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
538.18
M_2
587.30
M_3
3,341.24
mL1
1,759.69
mL3
2,111.10
mE1
1,969.25
mE3
2,082.61
mQ1
2,796.97
mQ3
2,255.88
mU1
3,111.95
mU3
3,040.37
mD1
963.41
mD3
883.73
At
3,143.42
Ab
-1,778.81
Atau
651.46
mu
-604.64
mA
1,746.32
tan(beta)
55.90
The truth!
2
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!
3
M_1
645.39
M_2
-3,520.54
M_3
2,997.82
mL1
1,765.08
mL3
1,939.26
mE1
717.73
mE3
1,894.44
mQ1
2,434.22
mQ3
1,805.40
mU1
3,556.24
mU3
2,030.84
mD1
1,932.02
mD3
3,330.44
At
3,475.98
Ab
13.15
Atau
-718.02
mu
-853.31
mA
1,281.19
tan(beta)
7.26
The truth!
4
M_1
-2,895.90
M_2
3,285.80
M_3
1,048.77
mL1
3,398.06
mL3
2,096.28
mE1
2,343.32
mE3
1,330.55
mQ1
350.65
mQ3
1,291.84
mU1
1,489.27
mU3
1,966.10
mD1
3,957.17
mD3
2,773.95
At
3,864.20
Ab
3,303.05
Atau
-3,403.79
mu
-308.82
mA
1,471.33
tan(beta)
3.88
The truth!
5
M_1
147.18
M_2
622.95
M_3
1,512.02
mL1
2,757.74
mL3
1,032.83
mE1
664.24
mE3
3,266.68
mQ1
366.68
mQ3
3,373.16
mU1
2,504.84
mU3
3,305.62
mD1
2,938.84
mD3
1,329.66
At
4,451.93
Ab
1,057.22
Atau
-2,428.35
mu
-171.39
mA
2,196.60
tan(beta)
11.80
The truth!
6
M_1
-3,142.14
M_2
198.43
M_3
3,025.53
mL1
1,472.92
mL3
1,207.91
mE1
1,762.59
mE3
2,703.17
mQ1
1,612.09
mQ3
3,236.33
mU1
1,875.55
mU3
3,576.47
mD1
3,194.44
mD3
1,475.28
At
-7,187.62
Ab
-1,745.87
Atau
2,748.05
mu
2,750.63
mA
1,816.82
tan(beta)
4.55
The truth!
7
M_1
-3,122.52
M_2
3,578.68
M_3
2,747.38
mL1
1,698.66
mL3
1,259.10
mE1
980.94
mE3
2,570.15
mQ1
840.23
mQ3
1,611.79
mU1
2,399.89
mU3
2,337.06
mD1
1,112.90
mD3
3,422.72
At
3,620.30
Ab
-90.49
Atau
-3,393.28
mu
-231.38
mA
2,765.48
tan(beta)
6.79
The truth!
8
M_1
1,572.96
M_2
-3,906.64
M_3
2,686.05
mL1
3,037.44
mL3
3,710.95
mE1
948.21
mE3
3,315.67
mQ1
1,889.71
mQ3
3,800.50
mU1
2,123.63
mU3
1,526.71
mD1
1,309.45
mD3
3,167.22
At
3,635.45
Ab
1,727.47
Atau
2,142.02
mu
709.91
mA
3,472.25
tan(beta)
33.27
The truth!
9
M_1
-318.99
M_2
-1,651.11
M_3
1,303.87
mL1
3,860.83
mL3
748.47
mE1
701.44
mE3
2,640.49
mQ1
379.43
mQ3
2,438.14
mU1
498.27
mU3
3,581.09
mD1
3,723.09
mD3
2,297.37
At
2,951.61
Ab
-3,464.68
Atau
-1,637.18
mu
339.08
mA
2,659.83
tan(beta)
17.16
The truth!
10
M_1
3,729.39
M_2
-370.51
M_3
2,601.63
mL1
3,105.04
mL3
822.83
mE1
2,032.95
mE3
1,033.16
mQ1
3,274.17
mQ3
3,345.79
mU1
1,420.47
mU3
3,233.53
mD1
1,287.07
mD3
2,021.98
At
-3,279.82
Ab
-2,689.86
Atau
1,632.00
mu
887.44
mA
1,205.99
tan(beta)
8.05
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
