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
-781.65
M_2
3,423.42
M_3
537.64
mL1
1,198.96
mL3
3,502.34
mE1
3,906.72
mE3
767.05
mQ1
1,809.88
mQ3
2,892.29
mU1
1,107.06
mU3
2,068.80
mD1
3,964.69
mD3
1,486.63
At
3,132.57
Ab
39.62
Atau
77.96
mu
-203.41
mA
2,439.94
tan(beta)
27.22
The truth!
2
M_1
-716.06
M_2
2,475.16
M_3
1,020.65
mL1
2,337.36
mL3
1,629.81
mE1
3,226.72
mE3
2,595.54
mQ1
737.99
mQ3
2,013.76
mU1
3,010.51
mU3
3,334.70
mD1
2,182.26
mD3
3,812.15
At
4,010.01
Ab
-3,894.58
Atau
1,880.86
mu
-545.94
mA
2,400.73
tan(beta)
24.51
The truth!
3
M_1
1,963.82
M_2
304.41
M_3
1,312.61
mL1
2,711.48
mL3
997.19
mE1
3,884.97
mE3
1,726.69
mQ1
3,983.33
mQ3
3,954.72
mU1
875.56
mU3
1,956.43
mD1
3,588.85
mD3
2,408.43
At
-4,005.35
Ab
693.81
Atau
3,831.34
mu
3,877.36
mA
3,450.22
tan(beta)
20.75
The truth!
4
M_1
-422.68
M_2
-1,598.68
M_3
714.37
mL1
2,202.18
mL3
3,812.41
mE1
2,838.90
mE3
3,470.35
mQ1
2,529.18
mQ3
2,928.47
mU1
2,063.85
mU3
2,377.96
mD1
330.26
mD3
1,752.33
At
3,132.20
Ab
67.11
Atau
-413.70
mu
1,014.76
mA
3,480.23
tan(beta)
13.09
The truth!
5
M_1
147.18
M_2
622.95
M_3
1,512.02
mL1
2,757.74
mL3
1,032.83
mE1
664.24
mE3
3,266.68
mQ1
366.68
mQ3
3,373.16
mU1
2,504.84
mU3
3,305.62
mD1
2,938.84
mD3
1,329.66
At
4,451.93
Ab
1,057.22
Atau
-2,428.35
mu
-171.39
mA
2,196.60
tan(beta)
11.80
The truth!
6
M_1
-431.56
M_2
409.71
M_3
1,709.06
mL1
1,587.50
mL3
865.74
mE1
1,398.87
mE3
3,382.01
mQ1
2,223.31
mQ3
2,202.53
mU1
2,476.79
mU3
1,979.42
mD1
3,223.91
mD3
203.49
At
-3,796.55
Ab
-95.09
Atau
158.27
mu
-1,934.34
mA
3,864.92
tan(beta)
13.50
The truth!
7
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!
8
M_1
-3,261.05
M_2
557.36
M_3
3,484.83
mL1
762.06
mL3
2,921.65
mE1
2,162.48
mE3
3,676.47
mQ1
2,433.51
mQ3
3,110.58
mU1
1,683.82
mU3
2,113.16
mD1
995.66
mD3
2,148.22
At
-2,851.07
Ab
-2,039.32
Atau
1,826.68
mu
1,289.94
mA
1,701.35
tan(beta)
21.79
The truth!
9
M_1
145.96
M_2
3,497.08
M_3
1,737.71
mL1
2,437.97
mL3
3,817.32
mE1
3,465.95
mE3
1,656.78
mQ1
290.43
mQ3
2,958.74
mU1
3,876.00
mU3
2,784.62
mD1
1,085.02
mD3
1,031.01
At
3,772.06
Ab
-2,538.91
Atau
3,804.66
mu
-163.49
mA
1,644.14
tan(beta)
35.67
The truth!
10
M_1
3,468.92
M_2
905.59
M_3
2,398.43
mL1
3,182.49
mL3
1,366.97
mE1
1,664.56
mE3
1,048.63
mQ1
3,907.29
mQ3
2,816.39
mU1
2,524.13
mU3
2,508.75
mD1
2,441.34
mD3
295.43
At
-3,678.76
Ab
2,217.03
Atau
474.78
mu
-482.29
mA
2,158.07
tan(beta)
52.94
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
