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
-47.13
M_2
-1,601.00
M_3
3,432.25
mL1
1,159.09
mL3
318.87
mE1
3,115.23
mE3
689.71
mQ1
2,355.37
mQ3
3,753.82
mU1
1,941.01
mU3
824.42
mD1
3,511.76
mD3
3,292.12
At
-3,491.48
Ab
-3,125.51
Atau
-1,578.30
mu
285.76
mA
3,693.96
tan(beta)
17.07
The truth!
2
M_1
1,472.00
M_2
799.45
M_3
608.31
mL1
3,451.14
mL3
663.99
mE1
1,291.42
mE3
1,554.26
mQ1
631.28
mQ3
1,488.14
mU1
2,953.63
mU3
3,521.98
mD1
2,998.39
mD3
1,503.47
At
-3,706.33
Ab
-3,325.28
Atau
-1,318.72
mu
-550.93
mA
3,879.01
tan(beta)
21.57
The truth!
3
M_1
-3,539.94
M_2
-1,432.49
M_3
3,294.85
mL1
1,887.59
mL3
2,078.62
mE1
3,728.53
mE3
2,039.24
mQ1
2,073.46
mQ3
3,874.98
mU1
1,430.96
mU3
2,761.01
mD1
3,777.51
mD3
1,490.97
At
-4,594.67
Ab
-3,690.61
Atau
1,909.71
mu
1,494.78
mA
3,587.12
tan(beta)
41.84
The truth!
4
M_1
3,357.77
M_2
325.98
M_3
3,187.74
mL1
3,838.85
mL3
3,641.55
mE1
829.90
mE3
3,642.25
mQ1
530.03
mQ3
2,715.86
mU1
1,552.90
mU3
2,751.11
mD1
3,364.17
mD3
886.77
At
-3,420.70
Ab
1,566.75
Atau
-3,522.93
mu
2,519.34
mA
3,348.45
tan(beta)
40.29
The truth!
5
M_1
1,059.65
M_2
679.47
M_3
2,743.76
mL1
1,176.61
mL3
3,511.06
mE1
2,457.58
mE3
2,449.83
mQ1
1,290.11
mQ3
2,606.91
mU1
885.49
mU3
1,509.24
mD1
3,280.51
mD3
2,816.08
At
-3,585.79
Ab
-1,577.47
Atau
638.63
mu
1,761.88
mA
3,493.33
tan(beta)
49.11
The truth!
6
M_1
-3,290.71
M_2
3,365.39
M_3
1,018.69
mL1
3,115.67
mL3
1,858.69
mE1
3,350.29
mE3
1,632.68
mQ1
1,502.94
mQ3
3,881.14
mU1
621.46
mU3
2,717.58
mD1
1,633.22
mD3
2,946.24
At
4,709.69
Ab
-2,867.29
Atau
-2,602.03
mu
408.71
mA
3,321.21
tan(beta)
20.13
The truth!
7
M_1
218.88
M_2
-2,063.94
M_3
3,287.97
mL1
1,118.38
mL3
835.51
mE1
1,135.01
mE3
3,628.24
mQ1
426.86
mQ3
2,489.12
mU1
3,240.91
mU3
3,111.47
mD1
3,249.86
mD3
652.31
At
2,971.47
Ab
-1,487.05
Atau
-3,180.98
mu
-163.44
mA
2,781.27
tan(beta)
30.05
The truth!
8
M_1
221.67
M_2
209.84
M_3
2,832.74
mL1
2,103.03
mL3
2,564.16
mE1
3,486.15
mE3
3,660.77
mQ1
3,060.94
mQ3
1,095.42
mU1
2,904.74
mU3
3,603.97
mD1
3,645.86
mD3
2,791.78
At
3,971.88
Ab
-2,338.88
Atau
3,152.22
mu
-3,985.56
mA
1,240.18
tan(beta)
5.08
The truth!
9
M_1
542.18
M_2
2,473.54
M_3
3,115.58
mL1
2,359.34
mL3
760.63
mE1
1,210.67
mE3
1,940.16
mQ1
2,780.67
mQ3
2,847.53
mU1
2,101.90
mU3
3,150.62
mD1
3,973.51
mD3
2,600.75
At
-4,781.42
Ab
-654.92
Atau
-1,296.47
mu
-421.99
mA
3,171.04
tan(beta)
15.02
The truth!
10
M_1
3,635.90
M_2
-3,024.03
M_3
1,998.55
mL1
3,055.51
mL3
3,957.04
mE1
1,516.70
mE3
1,372.18
mQ1
447.02
mQ3
2,553.90
mU1
1,493.87
mU3
3,976.99
mD1
3,612.43
mD3
2,072.44
At
-3,897.65
Ab
455.67
Atau
-512.16
mu
-288.73
mA
2,310.19
tan(beta)
29.51
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
