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
3,411.74
M_2
-2,224.44
M_3
1,180.62
mL1
3,518.39
mL3
2,228.97
mE1
948.16
mE3
2,901.13
mQ1
2,069.77
mQ3
2,132.22
mU1
3,840.80
mU3
2,635.90
mD1
2,374.53
mD3
256.38
At
2,866.02
Ab
-3,558.96
Atau
-3,997.30
mu
262.12
mA
2,682.52
tan(beta)
23.03
The truth!
2
M_1
-3,003.62
M_2
198.68
M_3
1,329.23
mL1
2,397.59
mL3
2,988.28
mE1
2,665.31
mE3
701.98
mQ1
1,793.43
mQ3
2,173.73
mU1
1,410.07
mU3
3,806.93
mD1
2,114.63
mD3
2,255.79
At
3,312.57
Ab
-1,294.32
Atau
278.07
mu
-2,887.72
mA
2,181.91
tan(beta)
25.00
The truth!
3
M_1
1,087.47
M_2
-996.82
M_3
3,346.24
mL1
447.25
mL3
2,876.04
mE1
3,320.73
mE3
889.37
mQ1
2,490.76
mQ3
2,227.36
mU1
2,179.22
mU3
3,094.54
mD1
1,375.33
mD3
3,559.61
At
-2,952.77
Ab
2,336.25
Atau
1,874.42
mu
416.58
mA
1,074.58
tan(beta)
22.70
The truth!
4
M_1
-3,728.73
M_2
-3,899.52
M_3
1,406.35
mL1
2,711.00
mL3
2,975.22
mE1
3,778.27
mE3
1,263.62
mQ1
1,858.32
mQ3
2,227.51
mU1
2,741.55
mU3
3,805.75
mD1
3,827.97
mD3
782.55
At
-3,778.07
Ab
2,587.41
Atau
-1,088.14
mu
-603.19
mA
2,186.92
tan(beta)
8.24
The truth!
5
M_1
705.42
M_2
-2,215.42
M_3
2,491.14
mL1
3,150.57
mL3
1,509.72
mE1
1,966.00
mE3
1,594.10
mQ1
3,505.60
mQ3
2,620.91
mU1
2,648.72
mU3
1,007.87
mD1
1,632.82
mD3
663.24
At
-3,469.16
Ab
-1,871.42
Atau
3,273.29
mu
1,588.56
mA
2,883.61
tan(beta)
21.65
The truth!
6
M_1
-568.39
M_2
3,907.71
M_3
454.19
mL1
1,957.55
mL3
1,155.44
mE1
561.46
mE3
2,477.63
mQ1
2,785.83
mQ3
1,214.00
mU1
1,636.56
mU3
3,568.34
mD1
3,816.02
mD3
2,269.91
At
-3,130.52
Ab
-1,339.12
Atau
252.43
mu
667.62
mA
1,594.66
tan(beta)
24.80
The truth!
7
M_1
-502.05
M_2
2,064.20
M_3
391.40
mL1
2,703.86
mL3
1,412.98
mE1
1,865.68
mE3
2,327.68
mQ1
1,567.77
mQ3
2,477.18
mU1
547.04
mU3
3,510.05
mD1
3,637.12
mD3
1,249.57
At
2,712.00
Ab
3,373.66
Atau
1,684.44
mu
-1,264.82
mA
3,786.91
tan(beta)
28.75
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
2,294.78
M_2
1,866.33
M_3
3,263.41
mL1
3,733.38
mL3
1,397.27
mE1
2,950.96
mE3
728.22
mQ1
2,354.16
mQ3
1,248.49
mU1
527.05
mU3
3,394.04
mD1
1,124.27
mD3
1,998.06
At
2,885.93
Ab
-1,604.00
Atau
-1,019.64
mu
-326.98
mA
1,297.75
tan(beta)
20.97
The truth!
10
M_1
-740.66
M_2
3,612.59
M_3
3,982.16
mL1
2,771.25
mL3
2,718.49
mE1
2,282.71
mE3
2,986.50
mQ1
1,893.00
mQ3
3,782.87
mU1
3,926.98
mU3
2,709.69
mD1
476.66
mD3
1,460.18
At
3,607.38
Ab
-1,093.99
Atau
-1,838.38
mu
-1,380.74
mA
2,201.21
tan(beta)
56.12
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
