Proton Calorimetry/Equipment/QuARC General Information - Revision history

SoniaEscribano: /* QuARC Information Summary */

2024-01-12T10:54:12Z

QuARC Information Summary

← Older revision		Revision as of 10:54, 12 January 2024
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	./livePlot num_DDCboards FSR yScale revB/C reversetrue/false (clean folder background.txt backST.txt frontST.txt ...)		./livePlot num_DDCboards FSR yScale revB/C reversetrue/false (clean folder background.txt backST.txt frontST.txt ...)


	~~'''5. Data acquisition'''~~

	Running FTDI code the average time per read should be ~170us, giving a data transfer speed of ~0.47MB/s.		Running FTDI code the average time per read should be ~170us, giving a data transfer speed of ~0.47MB/s.
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	'''6. Data analysis'''		'''5. Data analysis'''

	Two different codes are used for the data analysis: replay.py and fit.cpp, their functionalities are described below.		Two different codes are used for the data analysis: replay.py and fit.cpp, their functionalities are described below.

SoniaEscribano: /* QuARC Information Summary */

2023-09-27T15:31:56Z

QuARC Information Summary

← Older revision		Revision as of 15:31, 27 September 2023
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	To show these data in the GUI a shell script that iterates through the lines of the fitted output file is necessary.		To show these data in the GUI a shell script that iterates through the lines of the fitted output file is necessary.

	Previously the codes were		Previously both of the codes were developed in cpp. The replay.cpp replays the DDC232 acquisition by either stepping through each measurement or averaging measurements at specified frame rate, while fit.cpp performs the fit on calibrated and background subtracted data.

	'''Questions'''		'''Questions'''

SoniaEscribano: /* QuARC Information Summary */

2023-09-27T15:16:53Z

QuARC Information Summary

← Older revision		Revision as of 15:16, 27 September 2023
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	Two different codes are used for the data analysis: replay.py and fit.cpp, their functionalities are described below.		Two different codes are used for the data analysis: replay.py and fit.cpp, their functionalities are described below.

	replay.py code calibrates and subtracts the background of each measurement, generating an output text file with header information, all the individual calibrated measurements and the average of them all. An example file with data from Clatterbridge Hospital of what the generated file looks like can be found here [https://www.hep.ucl.ac.uk/pbt/wikiData/data/GUI-Clatterbridge/Run001_calibrated.txt]. To run this code we need the following arguments:		replay.py code calibrates and subtracts the background of each measurement, generating an output text file with header information, all the individual calibrated measurements and the average of them all. An example file with data from Clatterbridge Hospital of what the generated file looks like can be found here [https://www.hep.ucl.ac.uk/pbt/wikiData/data/GUI-Clatterbridge/Run001_calibrated.txt], every individual run fit takes ~3.3s on a MacBook to be completed. All of them can be processed together by running the shell script ./replayshell.sh. To run this code we need the following arguments:
	python3 replay.py t_INT framerate skipahead reverse folder run background backST frontST		python3 replay.py t_INT framerate skipahead reverse folder run background backST frontST

	fit.cpp averages the data at a user specified frequency (or generic 25 Hz) and then performs Bortfeld and Kelleter fits to the data. After the fitting, the beam energy and range are extracted for each of those averaged measurements. An example of what the output of fit.cpp looks like can be found here []. This code needs to be compiled, and then run with the appropriate arguments.		fit.cpp averages the data at a user specified frequency (or generic 25 Hz) and then performs Bortfeld and Kelleter fits to the data. After the fitting, the beam energy and range are extracted for each of those averaged measurements. All of them can be processed together by running the shell script ./fitshell.sh An example of what the output of fit.cpp looks like can be found here [https://www.hep.ucl.ac.uk/pbt/wikiData/data/GUI-Clatterbridge/Run001_fitted_25Hz.txt]. This code needs to be compiled, and then run with the appropriate arguments.
	g++ -o fit fit.cpp `root-config --cflags --glibs` -O3		g++ -o fit fit.cpp `root-config --cflags --glibs` -O3
	./fit reverse energy facility folder run (frequency)		./fit reverse energy facility folder run (frequency)

SoniaEscribano: /* QuARC Information Summary */

2023-09-27T15:11:23Z

QuARC Information Summary

← Older revision		Revision as of 15:11, 27 September 2023
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	'''6. Data analysis'''		'''6. Data analysis'''

			Two different codes are used for the data analysis: replay.py and fit.cpp, their functionalities are described below.

			replay.py code calibrates and subtracts the background of each measurement, generating an output text file with header information, all the individual calibrated measurements and the average of them all. An example file with data from Clatterbridge Hospital of what the generated file looks like can be found here [https://www.hep.ucl.ac.uk/pbt/wikiData/data/GUI-Clatterbridge/Run001_calibrated.txt]. To run this code we need the following arguments:
			python3 replay.py t_INT framerate skipahead reverse folder run background backST frontST

			fit.cpp averages the data at a user specified frequency (or generic 25 Hz) and then performs Bortfeld and Kelleter fits to the data. After the fitting, the beam energy and range are extracted for each of those averaged measurements. An example of what the output of fit.cpp looks like can be found here []. This code needs to be compiled, and then run with the appropriate arguments.
			g++ -o fit fit.cpp `root-config --cflags --glibs` -O3
			./fit reverse energy facility folder run (frequency)

			To show these data in the GUI a shell script that iterates through the lines of the fitted output file is necessary.

