
Measurement Region
Measurement of tendency to be made up of large steps.
Measurement of tendency to be made up of large steps.
Identification of Noise EventsSteppiness Measurement
N
n
n
pen pe
pe2
sumin termsofnumber
ronsphotoelect ofnumber expected
ronsphotoelect ofnumber
N
n
n
pe
pe
Expected number of photoelectrons calculated using differential of fit at that point.
Steps found by searching through event one time bin at a time and counting the number of photons in the next 30 ns.
Expected number of photoelectrons calculated using differential of fit at that point.
Steps found by searching through event one time bin at a time and counting the number of photons in the next 30 ns.
Identification of Noise EventsAfter-pulses and Jitter
Measurement Region
Jitter originally included to measure digitisation noise, now used to spot after-pulses which tend to occur in this region.
Jitter originally included to measure digitisation noise, now used to spot after-pulses which tend to occur in this region.
Identification of Noise EventsJitter Measurement
2
10002
)(
end
nstt datastart
datafit
data As for
Basically a chi squared measured over what should be the flat part of the data.
If the value is significant, an attempt can be made to fit just the early part of the data.
Basically a chi squared measured over what should be the flat part of the data.
If the value is significant, an attempt can be made to fit just the early part of the data.
Identification of Noise EventsLimited Time Fit
Alternatively, events with after-pulses may be cut completely by cutting on the jitter parameter
Alternatively, events with after-pulses may be cut completely by cutting on the jitter parameter
Comparison of Cuts
Uncut Noise
Far too many events with time const >100 ns
Far too many events with time const >100 ns
Comparison of Cuts
Noise Cut by Asymmetry
Long time constant events reduced but not eliminated
Asymmetry in time const. < 100 ns,
Asymmetry in start time < 100 ns.
Asymmetry in energy < 40%
Long time constant events reduced but not eliminated
Asymmetry in time const. < 100 ns,
Asymmetry in start time < 100 ns.
Asymmetry in energy < 40%
Comparison of CutsNoise Cut by
short
Long time constant events reduced but not eliminated
Asymmetry in time const. < 100 ns,
Asymmetry in start time < 100 ns.
Asymmetry in energy < 40%
Long time constant events reduced but not eliminated
Asymmetry in time const. < 100 ns,
Asymmetry in start time < 100 ns.
Asymmetry in energy < 40%
Comparison of Cuts
Noise Cut by Steppiness
Long time constant events reduced but not eliminated
Asymmetry in time const. < 100 ns,
Asymmetry in start time < 100 ns.
Asymmetry in energy < 40%
Long time constant events reduced but not eliminated
Asymmetry in time const. < 100 ns,
Asymmetry in start time < 100 ns.
Asymmetry in energy < 40%
Comparison of Cuts
Noise Cut by Identification
Cuts optimised to achieve same photopeak size in data (later slide)
Chi squared/d.o.f <3 all channels,
Short chi squared/dof <2 (sum) <2.5 channels,
Steppiness<4 (sum) <5 (channels)
Much better reduction of noise
Cuts optimised to achieve same photopeak size in data (later slide)
Chi squared/d.o.f <3 all channels,
Short chi squared/dof <2 (sum) <2.5 channels,
Steppiness<4 (sum) <5 (channels)
Much better reduction of noise
Comparison of Cuts
Uncut Data
Not really possible to use data in this condition.
Not really possible to use data in this condition.
Comparison of Cuts
Data Cut by Asymmetry
Asymmetry cuts have greatest effect on fast noise, which we don’t care about.
Asymmetry in time const. < 100 ns,
Asymmetry in start time < 100 ns.
Asymmetry in energy < 40%
Asymmetry cuts have greatest effect on fast noise, which we don’t care about.
Asymmetry in time const. < 100 ns,
Asymmetry in start time < 100 ns.
Asymmetry in energy < 40%
Comparison of Cuts
Data Cut by Identification
Fit to asymmetry cut tcd shown here for comparison.
Identification cuts have little effect on fast noise, which make it easier to use in fitting of noise.
Chi squared cuts tend to increase mean time constant, steppiness cuts have opposite effect. This can be used to persuade data and compton distributions to match.
Probably decide to use both identification and asymmetry, have to do more work on optimising cuts.
Chi squared/d.o.f <3 all channels,
Short chi squared/dof <2 (sum) <2.5 channels,
Steppiness<4 (sum) <5 (channels)
Much better reduction of noise
Fit to asymmetry cut tcd shown here for comparison.
Identification cuts have little effect on fast noise, which make it easier to use in fitting of noise.
Chi squared cuts tend to increase mean time constant, steppiness cuts have opposite effect. This can be used to persuade data and compton distributions to match.
Probably decide to use both identification and asymmetry, have to do more work on optimising cuts.
Chi squared/d.o.f <3 all channels,
Short chi squared/dof <2 (sum) <2.5 channels,
Steppiness<4 (sum) <5 (channels)
Much better reduction of noise
