S. Caron, J.S. Kim, K. Rolbiecki, R. Ruiz de Austri and B. Stienen, The BSM-AI project: SUSY-AI - Generalizing LHC limits on Supersymmetry with Machine Learning [arXiv:1605.02797]
SUSY-AI is a machine learning tool that is able to provide in a fraction of a second the exclusion of a pMSSM (sub)model point. This website provides a simple online interface for quick determination of exclusion of a model point using the results of ATLAS Run-I (8TeV) and ATLAS Run-II (13TeV). The papers associated with this data can be found here.
The full version of SUSY-AI is faster and can provide predicions for multiple modelpoints at the same time. It is under continuing active development and can be downloaded from the hepforge project page.
Classify alongside SUSY-AI and see how good a classifier you are
Below you'll find 10 points in the 19-dimensional pMSSM, defined by their coordinates. Based on these coordinates you could be able to classify each of them as excluded or allowed. You could... but are you able to this correctly for the 13TeV results? And are you able to beat the machine in doing so? Start classifying to find out!
P.S. Every refresh of this page gives you new points to predict!
1
M_1
-2,339.84
M_2
2,444.31
M_3
3,874.53
mL1
3,465.37
mL3
1,141.20
mE1
2,504.39
mE3
1,279.08
mQ1
2,052.95
mQ3
1,405.61
mU1
2,953.55
mU3
3,204.10
mD1
2,815.51
mD3
1,804.84
At
-6,193.06
Ab
-2,257.29
Atau
-1,548.73
mu
277.93
mA
1,919.83
tan(beta)
29.05
The truth!
2
M_1
1,275.85
M_2
-2,326.52
M_3
1,778.23
mL1
219.76
mL3
1,195.92
mE1
3,970.28
mE3
1,790.69
mQ1
1,360.39
mQ3
3,466.67
mU1
3,026.47
mU3
3,975.30
mD1
2,462.67
mD3
2,890.73
At
-2,452.48
Ab
-27.51
Atau
564.63
mu
-142.50
mA
3,568.52
tan(beta)
39.31
The truth!
3
M_1
-2,023.34
M_2
382.83
M_3
3,847.16
mL1
1,383.19
mL3
3,277.53
mE1
905.15
mE3
3,848.06
mQ1
3,149.37
mQ3
3,701.19
mU1
2,131.50
mU3
3,909.10
mD1
1,195.62
mD3
980.99
At
-4,309.47
Ab
2,164.82
Atau
1,327.05
mu
1,717.52
mA
3,550.74
tan(beta)
47.74
The truth!
4
M_1
3,401.44
M_2
-3,235.58
M_3
962.30
mL1
1,394.01
mL3
820.66
mE1
806.68
mE3
2,208.46
mQ1
3,739.45
mQ3
960.76
mU1
332.74
mU3
3,413.10
mD1
1,980.78
mD3
2,968.10
At
-2,701.74
Ab
-257.64
Atau
-1,577.22
mu
-416.46
mA
3,436.80
tan(beta)
56.40
The truth!
5
M_1
-2,562.70
M_2
-720.91
M_3
2,400.53
mL1
3,512.08
mL3
2,840.04
mE1
3,847.72
mE3
869.86
mQ1
2,633.00
mQ3
2,160.79
mU1
1,845.04
mU3
1,654.92
mD1
1,348.32
mD3
1,788.99
At
2,593.66
Ab
1,345.65
Atau
1,463.95
mu
-1,740.92
mA
4,118.73
tan(beta)
45.81
The truth!
6
M_1
-407.00
M_2
-1,375.62
M_3
321.48
mL1
1,113.26
mL3
3,597.96
mE1
2,230.79
mE3
1,978.22
mQ1
3,464.11
mQ3
3,139.30
mU1
1,477.81
mU3
2,019.03
mD1
3,674.63
mD3
1,632.17
At
3,110.80
Ab
-3,324.30
Atau
85.36
mu
-1,980.84
mA
3,666.38
tan(beta)
21.43
The truth!
7
M_1
-2,456.28
M_2
3,319.81
M_3
2,091.59
mL1
933.63
mL3
1,605.02
mE1
2,947.71
mE3
2,871.35
mQ1
2,032.68
mQ3
3,080.70
mU1
408.95
mU3
3,160.48
mD1
575.48
mD3
414.38
At
-4,266.83
Ab
-917.33
Atau
-1,682.60
mu
-249.68
mA
3,904.99
tan(beta)
30.80
The truth!
8
M_1
-44.52
M_2
1,923.81
M_3
3,790.58
mL1
2,210.29
mL3
1,450.87
mE1
977.75
mE3
1,445.44
mQ1
1,252.23
mQ3
3,249.44
mU1
3,106.83
mU3
2,560.23
mD1
614.83
mD3
939.32
At
-3,986.85
Ab
2,115.57
Atau
-449.87
mu
143.41
mA
3,313.31
tan(beta)
22.39
The truth!
9
M_1
-228.31
M_2
-371.40
M_3
2,610.85
mL1
3,398.16
mL3
863.43
mE1
1,170.34
mE3
758.51
mQ1
3,174.54
mQ3
3,899.03
mU1
1,282.57
mU3
2,879.17
mD1
1,330.13
mD3
2,914.38
At
2,760.43
Ab
1,127.27
Atau
-792.75
mu
-260.79
mA
2,584.78
tan(beta)
27.60
The truth!
10
M_1
-738.04
M_2
-3,980.17
M_3
3,862.87
mL1
3,075.59
mL3
3,704.88
mE1
1,720.73
mE3
1,014.17
mQ1
330.05
mQ3
2,734.03
mU1
512.14
mU3
3,961.80
mD1
1,433.29
mD3
3,669.73
At
4,127.03
Ab
2,452.18
Atau
2,638.84
mu
-3,078.85
mA
1,734.03
tan(beta)
35.97
The truth!
SUSY-AI and SUSY-AI Online were developed by S. Caron, J.S. Kim, K. Rolbiecki, R. Ruiz de Austri and B. Stienen. If you encounter any problems, don't hesitate to contact us! SUSY-AI and SUSY-AI Online (c) 2017
