

journal homepage: www.elsevier.com/locate/nima





Neutron detection
R3 B
NeuLAND
Machine Learning
Neural Networks





https://doi.org/10.1016/j.nima.2020.164951





Fig. 3).


NeuLAND.
2


Table 1


energy:

INCLXX

Bertini

INCLXX

Bertini

600 MeV
1000 MeV

81.7
85.2

80.7
82.8

623
691

554
632

NeuLAND.
∑
∑

as Ref. [18].
underestimates this.
3




Appendix.

4




cuts.
defined as:
,
(1)
cluster
𝐸dep


where 𝐸dep
beam
𝛽

5










7





design.
8




be required.

section).

observables
9




in this section.

10




in Fig. 12).

classification.
studies.
11





cross section values.
12





increased.

neutron energies.
13



required.



Acknowledgments






Hinge
Mean squared error

9000
9000

0
0

ADAM
ADAM

59.9%
73.7%

Cross entropy
Cross entropy
Cross entropy
Cross entropy

9000
9000
9000
9000

0
100
300
800

ADAM
ADAM
ADAM
ADAM

75.5%
74.1%
74.7%
74.2%

Cross entropy
Cross entropy
Cross entropy

9000
9000
9000

100
300
800

ADGRAD
ADGRAD
ADGRAD

73.3%
77.5%
79.7%

Cross entropy
Cross entropy
Cross entropy
Cross entropy
Cross entropy
Cross entropy
Cross entropy
Cross entropy

9000
9000
9000
9000
9000
9000
9000
9000

0
100
300
800
1200
3000
3500
6900

ADGRAD
ADGRAD
ADGRAD
ADGRAD
ADGRAD
ADGRAD
ADGRAD
ADGRAD

80.0%
79.5%
79.1%
79.7%
81.4%
81.1%
73.2%
20.0%

TDR [13]
Bayesian [13]
Scoring [13]

–
–
–

–
–
–

–
–
–

63.4%
50.4%
40.8%


14



a CNN.

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