Experiment Proposal - opera - Infn

#1 #2 #3 #4 #5
γ
γ
∆θ 4= |θ 5−θ 4|
e
∆θ 1= |θ 2−θ 1|
∆θ 3= |θ 4−θ 3|
γ
∆θ 2= |θ 3−θ 2|
γ
Figure 122: Schematic view of an electron propagating through a brick and variables entering into the
χ 2 definition.
Since electrons in a shower are mostly produced around the critical energy of the medium (10 MeV
for lead), it is important to be sensitive to electrons with such an energy. However, electron tracks have
to be connected on both sides of the emulsion film to reject β’s from a neighbouring lead plate. This
condition imposes a mimimum detectable energy for the electrons.
The connection efficiency as a function of the electron energy is shown in Fig. 123 by using the
connection condition of δθ ≤ 50 mrad and δr ≤ 6 µm at the centre of the film. The efficiency
degradation for lower electron energy is due to multiple scattering in the emulsion films. The figure
shows that the minimum detectable energy for electrons is close to the critical energy of lead. The
number of β’s accidentally connected on both sides of the film is expected to be 1/mm 2 for a β density
of 10 5 /cm 2 . This value is still acceptable, but further study must be done to optimise the connection
efficiency.
The electron energy can be simply inferred by counting the number of track segments in a cone along
the track. The cone dimensions have to be optimised to include the largest number of cascade electron
pairs without integrating a too large number of background tracks. A cone of ±50 mrad and a relative
angle δθ ≤ 200 mrad to the primary track is used in the Monte Carlo study described in Section 7.1.
One obtains a resolution ∆E/E of about 20%, as shown in Fig. 124.
The space and angular region to be used for the electron identification and shower energy measurement
are not yet optimised. A test experiment and extensive Monte Carlo studies have to be performed for
this purpose.
An algorithm for electron/hadron separation can be realised, which makes use of a χ 2 fit which
combines the energy loss features and the counting of track segments in the cone around the track. The
performance of this algorithm applied to simulated tracks is described in Section 7.1.
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