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
1,952.18
M_2
674.10
M_3
2,651.29
mL1
1,306.67
mL3
2,812.76
mE1
2,528.13
mE3
1,925.96
mQ1
2,097.62
mQ3
1,611.33
mU1
1,882.11
mU3
2,231.53
mD1
3,558.19
mD3
3,602.32
At
-3,131.44
Ab
-3,264.92
Atau
-2,846.05
mu
2,071.56
mA
4,399.10
tan(beta)
58.55
The truth!
2
M_1
546.95
M_2
3,596.30
M_3
2,552.92
mL1
1,757.25
mL3
2,613.12
mE1
3,044.24
mE3
2,585.38
mQ1
3,062.95
mQ3
2,324.92
mU1
814.61
mU3
2,819.35
mD1
287.07
mD3
2,916.04
At
3,434.50
Ab
941.87
Atau
-2,055.51
mu
-2,501.84
mA
2,593.28
tan(beta)
20.94
The truth!
3
M_1
3,628.16
M_2
-953.33
M_3
642.25
mL1
3,261.58
mL3
473.18
mE1
3,780.22
mE3
454.02
mQ1
782.68
mQ3
2,558.35
mU1
2,922.19
mU3
3,729.38
mD1
579.50
mD3
574.03
At
3,804.28
Ab
279.89
Atau
3,717.70
mu
321.79
mA
2,190.26
tan(beta)
35.75
The truth!
4
M_1
513.46
M_2
-1,644.59
M_3
1,137.07
mL1
3,553.20
mL3
1,214.71
mE1
3,567.61
mE3
3,778.25
mQ1
1,650.02
mQ3
3,641.33
mU1
3,744.83
mU3
3,507.73
mD1
3,409.93
mD3
326.59
At
-4,766.28
Ab
2,957.71
Atau
2,020.72
mu
3,015.14
mA
1,858.52
tan(beta)
29.18
The truth!
5
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!
6
M_1
-3,978.61
M_2
2,525.41
M_3
2,484.27
mL1
1,621.52
mL3
3,489.10
mE1
3,449.24
mE3
1,189.12
mQ1
353.29
mQ3
2,820.79
mU1
2,123.07
mU3
1,984.04
mD1
1,077.66
mD3
1,442.88
At
4,210.92
Ab
-2,967.04
Atau
2,775.79
mu
-201.33
mA
2,323.92
tan(beta)
8.53
The truth!
7
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!
8
M_1
2,795.23
M_2
-282.74
M_3
2,668.85
mL1
890.37
mL3
2,406.43
mE1
913.64
mE3
2,439.08
mQ1
607.12
mQ3
2,001.56
mU1
3,074.19
mU3
1,133.56
mD1
686.51
mD3
1,313.86
At
-3,453.06
Ab
-2,237.62
Atau
-2,834.02
mu
-1,464.07
mA
3,701.92
tan(beta)
10.33
The truth!
9
M_1
-771.98
M_2
3,104.28
M_3
635.67
mL1
1,136.26
mL3
3,194.62
mE1
2,455.23
mE3
1,026.54
mQ1
2,273.97
mQ3
1,872.39
mU1
3,230.92
mU3
3,763.96
mD1
2,337.27
mD3
982.27
At
-6,132.60
Ab
1,345.35
Atau
-3,311.07
mu
-2,352.22
mA
2,718.70
tan(beta)
5.19
The truth!
10
M_1
-2,338.84
M_2
-269.27
M_3
3,305.30
mL1
1,131.59
mL3
1,396.25
mE1
2,687.53
mE3
3,143.53
mQ1
653.33
mQ3
1,349.03
mU1
236.33
mU3
3,160.53
mD1
3,056.73
mD3
1,147.75
At
-6,097.58
Ab
778.86
Atau
1,879.47
mu
1,134.99
mA
2,694.82
tan(beta)
9.13
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
