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
-2,750.08
M_2
-1,102.50
M_3
3,235.22
mL1
2,056.04
mL3
1,767.38
mE1
2,282.23
mE3
3,190.56
mQ1
1,081.07
mQ3
2,642.38
mU1
1,629.13
mU3
3,453.65
mD1
3,066.24
mD3
2,227.82
At
3,383.00
Ab
-1,455.22
Atau
2,993.38
mu
-1,105.84
mA
1,250.43
tan(beta)
22.23
The truth!
2
M_1
-200.53
M_2
-2,598.60
M_3
3,125.73
mL1
1,671.64
mL3
3,784.91
mE1
1,858.38
mE3
2,163.68
mQ1
785.44
mQ3
3,185.75
mU1
3,484.48
mU3
2,640.25
mD1
3,226.94
mD3
3,356.86
At
3,130.05
Ab
3,215.15
Atau
-2,850.75
mu
-216.78
mA
1,854.82
tan(beta)
28.75
The truth!
3
M_1
-1,009.49
M_2
3,256.93
M_3
1,022.99
mL1
3,597.43
mL3
3,811.80
mE1
1,667.50
mE3
1,216.09
mQ1
1,218.89
mQ3
3,241.56
mU1
3,432.48
mU3
1,887.84
mD1
1,452.06
mD3
3,660.28
At
-2,931.80
Ab
-1,415.30
Atau
3,636.87
mu
457.03
mA
2,463.89
tan(beta)
50.08
The truth!
4
M_1
3,924.22
M_2
237.52
M_3
1,365.35
mL1
2,736.90
mL3
3,415.58
mE1
2,455.86
mE3
3,219.43
mQ1
3,798.67
mQ3
3,354.72
mU1
3,540.09
mU3
1,175.61
mD1
3,745.88
mD3
783.35
At
2,757.38
Ab
-2,146.97
Atau
1,306.75
mu
771.47
mA
3,158.12
tan(beta)
47.26
The truth!
5
M_1
916.90
M_2
901.71
M_3
3,572.66
mL1
1,225.94
mL3
3,633.52
mE1
3,763.47
mE3
2,957.09
mQ1
3,839.98
mQ3
3,796.68
mU1
2,842.90
mU3
3,036.21
mD1
2,452.63
mD3
860.31
At
2,353.16
Ab
2,105.14
Atau
-3,746.52
mu
-1,024.10
mA
2,204.14
tan(beta)
20.44
The truth!
6
M_1
1,644.84
M_2
543.20
M_3
2,539.88
mL1
1,222.58
mL3
1,851.32
mE1
1,559.18
mE3
3,887.84
mQ1
738.86
mQ3
2,768.80
mU1
1,365.92
mU3
3,443.65
mD1
3,399.39
mD3
2,792.80
At
-4,472.38
Ab
-625.56
Atau
-1,499.62
mu
2,637.52
mA
2,317.98
tan(beta)
35.02
The truth!
7
M_1
1,200.86
M_2
485.50
M_3
2,633.45
mL1
3,874.98
mL3
3,316.53
mE1
1,201.52
mE3
1,745.31
mQ1
1,383.80
mQ3
3,424.40
mU1
3,493.73
mU3
2,478.88
mD1
3,667.63
mD3
2,223.50
At
4,954.27
Ab
2,090.97
Atau
-1,433.34
mu
-2,210.58
mA
2,366.57
tan(beta)
5.68
The truth!
8
M_1
-138.79
M_2
204.45
M_3
818.76
mL1
2,790.04
mL3
2,740.62
mE1
806.88
mE3
557.20
mQ1
3,682.08
mQ3
2,708.54
mU1
2,949.96
mU3
2,932.82
mD1
3,092.68
mD3
2,614.58
At
-3,785.43
Ab
-3,296.33
Atau
-831.42
mu
183.97
mA
1,143.72
tan(beta)
8.03
The truth!
9
M_1
-472.67
M_2
3,737.72
M_3
700.06
mL1
2,991.52
mL3
3,342.58
mE1
2,719.13
mE3
446.99
mQ1
2,148.97
mQ3
867.80
mU1
416.60
mU3
3,104.57
mD1
729.94
mD3
1,240.07
At
2,729.20
Ab
3,233.67
Atau
3,382.67
mu
-841.77
mA
2,511.24
tan(beta)
37.91
The truth!
10
M_1
3,657.59
M_2
414.67
M_3
1,454.73
mL1
1,648.34
mL3
485.55
mE1
2,980.70
mE3
1,892.06
mQ1
659.01
mQ3
2,601.69
mU1
1,103.70
mU3
2,773.81
mD1
3,221.09
mD3
363.44
At
3,601.81
Ab
821.71
Atau
-1,616.77
mu
-1,362.24
mA
2,677.51
tan(beta)
17.09
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
