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
-1,262.19
M_2
-3,064.49
M_3
647.31
mL1
207.16
mL3
2,590.81
mE1
706.11
mE3
3,564.93
mQ1
1,453.91
mQ3
3,758.29
mU1
3,840.44
mU3
3,326.33
mD1
1,904.24
mD3
1,615.73
At
4,516.67
Ab
-2,725.89
Atau
945.87
mu
-342.38
mA
3,442.65
tan(beta)
14.85
The truth!
2
M_1
-932.18
M_2
-886.12
M_3
3,290.70
mL1
3,679.41
mL3
1,543.05
mE1
2,874.53
mE3
3,785.69
mQ1
3,516.61
mQ3
2,114.27
mU1
1,057.34
mU3
1,299.92
mD1
3,012.67
mD3
2,503.29
At
-3,919.20
Ab
3,399.99
Atau
2,671.88
mu
3,228.78
mA
1,188.09
tan(beta)
14.74
The truth!
3
M_1
1,180.60
M_2
-2,757.96
M_3
2,928.34
mL1
3,120.82
mL3
2,716.76
mE1
879.45
mE3
3,515.45
mQ1
1,244.75
mQ3
2,279.06
mU1
3,014.79
mU3
1,984.20
mD1
3,796.51
mD3
590.59
At
-3,162.28
Ab
2,619.23
Atau
1,895.36
mu
150.59
mA
1,479.79
tan(beta)
34.46
The truth!
4
M_1
2,230.84
M_2
2,902.14
M_3
3,421.47
mL1
3,607.59
mL3
3,435.10
mE1
2,010.74
mE3
1,838.86
mQ1
3,282.74
mQ3
3,936.11
mU1
1,346.50
mU3
1,594.31
mD1
332.08
mD3
2,681.68
At
4,040.21
Ab
2,263.39
Atau
-1,404.53
mu
-290.01
mA
927.27
tan(beta)
23.78
The truth!
5
M_1
-3,373.43
M_2
3,169.51
M_3
1,252.16
mL1
961.80
mL3
3,907.58
mE1
716.67
mE3
2,983.50
mQ1
647.19
mQ3
3,963.58
mU1
3,466.36
mU3
3,606.16
mD1
492.45
mD3
176.01
At
-4,450.92
Ab
1,052.91
Atau
3,865.29
mu
223.84
mA
3,176.50
tan(beta)
11.94
The truth!
6
M_1
1,559.82
M_2
2,344.59
M_3
1,837.67
mL1
1,133.40
mL3
3,589.43
mE1
825.77
mE3
2,797.89
mQ1
3,339.89
mQ3
2,710.24
mU1
1,845.35
mU3
1,858.57
mD1
2,312.02
mD3
3,694.67
At
-3,730.30
Ab
-2,312.41
Atau
3,493.10
mu
136.90
mA
3,314.47
tan(beta)
42.77
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
-2,908.11
M_2
-637.88
M_3
2,953.79
mL1
854.10
mL3
3,220.32
mE1
594.64
mE3
3,215.05
mQ1
2,382.11
mQ3
2,207.62
mU1
1,052.21
mU3
1,409.72
mD1
1,401.92
mD3
3,996.15
At
-5,393.90
Ab
3,189.55
Atau
1,559.64
mu
254.05
mA
3,064.49
tan(beta)
20.35
The truth!
9
M_1
-440.47
M_2
-2,510.62
M_3
335.92
mL1
2,837.61
mL3
1,220.06
mE1
2,880.12
mE3
3,748.17
mQ1
3,377.59
mQ3
3,321.63
mU1
3,473.95
mU3
3,335.63
mD1
434.76
mD3
2,608.99
At
-5,268.62
Ab
-3,737.93
Atau
-3,328.10
mu
2,029.74
mA
1,548.43
tan(beta)
12.09
The truth!
10
M_1
2,530.47
M_2
-1,865.81
M_3
1,067.55
mL1
840.16
mL3
3,691.75
mE1
1,857.53
mE3
1,403.08
mQ1
1,866.51
mQ3
3,765.66
mU1
2,848.67
mU3
3,115.35
mD1
3,191.01
mD3
2,415.25
At
-4,091.04
Ab
-3,231.61
Atau
-3,730.75
mu
266.92
mA
3,432.88
tan(beta)
43.08
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
