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
-63.55
M_2
-803.67
M_3
3,586.44
mL1
1,342.52
mL3
3,440.22
mE1
1,493.26
mE3
3,776.00
mQ1
633.97
mQ3
2,373.71
mU1
1,223.39
mU3
3,174.14
mD1
2,738.54
mD3
285.24
At
3,156.05
Ab
-544.16
Atau
-236.14
mu
-518.03
mA
2,576.24
tan(beta)
16.67
The truth!
2
M_1
3,296.90
M_2
506.75
M_3
3,285.74
mL1
2,236.18
mL3
1,217.59
mE1
1,900.24
mE3
2,937.88
mQ1
3,086.44
mQ3
2,319.03
mU1
383.72
mU3
2,864.01
mD1
3,097.39
mD3
928.44
At
3,355.88
Ab
-1,305.84
Atau
-3,883.72
mu
-3,324.99
mA
3,514.41
tan(beta)
7.83
The truth!
3
M_1
3,120.86
M_2
1,333.36
M_3
3,469.06
mL1
1,978.53
mL3
1,177.21
mE1
1,690.75
mE3
1,061.83
mQ1
1,907.70
mQ3
2,744.72
mU1
2,986.58
mU3
2,912.33
mD1
1,630.39
mD3
3,083.15
At
-2,875.91
Ab
-3,366.71
Atau
-655.59
mu
-828.32
mA
3,527.13
tan(beta)
23.35
The truth!
4
M_1
-3,709.18
M_2
-3,134.63
M_3
2,795.39
mL1
3,371.95
mL3
3,329.16
mE1
1,226.43
mE3
731.37
mQ1
2,554.66
mQ3
3,303.66
mU1
1,961.22
mU3
1,122.63
mD1
735.49
mD3
2,426.31
At
-4,163.53
Ab
64.87
Atau
3,199.26
mu
309.47
mA
3,184.73
tan(beta)
17.70
The truth!
5
M_1
3,607.25
M_2
-331.34
M_3
3,901.39
mL1
2,983.03
mL3
2,871.08
mE1
358.82
mE3
2,520.43
mQ1
3,969.31
mQ3
3,598.11
mU1
704.17
mU3
1,753.63
mD1
1,479.12
mD3
1,729.60
At
-2,929.67
Ab
-2,799.44
Atau
792.62
mu
1,990.01
mA
1,407.89
tan(beta)
27.85
The truth!
6
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!
7
M_1
2,961.80
M_2
-517.11
M_3
3,186.70
mL1
1,953.20
mL3
3,649.45
mE1
3,064.79
mE3
3,263.66
mQ1
3,345.65
mQ3
1,901.98
mU1
822.34
mU3
1,143.60
mD1
1,941.14
mD3
3,157.69
At
-2,788.69
Ab
-1,581.52
Atau
2,537.20
mu
1,815.67
mA
3,414.67
tan(beta)
30.43
The truth!
8
M_1
-815.99
M_2
-3,264.12
M_3
1,483.17
mL1
3,507.89
mL3
887.61
mE1
954.61
mE3
1,978.96
mQ1
1,529.66
mQ3
3,976.98
mU1
1,288.18
mU3
1,537.98
mD1
593.81
mD3
321.39
At
-3,518.88
Ab
3,093.16
Atau
-1,042.80
mu
284.76
mA
3,875.92
tan(beta)
33.36
The truth!
9
M_1
-1,729.10
M_2
-370.41
M_3
1,212.54
mL1
1,698.71
mL3
2,136.05
mE1
3,240.00
mE3
1,672.32
mQ1
3,348.07
mQ3
821.64
mU1
2,721.02
mU3
1,380.41
mD1
2,846.62
mD3
1,870.83
At
-2,644.59
Ab
-1,864.08
Atau
1,007.94
mu
1,690.05
mA
885.21
tan(beta)
17.36
The truth!
10
M_1
1,975.95
M_2
-1,693.05
M_3
1,149.76
mL1
424.86
mL3
1,356.87
mE1
615.20
mE3
2,032.18
mQ1
2,592.75
mQ3
820.96
mU1
1,139.96
mU3
3,214.17
mD1
2,036.27
mD3
3,453.10
At
-5,157.37
Ab
-1,906.52
Atau
761.99
mu
312.06
mA
2,052.54
tan(beta)
5.87
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
