1. Noise Analysis in NaIAD Matthew Robinson Experiment now running reliably therefore time to work on getting the best from the analysis

2. Noise Experiment Measurements 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

3. 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

4. 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

5. 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

6. Identification of Noise Events    Measurement Measurement Region Now measured from fitted start of event to end of data rather than from 0 ns

7. Identification of Noise Events    Measurement 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

8. 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

9. Identification of Noise Events   short  Measurement As for  

10. Identification of Noise Events Steppiness Measurement Measurement Region Measurement of tendency to be made up of large steps.

11. Identification of Noise Events Steppiness Measurement 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.

12. Identification of Noise Events After-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.

13. Identification of Noise Events Jitter Measurement 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.

14. Identification of Noise Events Limited Time Fit Alternatively, events with after-pulses may be cut completely by cutting on the jitter parameter

15. Comparison of Cuts Uncut Noise Far too many events with time const >100 ns

16. 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%

17. Comparison of Cuts Noise 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%

18. 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%

19. 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

20. Comparison of Cuts Uncut Data Not really possible to use data in this condition.

21. 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%

22. 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