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
-63.55
M_2
-803.67
M_3
3,586.44
mL1
1,342.52
mL3
3,440.22
mE1
1,493.26
mE3
3,776.00
mQ1
633.97
mQ3
2,373.71
mU1
1,223.39
mU3
3,174.14
mD1
2,738.54
mD3
285.24
At
3,156.05
Ab
-544.16
Atau
-236.14
mu
-518.03
mA
2,576.24
tan(beta)
16.67
The truth!
2
M_1
1,189.72
M_2
3,426.89
M_3
1,073.76
mL1
3,493.81
mL3
3,579.20
mE1
2,469.07
mE3
3,768.85
mQ1
1,724.39
mQ3
2,594.57
mU1
1,385.78
mU3
3,624.00
mD1
1,984.32
mD3
2,601.02
At
-3,353.27
Ab
-322.90
Atau
-3,581.48
mu
-2,738.80
mA
3,939.77
tan(beta)
25.53
The truth!
3
M_1
2,641.71
M_2
3,050.63
M_3
333.54
mL1
1,570.48
mL3
3,651.44
mE1
2,270.73
mE3
1,909.21
mQ1
868.40
mQ3
3,351.39
mU1
221.42
mU3
3,979.67
mD1
3,773.65
mD3
855.20
At
3,992.35
Ab
58.35
Atau
1,305.21
mu
239.85
mA
2,826.86
tan(beta)
26.23
The truth!
4
M_1
615.77
M_2
2,881.33
M_3
496.98
mL1
2,216.14
mL3
1,978.02
mE1
2,023.85
mE3
3,335.60
mQ1
3,485.37
mQ3
3,609.38
mU1
1,769.09
mU3
3,520.25
mD1
3,993.18
mD3
3,943.38
At
5,621.32
Ab
-3,497.16
Atau
3,999.77
mu
-2,982.81
mA
2,324.79
tan(beta)
4.04
The truth!
5
M_1
-206.44
M_2
-2,078.46
M_3
1,517.67
mL1
1,610.39
mL3
3,606.19
mE1
2,017.71
mE3
270.56
mQ1
2,837.79
mQ3
1,502.00
mU1
2,952.32
mU3
3,996.26
mD1
3,106.51
mD3
1,422.78
At
3,525.57
Ab
20.37
Atau
-1,519.02
mu
233.44
mA
2,178.59
tan(beta)
33.99
The truth!
6
M_1
553.86
M_2
3,728.93
M_3
1,294.34
mL1
1,888.45
mL3
3,204.86
mE1
1,383.10
mE3
837.02
mQ1
735.69
mQ3
2,154.20
mU1
404.31
mU3
3,245.12
mD1
1,191.78
mD3
1,749.18
At
-4,779.16
Ab
-3,894.66
Atau
-212.02
mu
4,041.48
mA
1,069.10
tan(beta)
22.15
The truth!
7
M_1
-777.53
M_2
2,741.49
M_3
2,984.92
mL1
999.10
mL3
3,351.05
mE1
2,972.68
mE3
3,311.90
mQ1
2,398.66
mQ3
3,692.15
mU1
834.83
mU3
692.07
mD1
1,632.25
mD3
1,905.37
At
-4,334.40
Ab
2,562.52
Atau
-1,433.66
mu
2,807.53
mA
1,951.79
tan(beta)
18.71
The truth!
8
M_1
2,672.50
M_2
1,256.89
M_3
1,925.63
mL1
3,876.93
mL3
2,918.44
mE1
3,325.95
mE3
3,424.93
mQ1
3,536.53
mQ3
1,405.96
mU1
2,868.66
mU3
2,137.93
mD1
1,371.10
mD3
1,272.68
At
-3,465.99
Ab
554.03
Atau
436.22
mu
2,997.32
mA
2,661.18
tan(beta)
14.19
The truth!
9
M_1
2,707.92
M_2
495.36
M_3
475.73
mL1
547.13
mL3
3,219.03
mE1
2,693.61
mE3
934.74
mQ1
2,293.29
mQ3
2,981.98
mU1
3,876.39
mU3
3,630.49
mD1
638.19
mD3
2,234.34
At
3,501.42
Ab
2,194.94
Atau
-2,325.56
mu
-3,059.33
mA
1,576.15
tan(beta)
13.67
The truth!
10
M_1
-647.58
M_2
-2,686.09
M_3
631.93
mL1
1,976.54
mL3
3,282.76
mE1
2,624.08
mE3
2,006.51
mQ1
3,242.71
mQ3
1,172.83
mU1
609.77
mU3
2,612.47
mD1
2,804.45
mD3
1,238.21
At
-2,729.41
Ab
-40.21
Atau
1,408.66
mu
-1,097.16
mA
3,879.07
tan(beta)
18.15
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
