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
382.39
M_2
-2,177.05
M_3
891.44
mL1
1,965.76
mL3
2,369.65
mE1
2,093.48
mE3
3,824.57
mQ1
838.94
mQ3
1,752.53
mU1
259.93
mU3
3,976.18
mD1
713.21
mD3
319.99
At
-4,098.87
Ab
-3,254.21
Atau
3,433.30
mu
650.84
mA
2,417.23
tan(beta)
12.91
The truth!
2
M_1
879.31
M_2
-1,405.79
M_3
767.42
mL1
2,180.58
mL3
2,913.66
mE1
2,103.29
mE3
3,263.79
mQ1
2,617.14
mQ3
3,725.08
mU1
2,178.39
mU3
2,312.35
mD1
3,294.29
mD3
3,196.96
At
-3,469.89
Ab
1,493.88
Atau
-1,841.11
mu
823.06
mA
2,806.88
tan(beta)
22.09
The truth!
3
M_1
-2,646.10
M_2
1,399.36
M_3
3,023.78
mL1
2,026.07
mL3
2,686.25
mE1
877.36
mE3
3,774.44
mQ1
2,748.46
mQ3
2,868.30
mU1
1,025.25
mU3
3,947.74
mD1
3,723.01
mD3
511.62
At
4,853.83
Ab
1,400.12
Atau
-1,150.92
mu
632.54
mA
3,855.30
tan(beta)
5.63
The truth!
4
M_1
-394.11
M_2
3,667.31
M_3
1,365.56
mL1
1,333.50
mL3
3,023.04
mE1
3,690.20
mE3
1,369.17
mQ1
1,456.08
mQ3
3,743.69
mU1
2,296.07
mU3
2,363.41
mD1
1,057.49
mD3
1,425.39
At
-3,297.83
Ab
-2,702.18
Atau
-2,742.43
mu
-385.18
mA
2,598.36
tan(beta)
21.52
The truth!
5
M_1
3,109.69
M_2
-112.56
M_3
2,752.52
mL1
228.21
mL3
2,953.57
mE1
1,327.25
mE3
2,823.67
mQ1
1,087.28
mQ3
1,534.41
mU1
2,788.02
mU3
2,582.24
mD1
2,560.56
mD3
2,543.42
At
3,057.94
Ab
-3,247.85
Atau
1,644.12
mu
-1,519.34
mA
3,615.08
tan(beta)
23.06
The truth!
6
M_1
1,305.60
M_2
-2,928.97
M_3
2,388.53
mL1
1,350.46
mL3
3,306.67
mE1
1,502.05
mE3
1,876.71
mQ1
1,477.19
mQ3
3,761.42
mU1
1,181.41
mU3
1,308.58
mD1
2,384.18
mD3
3,603.06
At
3,938.22
Ab
-1,175.64
Atau
-3,060.47
mu
-944.33
mA
3,289.00
tan(beta)
6.91
The truth!
7
M_1
64.60
M_2
3,792.70
M_3
3,323.39
mL1
1,723.33
mL3
3,181.45
mE1
1,802.00
mE3
3,667.43
mQ1
984.98
mQ3
3,645.26
mU1
2,938.71
mU3
3,761.41
mD1
3,816.60
mD3
512.06
At
3,839.00
Ab
-2,998.95
Atau
-2,615.54
mu
371.60
mA
2,190.09
tan(beta)
22.07
The truth!
8
M_1
2,097.93
M_2
2,326.12
M_3
2,212.31
mL1
3,837.82
mL3
1,364.77
mE1
1,263.12
mE3
3,161.43
mQ1
1,762.75
mQ3
2,925.20
mU1
2,993.60
mU3
2,706.02
mD1
561.74
mD3
655.96
At
-4,337.65
Ab
1,773.55
Atau
-1,134.72
mu
-681.64
mA
2,644.60
tan(beta)
44.05
The truth!
9
M_1
2,185.25
M_2
-3,838.42
M_3
2,402.94
mL1
3,465.11
mL3
2,952.10
mE1
233.09
mE3
1,052.24
mQ1
2,500.86
mQ3
3,198.17
mU1
3,004.94
mU3
2,598.33
mD1
3,490.80
mD3
1,066.14
At
-3,226.77
Ab
1,453.98
Atau
1,759.05
mu
-103.66
mA
2,294.61
tan(beta)
22.27
The truth!
10
M_1
-2,611.46
M_2
-3,219.57
M_3
1,350.38
mL1
3,297.93
mL3
2,765.66
mE1
823.89
mE3
1,900.02
mQ1
1,080.64
mQ3
3,478.20
mU1
257.45
mU3
3,926.75
mD1
3,697.56
mD3
259.81
At
-3,218.12
Ab
1,833.32
Atau
-730.40
mu
419.91
mA
2,756.03
tan(beta)
32.63
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
