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
-442.61
M_2
1,559.92
M_3
1,387.40
mL1
1,543.72
mL3
547.33
mE1
1,228.45
mE3
562.69
mQ1
337.18
mQ3
1,353.62
mU1
699.86
mU3
1,647.11
mD1
3,025.78
mD3
1,380.44
At
2,442.88
Ab
-2,417.26
Atau
-3,954.72
mu
1,161.34
mA
2,630.14
tan(beta)
27.73
The truth!
2
M_1
-422.68
M_2
-1,598.68
M_3
714.37
mL1
2,202.18
mL3
3,812.41
mE1
2,838.90
mE3
3,470.35
mQ1
2,529.18
mQ3
2,928.47
mU1
2,063.85
mU3
2,377.96
mD1
330.26
mD3
1,752.33
At
3,132.20
Ab
67.11
Atau
-413.70
mu
1,014.76
mA
3,480.23
tan(beta)
13.09
The truth!
3
M_1
3,063.59
M_2
-219.24
M_3
3,147.11
mL1
3,908.60
mL3
2,630.93
mE1
2,751.61
mE3
509.42
mQ1
959.72
mQ3
1,010.29
mU1
3,216.16
mU3
2,683.68
mD1
911.95
mD3
1,888.62
At
-2,399.77
Ab
3,329.62
Atau
3,136.22
mu
1,344.82
mA
3,091.16
tan(beta)
48.82
The truth!
4
M_1
-777.53
M_2
2,741.49
M_3
2,984.92
mL1
999.10
mL3
3,351.05
mE1
2,972.68
mE3
3,311.90
mQ1
2,398.66
mQ3
3,692.15
mU1
834.83
mU3
692.07
mD1
1,632.25
mD3
1,905.37
At
-4,334.40
Ab
2,562.52
Atau
-1,433.66
mu
2,807.53
mA
1,951.79
tan(beta)
18.71
The truth!
5
M_1
-3,916.39
M_2
-127.70
M_3
3,185.20
mL1
3,415.38
mL3
325.45
mE1
548.57
mE3
522.01
mQ1
2,784.48
mQ3
3,038.44
mU1
304.77
mU3
1,187.34
mD1
2,425.87
mD3
3,731.94
At
3,447.23
Ab
3,248.39
Atau
2,514.29
mu
2,561.83
mA
3,152.50
tan(beta)
11.40
The truth!
6
M_1
-3,124.93
M_2
-1,723.67
M_3
3,250.12
mL1
2,401.87
mL3
2,026.97
mE1
3,373.52
mE3
3,993.48
mQ1
1,576.16
mQ3
2,894.42
mU1
1,218.39
mU3
3,210.20
mD1
1,411.74
mD3
3,430.82
At
-3,668.07
Ab
1,730.70
Atau
3,036.15
mu
-228.20
mA
1,224.61
tan(beta)
10.44
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
1,325.73
M_2
-1,735.91
M_3
1,188.64
mL1
1,312.33
mL3
1,703.19
mE1
3,834.31
mE3
1,780.36
mQ1
1,359.07
mQ3
1,358.36
mU1
3,710.92
mU3
2,021.46
mD1
1,815.17
mD3
1,739.65
At
-3,711.74
Ab
-2,866.03
Atau
-2,867.82
mu
2,358.71
mA
2,807.97
tan(beta)
7.74
The truth!
9
M_1
-46.72
M_2
2,469.40
M_3
3,811.88
mL1
279.85
mL3
3,959.32
mE1
3,693.07
mE3
3,530.45
mQ1
1,367.76
mQ3
2,952.65
mU1
537.33
mU3
2,131.56
mD1
1,227.75
mD3
3,227.82
At
-4,179.70
Ab
-3,643.28
Atau
316.92
mu
257.92
mA
1,925.22
tan(beta)
57.34
The truth!
10
M_1
-44.52
M_2
1,923.81
M_3
3,790.58
mL1
2,210.29
mL3
1,450.87
mE1
977.75
mE3
1,445.44
mQ1
1,252.23
mQ3
3,249.44
mU1
3,106.83
mU3
2,560.23
mD1
614.83
mD3
939.32
At
-3,986.85
Ab
2,115.57
Atau
-449.87
mu
143.41
mA
3,313.31
tan(beta)
22.39
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
