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
-303.14
M_2
-2,604.07
M_3
3,178.20
mL1
2,073.15
mL3
3,299.74
mE1
3,446.03
mE3
575.94
mQ1
1,499.41
mQ3
2,076.50
mU1
943.86
mU3
1,744.22
mD1
499.80
mD3
1,934.66
At
-2,886.72
Ab
3,969.76
Atau
-394.97
mu
499.13
mA
615.63
tan(beta)
16.21
The truth!
2
M_1
869.50
M_2
-1,667.38
M_3
3,620.83
mL1
939.03
mL3
1,361.91
mE1
3,556.33
mE3
1,202.76
mQ1
3,505.46
mQ3
1,536.08
mU1
1,394.05
mU3
3,744.48
mD1
3,508.74
mD3
1,211.95
At
3,920.92
Ab
-1,477.78
Atau
1,305.31
mu
176.65
mA
1,148.94
tan(beta)
9.58
The truth!
3
M_1
-649.11
M_2
-2,429.82
M_3
1,873.23
mL1
1,986.48
mL3
1,552.23
mE1
1,156.07
mE3
746.94
mQ1
3,986.05
mQ3
1,996.46
mU1
3,053.56
mU3
2,997.55
mD1
523.06
mD3
2,296.65
At
3,722.88
Ab
-2,289.05
Atau
-3,237.18
mu
579.11
mA
3,818.47
tan(beta)
52.66
The truth!
4
M_1
-2,616.14
M_2
-1,738.23
M_3
3,199.54
mL1
1,424.78
mL3
2,888.02
mE1
3,551.64
mE3
1,430.58
mQ1
1,424.61
mQ3
2,401.42
mU1
1,115.27
mU3
2,680.66
mD1
1,484.34
mD3
3,083.13
At
5,325.74
Ab
-3,911.42
Atau
-1,318.06
mu
378.27
mA
3,726.18
tan(beta)
4.17
The truth!
5
M_1
328.29
M_2
-1,588.31
M_3
1,251.06
mL1
1,696.92
mL3
3,019.10
mE1
3,290.58
mE3
1,201.04
mQ1
3,279.36
mQ3
3,044.24
mU1
3,941.29
mU3
3,739.46
mD1
629.68
mD3
1,224.45
At
-3,139.42
Ab
3,715.83
Atau
1,915.37
mu
318.72
mA
1,711.32
tan(beta)
16.89
The truth!
6
M_1
-3,231.62
M_2
353.67
M_3
1,333.27
mL1
1,707.49
mL3
2,849.71
mE1
3,614.24
mE3
2,641.33
mQ1
2,898.11
mQ3
1,785.96
mU1
786.13
mU3
1,338.15
mD1
280.28
mD3
3,961.62
At
2,820.17
Ab
1,822.08
Atau
2,952.12
mu
-2,441.13
mA
4,028.81
tan(beta)
48.12
The truth!
7
M_1
-259.47
M_2
-3,719.40
M_3
1,005.30
mL1
1,443.69
mL3
825.16
mE1
1,753.49
mE3
3,545.48
mQ1
2,805.49
mQ3
3,763.89
mU1
1,453.71
mU3
3,852.43
mD1
2,371.82
mD3
731.98
At
-3,513.19
Ab
-1,008.05
Atau
-853.05
mu
287.58
mA
946.22
tan(beta)
18.36
The truth!
8
M_1
1,035.47
M_2
-2,371.31
M_3
883.68
mL1
1,099.20
mL3
1,739.73
mE1
2,876.93
mE3
3,510.66
mQ1
1,583.29
mQ3
2,180.31
mU1
3,505.37
mU3
3,092.19
mD1
3,478.04
mD3
2,131.63
At
3,626.29
Ab
-366.74
Atau
-302.36
mu
-2,345.61
mA
3,785.66
tan(beta)
33.42
The truth!
9
M_1
62.91
M_2
3,169.75
M_3
2,914.80
mL1
1,887.83
mL3
2,748.09
mE1
3,194.78
mE3
2,083.18
mQ1
1,067.79
mQ3
2,585.23
mU1
769.56
mU3
3,359.90
mD1
3,614.71
mD3
3,602.08
At
-3,424.94
Ab
1,211.42
Atau
-2,585.65
mu
-170.35
mA
1,324.62
tan(beta)
49.86
The truth!
10
M_1
47.44
M_2
-1,238.94
M_3
3,939.08
mL1
2,181.93
mL3
611.47
mE1
3,400.04
mE3
2,432.59
mQ1
819.28
mQ3
3,278.67
mU1
2,488.46
mU3
2,282.91
mD1
2,120.48
mD3
1,053.83
At
-4,302.09
Ab
-1,062.89
Atau
-3,701.15
mu
-154.96
mA
3,435.09
tan(beta)
10.60
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
