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
-501.08
M_2
1,978.39
M_3
2,774.62
mL1
1,473.18
mL3
2,557.75
mE1
1,863.09
mE3
2,259.30
mQ1
3,077.22
mQ3
777.15
mU1
2,752.12
mU3
3,606.04
mD1
2,907.67
mD3
493.81
At
2,632.37
Ab
-2,449.17
Atau
-3,205.95
mu
-513.44
mA
1,882.02
tan(beta)
15.40
The truth!
2
M_1
-186.23
M_2
177.73
M_3
2,238.76
mL1
3,083.17
mL3
2,161.39
mE1
2,979.10
mE3
2,345.21
mQ1
2,216.89
mQ3
2,198.99
mU1
2,897.51
mU3
2,250.56
mD1
2,887.88
mD3
2,221.69
At
2,763.46
Ab
-2,498.11
Atau
3,155.82
mu
-1,177.63
mA
3,944.87
tan(beta)
19.78
The truth!
3
M_1
3,674.07
M_2
-3,260.89
M_3
965.33
mL1
3,516.78
mL3
3,482.04
mE1
2,075.02
mE3
2,690.84
mQ1
640.65
mQ3
786.15
mU1
3,209.30
mU3
3,355.88
mD1
829.25
mD3
3,969.98
At
-4,930.79
Ab
-753.07
Atau
465.52
mu
-557.82
mA
1,423.51
tan(beta)
4.48
The truth!
4
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!
5
M_1
1,354.77
M_2
2,581.33
M_3
2,596.24
mL1
1,325.26
mL3
1,825.43
mE1
688.99
mE3
1,452.85
mQ1
2,448.70
mQ3
1,449.58
mU1
314.05
mU3
2,929.29
mD1
2,957.18
mD3
1,572.68
At
-3,434.59
Ab
274.10
Atau
-186.70
mu
211.18
mA
2,794.39
tan(beta)
15.04
The truth!
6
M_1
1,968.05
M_2
430.55
M_3
3,545.62
mL1
1,891.44
mL3
3,809.56
mE1
3,085.24
mE3
3,253.13
mQ1
692.35
mQ3
3,106.82
mU1
1,001.47
mU3
1,685.77
mD1
3,434.16
mD3
1,485.54
At
-2,920.14
Ab
-3,212.45
Atau
-22.09
mu
2,821.81
mA
1,629.00
tan(beta)
22.60
The truth!
7
M_1
-3,280.67
M_2
3,424.08
M_3
2,912.97
mL1
3,427.79
mL3
3,082.67
mE1
824.44
mE3
2,254.27
mQ1
2,739.91
mQ3
3,277.58
mU1
1,648.04
mU3
2,543.93
mD1
3,680.31
mD3
3,886.04
At
-3,957.54
Ab
-355.74
Atau
535.50
mu
723.78
mA
2,087.51
tan(beta)
27.62
The truth!
8
M_1
-2,147.08
M_2
-944.21
M_3
3,321.61
mL1
1,182.43
mL3
3,846.15
mE1
3,806.62
mE3
2,663.20
mQ1
1,825.98
mQ3
3,443.22
mU1
3,590.30
mU3
1,342.02
mD1
3,283.09
mD3
3,883.07
At
3,094.82
Ab
3,144.56
Atau
2,347.99
mu
2,092.67
mA
3,535.57
tan(beta)
22.81
The truth!
9
M_1
-2,256.98
M_2
-3,119.93
M_3
2,227.81
mL1
1,898.90
mL3
508.76
mE1
2,097.45
mE3
3,105.23
mQ1
3,042.07
mQ3
3,086.33
mU1
399.92
mU3
1,134.21
mD1
1,064.84
mD3
3,570.29
At
3,406.45
Ab
2,793.40
Atau
-175.86
mu
-315.42
mA
1,689.83
tan(beta)
18.78
The truth!
10
M_1
-676.39
M_2
-2,279.99
M_3
1,841.46
mL1
2,682.53
mL3
1,053.64
mE1
1,816.30
mE3
3,484.77
mQ1
2,222.36
mQ3
3,372.11
mU1
432.21
mU3
3,096.15
mD1
488.92
mD3
699.55
At
5,218.91
Ab
3,347.23
Atau
3,059.27
mu
-3,917.44
mA
2,883.77
tan(beta)
3.84
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
