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
-1,398.41
M_2
-974.86
M_3
1,939.72
mL1
3,851.71
mL3
952.05
mE1
1,563.16
mE3
560.87
mQ1
204.73
mQ3
2,158.40
mU1
1,438.36
mU3
3,690.63
mD1
3,807.16
mD3
756.19
At
-2,842.64
Ab
170.85
Atau
-3,043.68
mu
439.39
mA
1,906.94
tan(beta)
13.02
The truth!
3
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!
4
M_1
-621.20
M_2
-1,175.16
M_3
3,851.73
mL1
3,491.95
mL3
3,942.65
mE1
3,370.65
mE3
2,328.25
mQ1
1,796.20
mQ3
2,740.23
mU1
2,566.23
mU3
3,829.76
mD1
1,011.04
mD3
626.85
At
2,674.69
Ab
-2,635.56
Atau
2,865.49
mu
-655.96
mA
2,176.93
tan(beta)
47.49
The truth!
5
M_1
659.72
M_2
-2,261.73
M_3
3,090.13
mL1
3,172.41
mL3
1,468.62
mE1
2,316.32
mE3
1,036.56
mQ1
3,377.16
mQ3
3,520.09
mU1
2,958.11
mU3
3,657.13
mD1
737.33
mD3
2,368.61
At
5,098.75
Ab
1,338.31
Atau
-1,604.79
mu
-1,043.57
mA
1,411.93
tan(beta)
10.55
The truth!
6
M_1
1,572.12
M_2
-197.46
M_3
2,029.42
mL1
1,292.98
mL3
1,923.02
mE1
2,643.52
mE3
3,672.51
mQ1
1,123.47
mQ3
3,243.95
mU1
3,227.24
mU3
1,971.96
mD1
1,559.58
mD3
579.23
At
3,159.71
Ab
2,588.69
Atau
-1,948.60
mu
-3,748.06
mA
2,769.00
tan(beta)
14.13
The truth!
7
M_1
2,539.25
M_2
2,562.99
M_3
3,285.09
mL1
390.77
mL3
513.49
mE1
3,466.22
mE3
3,260.93
mQ1
739.06
mQ3
3,839.29
mU1
2,702.28
mU3
2,950.69
mD1
3,789.84
mD3
3,689.78
At
-3,163.19
Ab
-1,357.81
Atau
-487.13
mu
-248.95
mA
2,927.66
tan(beta)
57.44
The truth!
8
M_1
1,899.37
M_2
-2,707.82
M_3
2,608.66
mL1
2,866.23
mL3
1,872.93
mE1
1,841.46
mE3
3,619.34
mQ1
444.52
mQ3
3,814.09
mU1
973.71
mU3
3,195.46
mD1
759.40
mD3
2,531.64
At
-4,859.13
Ab
2,923.10
Atau
-1,543.04
mu
-388.70
mA
2,240.49
tan(beta)
20.23
The truth!
9
M_1
49.78
M_2
2,865.33
M_3
1,552.90
mL1
1,386.40
mL3
2,825.43
mE1
3,655.40
mE3
2,011.38
mQ1
1,299.57
mQ3
2,431.77
mU1
1,206.09
mU3
3,085.83
mD1
540.37
mD3
625.89
At
-2,845.14
Ab
929.01
Atau
3,163.63
mu
-226.95
mA
3,643.60
tan(beta)
45.13
The truth!
10
M_1
3,526.52
M_2
718.16
M_3
3,426.70
mL1
3,146.19
mL3
511.34
mE1
2,829.46
mE3
2,988.67
mQ1
3,148.74
mQ3
2,397.27
mU1
1,492.89
mU3
2,117.46
mD1
3,691.05
mD3
3,394.95
At
-2,839.95
Ab
-3,624.34
Atau
514.91
mu
248.96
mA
2,003.81
tan(beta)
50.18
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