			Previously the codes were

	'''Questions'''		'''Questions'''

SoniaEscribano: Created page with "== QuARC Information Summary == '''1. Scintillators''' The main properties of our scintillators are: * ρ = (1.03±0.01)g cm-3 * decay constant = 2.5 ns * maximum emission at 4..."

2023-09-27T14:51:55Z

Created page with "== QuARC Information Summary == '''1. Scintillators''' The main properties of our scintillators are: * ρ = (1.03±0.01)g cm-3 * decay constant = 2.5 ns * maximum emission at 4..."

New page

== QuARC Information Summary ==

'''1. Scintillators'''

The main properties of our scintillators are:
* ρ = (1.03±0.01)g cm-3
* decay constant = 2.5 ns
* maximum emission at 425 nm (blue-violet)
* light output = 56% of anthracene
* n = 1.57

In total we have 302 scintillator sheets. Their production mechanisms and average thicknesses are summarised in this file []. The order in which the scintillators are assembled in each stack is available in the GUI, inside every experimental run log.
* Each sheet thickness is measured in 8 different points of the scintillator: 4 corners, and 4 middle parts of each side.
* The uncertainty is simply calculated as the standard deviation.
* For the scintillator stacks used in Manchester and PARTREC we measured the thickness in 3 points.
* Clear polished scintillators (machine block) offer the best light output.

'''2. Photodiodes'''

The first prototype of the QuARC had a CMOS sensor, but it was then upgraded to photodiodes coupled to each sheet individually. There is a direct coupling, so no optical gel is used.
* Model = hamamatsu S12915-16R
* Effective photosensitive area = 6.0 mm2
* Spectral response wavelength between 340-1100 nm
* Quantum efficiency peaks at 960 nm

'''3. Boards: DDC232'''

We have two different types of boards (revB and revC) that differ in the number of PD and the space between them. They are directly coupled to the PD, and need a power cable and a PMOD connection that goes to the FPGA.
* revB: 16 photodiodes, spaced 2.86 mm. Uses 12V power cable.
* revC: 32 photodiodes, spaced 3.0 mm. Uses 7V power cable -> revD is a modification of revC, with a change in the heat management.

'''4. FPGA'''

The FPGA is used to read the boards and transfer the data to the PC. The FPGA automatically loads the last code designed, but if not we can load it with Vivado: the code used is FTDI.h, which uses libFTDI libraries so they need to be installed in the laptop. We have 2 different boards:
* Nexys board:
* usb104: powered via USB and connected to the board with a PMOD cable.

If we want to run the DAQ, first we have to compile the code as follows:
MacBook: g++ -L/opt/local/lib -I/opt/local/include/libftdi1 -lftdi1 -std=c++17 -O3 FTDI.cpp -o FTDI
Nuc PC: g++ -L/usr/lib64 -I/usr/include/libftdi1 -lftdi1 -std=c++17 -O3 FTDI.cpp -o FTDI

We can then run the code with the adequate parameters:
./FTDI nexys/usb104 num_DDCports FSR t_INT(us) nmeasurements outputfile board (testmode)

We can also visualise "live" (reads from average.csv) using the livePlot code.
g++ -o livePlot livePlot.cpp `root-config --cflags --glibs` -O3

./livePlot num_DDCboards FSR yScale revB/C reversetrue/false (clean folder background.txt backST.txt frontST.txt ...)

'''5. Data acquisition'''

Running FTDI code the average time per read should be ~170us, giving a data transfer speed of ~0.47MB/s.
* We acquire data using FTDI.cpp code, changing the output file to Run001.txt, Run002.txt and so on.
* If we want to acquire data and show it live we need to use progh.sh
./prog.sh nexys/usb104 4 350 0 170 100000 50 true capture.txt revB/C true/false (fit/test/clean) (folder background.txt backST.txt frontST.txt ...)

'''6. Data analysis'''

'''Questions'''
* Boards: What needs to be done to them? Vivado codes into FPGA but boards? Daisy chaining? What was done in terms of usbC connection?
* FPGA: no longer using nexys? What issues did we have with both of them?
* Experiment: just use FTDI code changing the run number? where are these runs saved? Possible errors during data acquisition? test mode?
* Data analysis: parameters for fit based on what?