Experiment Proposal - opera - Infn

Studies on the strategies have started only recently. The underlying idea consists of looking at the
sample of bricks which are close to the first predicted brick either in the same wall or in the downstream
wall(s). Because of the outputs of the wall finding neural network and to the transverse position of the
predicted vertex in the predicted brick, a probability can be associated to each brick of this sample.
Bricks are then removed starting from those which have the highest probability until the sum of the
probabilities of the removed bricks reaches a predefined threshold.
Fig. 81 shows the brick finding efficiency obtained with dynamic brick removal for DIS ν µ NC events
and DIS τ → e events, compared to what obtained with the static 2 bricks removal strategy and a function
of the average number of bricks removed. In the case of DIS ν µ NC events the efficiency obtained when 2
bricks are removed for all events (static removal strategy) is 77.6%. In order to reach the same efficiency
with a dynamic removal strategy, only 1.5 bricks are needed to be taken out on average.
Brick finding eff.
Brick finding eff.
0.85
0.825
0.8
0.775
0.75
0.725
0.7
0.675
0.65
0.625
0.6
1
0.98
0.96
0.94
0.92
0.9
0.88
0.86
0.84
0.82
0.8
1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6
DIS ν µ
NC events
1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6
DIS τ → e events
Figure 81: Brick finding efficiency with dynamic and static removal as a function of the average number
of removed bricks.
By combining the dynamic with the sequencial approaches the number of bricks to be removed per
event in order to obtain a given efficiency can be further reduced. Fig. 82 shows that the above 77.6%
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