Development of a Liquid Scintillator and of Data ... - Borexino - Infn

5 Particle Identification with a Neural Network
5.3.2 Data from CTF2
I tested the neural network also with real CTF data to see if in the case of real, more noisy
spectra the NN would be superior to the tail-to-total method. Not for all CTF2 runs flash ADC
data are available. To have a sufficiently high statistics for the training of the net, I chose
one of the source runs, run 793. In this run, all the Bi and Po events occurred at the same
position, i.e. the center of the Inner Vessel. In this case, the pulse shape is not further broadened
by the time of flight differences to the different PMTs, so that the pulse shape discrimination
should yield better results than for randomly distributed events. Fig. 5.9 shows a typical Bi
beta and a Po alpha pulse.
A total sample of 2360 events was available from run 793. The sample was too small to reduce
it further by restricting the energy of the Bi events to the same energy range as the Po
events, which would have been the case for only events. Giving the energy of the event
as input parameter for the NN could lead to the unwanted case, that the NN uses the energy
as decision criterium: high energy beta events, low energy alpha events. Hence I took as input
parameter only the normalized flash ADC spectrum (100 bins = 250 ns). 1000 events were
chosen as training set for the NN. The other 1360 events are the validation set.
The training process continued 5000 epochs, until the error function became flat. The results
of the NN algorithm are shown in fig. 5.10. From 680 beta events only 26 were misidentified
as alpha events. From 680 alpha events 22 were misidentified as beta events. This means, that
for 96.2 % beta identification efficiency, 96.7 % of the alpha events are rejected.
For a comparison I tested the pulse shape discrimination using the tail-to-total ratio (with the
tail starting after 48 ns) on the same data sample. For a beta identification efficiency of 96 %,
an alpha rejection efficiency of 84.9 % is obtained (see fig. 5.11).
In this case, the neural network is clearly better than the simple tail-to-total ratio. These results
are encouraging for BOREXINO, but data samples with a higher statistics and further
systematic tests are needed.
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