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
3,088.39
M_2
-3,439.65
M_3
1,318.68
mL1
1,124.80
mL3
3,651.59
mE1
624.28
mE3
329.45
mQ1
991.26
mQ3
3,687.79
mU1
2,802.00
mU3
3,501.84
mD1
2,883.50
mD3
300.18
At
3,739.51
Ab
-3,541.69
Atau
3,221.83
mu
-263.05
mA
2,919.21
tan(beta)
29.00
The truth!
2
M_1
-3,886.40
M_2
1,559.88
M_3
2,708.18
mL1
1,065.55
mL3
2,725.71
mE1
552.40
mE3
3,101.36
mQ1
1,296.52
mQ3
1,341.41
mU1
3,790.76
mU3
3,614.35
mD1
445.15
mD3
458.42
At
2,490.75
Ab
-2,868.59
Atau
-3,229.34
mu
342.07
mA
1,821.53
tan(beta)
52.16
The truth!
3
M_1
-3,272.07
M_2
-608.38
M_3
1,041.64
mL1
1,937.68
mL3
2,380.99
mE1
457.05
mE3
3,927.19
mQ1
876.87
mQ3
3,265.63
mU1
3,888.87
mU3
3,079.46
mD1
3,688.45
mD3
1,813.16
At
-3,295.97
Ab
-1,336.87
Atau
-913.39
mu
-350.50
mA
2,323.75
tan(beta)
13.47
The truth!
4
M_1
1,270.04
M_2
185.88
M_3
3,528.71
mL1
3,638.42
mL3
1,564.39
mE1
2,670.22
mE3
620.03
mQ1
3,397.97
mQ3
2,813.77
mU1
1,361.18
mU3
1,210.27
mD1
935.38
mD3
2,586.63
At
-4,760.02
Ab
-3,260.05
Atau
-2,385.15
mu
3,985.47
mA
3,646.01
tan(beta)
29.01
The truth!
5
M_1
2,840.94
M_2
-945.55
M_3
3,860.65
mL1
2,414.38
mL3
1,582.22
mE1
1,365.66
mE3
2,735.28
mQ1
3,066.76
mQ3
1,555.79
mU1
1,047.85
mU3
3,401.22
mD1
2,422.50
mD3
720.46
At
-4,257.77
Ab
-2,646.23
Atau
-1,340.83
mu
-580.43
mA
2,598.14
tan(beta)
12.70
The truth!
6
M_1
3,924.22
M_2
237.52
M_3
1,365.35
mL1
2,736.90
mL3
3,415.58
mE1
2,455.86
mE3
3,219.43
mQ1
3,798.67
mQ3
3,354.72
mU1
3,540.09
mU3
1,175.61
mD1
3,745.88
mD3
783.35
At
2,757.38
Ab
-2,146.97
Atau
1,306.75
mu
771.47
mA
3,158.12
tan(beta)
47.26
The truth!
7
M_1
1,572.96
M_2
-3,906.64
M_3
2,686.05
mL1
3,037.44
mL3
3,710.95
mE1
948.21
mE3
3,315.67
mQ1
1,889.71
mQ3
3,800.50
mU1
2,123.63
mU3
1,526.71
mD1
1,309.45
mD3
3,167.22
At
3,635.45
Ab
1,727.47
Atau
2,142.02
mu
709.91
mA
3,472.25
tan(beta)
33.27
The truth!
8
M_1
-1,732.49
M_2
-3,926.87
M_3
495.98
mL1
1,134.56
mL3
2,688.70
mE1
1,903.34
mE3
540.31
mQ1
3,447.03
mQ3
1,279.66
mU1
711.25
mU3
2,271.98
mD1
1,399.22
mD3
1,198.49
At
2,681.23
Ab
-1,528.95
Atau
-2,347.05
mu
-473.63
mA
2,227.14
tan(beta)
8.99
The truth!
9
M_1
-1,575.30
M_2
-2,742.67
M_3
3,558.25
mL1
1,935.68
mL3
3,471.42
mE1
3,753.69
mE3
3,776.58
mQ1
2,098.62
mQ3
3,145.48
mU1
357.02
mU3
3,273.52
mD1
2,527.88
mD3
2,913.90
At
3,363.54
Ab
-751.21
Atau
-1,619.27
mu
681.70
mA
2,845.62
tan(beta)
40.01
The truth!
10
M_1
714.27
M_2
3,271.00
M_3
2,968.08
mL1
731.89
mL3
3,195.25
mE1
1,024.17
mE3
1,928.70
mQ1
3,124.19
mQ3
2,651.63
mU1
2,369.85
mU3
2,500.33
mD1
2,050.06
mD3
906.33
At
-4,748.25
Ab
-1,392.11
Atau
1,972.06
mu
1,049.48
mA
1,459.46
tan(beta)
12.12
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
