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
3,814.70
M_2
3,905.35
M_3
1,855.22
mL1
1,573.08
mL3
3,957.95
mE1
691.89
mE3
935.08
mQ1
1,610.47
mQ3
3,492.07
mU1
790.40
mU3
3,962.61
mD1
1,021.23
mD3
3,758.21
At
5,316.82
Ab
1,649.59
Atau
1,177.68
mu
-614.62
mA
1,118.68
tan(beta)
13.43
The truth!
2
M_1
1,134.70
M_2
2,413.30
M_3
866.80
mL1
1,382.57
mL3
3,686.84
mE1
2,313.77
mE3
1,486.44
mQ1
2,887.81
mQ3
2,162.36
mU1
2,593.13
mU3
1,536.68
mD1
335.62
mD3
2,063.88
At
2,944.77
Ab
3,710.08
Atau
2,579.99
mu
-279.93
mA
1,400.00
tan(beta)
10.08
The truth!
3
M_1
-2,684.24
M_2
-806.79
M_3
776.73
mL1
2,336.81
mL3
2,372.63
mE1
2,455.35
mE3
2,357.70
mQ1
3,878.57
mQ3
1,828.97
mU1
2,652.77
mU3
2,051.34
mD1
1,673.02
mD3
1,388.11
At
-3,501.99
Ab
-579.02
Atau
-3,430.48
mu
3,458.44
mA
2,722.68
tan(beta)
23.36
The truth!
4
M_1
1,909.05
M_2
176.57
M_3
651.63
mL1
1,425.79
mL3
2,816.08
mE1
3,966.78
mE3
2,096.24
mQ1
2,604.72
mQ3
1,394.13
mU1
2,390.43
mU3
3,035.42
mD1
3,852.27
mD3
3,303.06
At
2,410.59
Ab
-355.76
Atau
-2,841.34
mu
-685.48
mA
3,240.92
tan(beta)
53.04
The truth!
5
M_1
800.58
M_2
-135.23
M_3
3,079.86
mL1
3,393.47
mL3
1,936.86
mE1
745.70
mE3
691.29
mQ1
2,813.37
mQ3
3,613.45
mU1
411.49
mU3
2,078.88
mD1
1,550.93
mD3
3,879.76
At
-2,839.51
Ab
2,973.63
Atau
-556.14
mu
1,918.80
mA
1,866.56
tan(beta)
54.02
The truth!
6
M_1
-220.21
M_2
-1,560.87
M_3
3,747.98
mL1
147.82
mL3
2,418.37
mE1
782.39
mE3
911.17
mQ1
1,276.05
mQ3
1,929.81
mU1
2,896.72
mU3
3,853.92
mD1
3,964.70
mD3
780.41
At
-3,981.38
Ab
2,418.97
Atau
306.36
mu
-851.14
mA
3,305.13
tan(beta)
20.79
The truth!
7
M_1
-815.99
M_2
-3,264.12
M_3
1,483.17
mL1
3,507.89
mL3
887.61
mE1
954.61
mE3
1,978.96
mQ1
1,529.66
mQ3
3,976.98
mU1
1,288.18
mU3
1,537.98
mD1
593.81
mD3
321.39
At
-3,518.88
Ab
3,093.16
Atau
-1,042.80
mu
284.76
mA
3,875.92
tan(beta)
33.36
The truth!
8
M_1
2,034.31
M_2
509.20
M_3
1,590.26
mL1
2,422.09
mL3
3,535.57
mE1
3,592.56
mE3
2,794.04
mQ1
1,811.32
mQ3
1,475.83
mU1
3,403.05
mU3
1,777.87
mD1
2,756.95
mD3
3,989.85
At
2,409.27
Ab
-3,467.24
Atau
1,636.99
mu
-573.62
mA
1,681.04
tan(beta)
25.51
The truth!
9
M_1
-65.98
M_2
-3,577.03
M_3
2,780.06
mL1
3,323.28
mL3
3,821.55
mE1
2,220.49
mE3
824.10
mQ1
2,963.50
mQ3
2,974.25
mU1
767.47
mU3
3,690.18
mD1
1,224.81
mD3
1,389.48
At
4,029.14
Ab
-2,468.58
Atau
-865.95
mu
147.14
mA
3,257.96
tan(beta)
55.08
The truth!
10
M_1
2,315.70
M_2
-529.36
M_3
2,596.86
mL1
2,507.46
mL3
930.58
mE1
1,155.34
mE3
3,660.39
mQ1
2,143.31
mQ3
1,343.77
mU1
3,255.89
mU3
1,863.74
mD1
3,154.18
mD3
3,239.97
At
2,557.52
Ab
-3,407.88
Atau
-1,905.61
mu
-702.31
mA
2,033.92
tan(beta)
8.97
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
