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
875.13
M_2
-938.43
M_3
1,575.11
mL1
961.51
mL3
3,918.74
mE1
1,097.38
mE3
1,955.03
mQ1
1,283.42
mQ3
766.16
mU1
1,437.77
mU3
2,726.24
mD1
3,571.87
mD3
1,430.44
At
3,177.05
Ab
-705.44
Atau
3,571.69
mu
2,069.47
mA
2,392.38
tan(beta)
10.61
The truth!
2
M_1
3,123.06
M_2
-256.44
M_3
1,228.54
mL1
3,263.53
mL3
2,107.76
mE1
2,740.54
mE3
1,236.85
mQ1
3,589.73
mQ3
468.49
mU1
3,682.34
mU3
3,814.63
mD1
3,063.98
mD3
3,353.92
At
-3,375.83
Ab
-2,687.98
Atau
3,837.51
mu
3,782.89
mA
4,664.84
tan(beta)
26.05
The truth!
3
M_1
-1,894.40
M_2
-3,713.20
M_3
3,607.66
mL1
1,725.58
mL3
3,109.44
mE1
2,336.25
mE3
1,556.47
mQ1
702.60
mQ3
2,649.70
mU1
1,527.57
mU3
3,220.60
mD1
1,964.58
mD3
2,996.35
At
-4,619.87
Ab
-3,210.59
Atau
2,837.47
mu
218.60
mA
1,478.49
tan(beta)
26.59
The truth!
4
M_1
867.51
M_2
829.56
M_3
2,192.66
mL1
3,940.34
mL3
1,886.21
mE1
1,090.05
mE3
1,428.69
mQ1
2,459.05
mQ3
3,460.72
mU1
2,873.29
mU3
2,000.86
mD1
2,157.46
mD3
1,848.20
At
3,561.78
Ab
-3,552.18
Atau
3,287.79
mu
-1,512.62
mA
3,983.53
tan(beta)
14.90
The truth!
5
M_1
-419.96
M_2
-327.24
M_3
1,164.41
mL1
1,086.47
mL3
3,061.80
mE1
2,406.24
mE3
2,903.13
mQ1
3,663.17
mQ3
3,053.78
mU1
217.19
mU3
3,085.11
mD1
403.97
mD3
3,766.63
At
-3,149.36
Ab
2,540.91
Atau
-3,902.41
mu
3,524.86
mA
2,203.32
tan(beta)
9.25
The truth!
6
M_1
925.00
M_2
1,667.77
M_3
782.25
mL1
2,496.74
mL3
3,204.84
mE1
3,012.18
mE3
2,819.29
mQ1
2,361.34
mQ3
3,719.08
mU1
2,170.41
mU3
3,764.95
mD1
1,349.88
mD3
2,344.35
At
4,516.52
Ab
-1,834.45
Atau
2,673.79
mu
-2,737.05
mA
1,289.12
tan(beta)
22.94
The truth!
7
M_1
-3,172.86
M_2
936.23
M_3
2,819.91
mL1
2,279.79
mL3
2,692.76
mE1
3,981.59
mE3
1,970.45
mQ1
3,926.64
mQ3
1,763.29
mU1
1,805.32
mU3
2,352.21
mD1
1,845.08
mD3
1,909.91
At
-3,155.22
Ab
2,425.93
Atau
1,966.99
mu
-2,757.77
mA
3,413.93
tan(beta)
14.49
The truth!
8
M_1
-664.43
M_2
-3,024.31
M_3
3,577.68
mL1
1,599.39
mL3
759.29
mE1
3,693.98
mE3
923.42
mQ1
2,069.15
mQ3
3,150.23
mU1
454.44
mU3
2,358.80
mD1
2,870.42
mD3
1,115.30
At
4,941.16
Ab
-618.63
Atau
-1,104.95
mu
-1,769.14
mA
1,464.34
tan(beta)
3.98
The truth!
9
M_1
-138.79
M_2
204.45
M_3
818.76
mL1
2,790.04
mL3
2,740.62
mE1
806.88
mE3
557.20
mQ1
3,682.08
mQ3
2,708.54
mU1
2,949.96
mU3
2,932.82
mD1
3,092.68
mD3
2,614.58
At
-3,785.43
Ab
-3,296.33
Atau
-831.42
mu
183.97
mA
1,143.72
tan(beta)
8.03
The truth!
10
M_1
-1,453.50
M_2
256.81
M_3
1,663.94
mL1
2,465.96
mL3
3,702.24
mE1
3,973.65
mE3
853.92
mQ1
1,531.38
mQ3
1,190.27
mU1
2,533.03
mU3
1,979.35
mD1
2,133.35
mD3
2,519.05
At
3,518.39
Ab
3,932.36
Atau
3,936.27
mu
1,401.94
mA
2,297.98
tan(beta)
4.25
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
