1. Alberto A. Colavita, INFN, TS
Compass Upgrade proposal for the RICH1 electronics. A simple and rapidly executable way of partially getting rid of Halo events, using correlation functions, as if they were noise.
Alberto A. Colavita, INFN, TS
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2. Showing the “noise” in the central region of the RICH1.
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7. Schematic view of the classical architecture of the presently installed front-end
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8. The shape of a “normal” signal of an event.
An artist’s rendition.
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9. Halo event, starting at t0 < 0
An artist’s rendition.
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10. Halo event starting at 1 ms > t0 >0
An artist’s rendition.
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11. What separates Halo events from good events ?
The only discriminating parameter is the “time history” of the signal.
We need to memorize the past, before the trigger, and part of the future after the trigger.
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12. What it takes to Upgrade the Four Half Chambers
60 BORAs comprising ~ pixels (chamber pads).
To save the history of the signal we need to buffer at least ~ 1024 samples per signal/channel. If we samples every ~ 100 ns we preserve the last 102 ms of the signal.
We need processing power in order to identify the Halo events from good events.
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13. To see the time history of the signal I need a new version of the Gassiplex front-end chip and of the general architecture !!!! Because we have little time to waste, we introduced simple modifications in order to meet IMEC’s September 15th prototyping run.
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14. New Version of Gassiplex (a)
The new chip allows us to track simultaneously all 16 analog channels.
The new Gassiplex can be described as a 16_IN – 16_OUT front-end chip.
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15. New Version of Gassiplex (b)
The overall final gain is 5 times larger than in the present Gassiplex in order to allow a larger range of ADC division to be used.
The peaking time was reduced to 500 ns in order to have the best possible S/N ratio and to increase the time resolution of the front-end.
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16. Why 8-bits are enough
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17. The new architecture
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19. Strategy for local data treatment
Sample every ~100 ns, place the two 8-bit results into a circular buffer of at least 1024 bytes/channel deep (storing ~100 ms of the signal). The BF531 DSP handles circular buffers using internal hardware. The DSP uses DMA to retrieve the samples from the ADCs, freeing core cycles.
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20. Algorithmic Procedure (a)
When the trigger arrives, read the “write- pointer” of the circular buffer. Calculate the correlation of the experimental points with the points describing the “pure” signal. The calculation starts from the oldest sample, in order to eventually wait for the future samples to arrive to the buffer.
Find the peak of the correlations discriminating if it is large enough and in the right position in time.
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21. Algorithmic Procedure (b)
If the peak is at the right (correct) place in time, fit the pure signal to the data points in order to find a better peak value than that furnished by the ADC.
Then, write the event in an output buffer together with the channel ID.
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22. Showing the procedure
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23. The procedure behind the simulations
We generate a Gaussian-noise background every time we need it.
We have a “pure” signal described by a set of values {xi} of peak height = 1. In general the height is denoted by h.
We calculate the correlation between the pure signal and the noise background. The sigma of the correlation is used to evaluate the visibility of a buried signal.
We define S/N = h / snoise
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27. Correlation of the “pure” signal with a signal buried in noise.
The new detection method consist in comparing the peak of correlation between “pure” and buried signal with the sigma of the correlation between the “pure” signal and the random Gaussian-noise background.
For example: is the peak larger than 2.5 or 3 * sigma (pure – Gaussian noise) ?
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31. Simulations
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32. How do we detect a buried signal with the new correlation method ?
We compute the correlation with a “pure” signal. We then locate the peak and ask is the peak is located in the 3 points centered around the proper peaking time. Then, we ask is the peak high enough?
If YES to both questions we have a signal in time and contrast.
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33. How do we detect a buried signal with the classical correlation method ?
I bury a “pure” signal in noise with a certain S/N ratio.
We look at the value of the buried signal at the time it should peak.
We ask if the value at peaking time is larger than, let’s say, 2.5 sigma-noise.
If YES to the question we have a signal in “contrast”.
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34. Signal starts at t=0, then maximum is at the correct peaking time.
correlation
0 %
0 %
0.6 %
0.6%
Rich_now
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35. Sensitive of the correlation method with respect to Halo (off-time) signals.
We add a “pure” signal, with a give S/N, to the Gaussian-noise starting at time t0 ( – 1000 ns).
We ask if the peak of the correlation is at the right position and if the correlation is large enough.
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36. Sensitivity of the classical method with respect to Halo signals.
We add a “pure” signal, with a give S/N, to the Gaussian-noise starting at time t0 (from to ns).
We analyze the sample at the “proper” peaking time of the buried signal.
Decision is always the same, is the signal larger than 2.5 sigma-noise.
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37. 0 %
0.6 %
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38. 23 %
1.2% 2%
~ 100 ns
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39. Fitting the “pure” to the experimental values.
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41. Precision Improvement obtained by fitting
38 %
13%
Not enough statistics
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42. Counting Cycles of the DSPs
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43. Total cycle count: correlation and fitting.
2319 cycles, add 50% just in case for
A safe total of 3500 cycles
= 1.7 ns x 3500 = 5.95 ms ( 600 MHz, 25 U$ DSP)
= 2.5 ns x 3500 = 8.75 ms (400 MHz, 8 U$ DSP)
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44. Status
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45. New Gassiplex to be delivered end of November, beginning of December.
Algorithm almost finished.
Front-tend control software, finished.
Schematic 15 days to be finished.
Layout finished 30%.
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47. Schematic
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