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
978.07
M_2
-2,389.02
M_3
1,028.09
mL1
947.19
mL3
1,351.79
mE1
795.31
mE3
3,174.48
mQ1
2,501.44
mQ3
2,445.22
mU1
289.57
mU3
3,350.34
mD1
2,794.94
mD3
2,042.13
At
3,132.76
Ab
3,517.63
Atau
-3,035.29
mu
295.46
mA
1,914.82
tan(beta)
29.71
The truth!
2
M_1
-252.97
M_2
1,163.57
M_3
2,427.52
mL1
3,070.30
mL3
828.06
mE1
2,729.96
mE3
1,847.22
mQ1
3,793.53
mQ3
1,553.02
mU1
911.66
mU3
2,733.82
mD1
1,396.64
mD3
3,699.82
At
-4,539.17
Ab
2,774.47
Atau
-731.00
mu
3,422.53
mA
598.50
tan(beta)
9.64
The truth!
3
M_1
2,773.37
M_2
1,362.08
M_3
1,491.05
mL1
2,791.73
mL3
3,173.70
mE1
3,246.68
mE3
1,110.86
mQ1
2,052.43
mQ3
639.57
mU1
1,336.27
mU3
3,641.67
mD1
2,768.13
mD3
527.70
At
-4,889.45
Ab
-2,658.50
Atau
-1,713.30
mu
592.86
mA
1,248.82
tan(beta)
4.11
The truth!
4
M_1
3,063.59
M_2
-219.24
M_3
3,147.11
mL1
3,908.60
mL3
2,630.93
mE1
2,751.61
mE3
509.42
mQ1
959.72
mQ3
1,010.29
mU1
3,216.16
mU3
2,683.68
mD1
911.95
mD3
1,888.62
At
-2,399.77
Ab
3,329.62
Atau
3,136.22
mu
1,344.82
mA
3,091.16
tan(beta)
48.82
The truth!
5
M_1
-1,124.63
M_2
184.22
M_3
3,218.17
mL1
3,981.45
mL3
3,152.66
mE1
2,958.97
mE3
2,542.81
mQ1
3,826.38
mQ3
3,297.19
mU1
1,109.96
mU3
3,288.74
mD1
366.15
mD3
2,226.65
At
-1,932.59
Ab
3,448.24
Atau
-834.54
mu
1,050.21
mA
2,308.12
tan(beta)
36.59
The truth!
6
M_1
-1,105.27
M_2
522.58
M_3
1,172.75
mL1
3,143.71
mL3
931.52
mE1
2,531.04
mE3
1,144.62
mQ1
1,348.01
mQ3
3,024.22
mU1
1,363.72
mU3
3,857.46
mD1
2,099.73
mD3
1,364.52
At
-4,459.23
Ab
-1,660.61
Atau
-1,484.57
mu
3,717.93
mA
1,533.22
tan(beta)
38.58
The truth!
7
M_1
3,435.37
M_2
955.25
M_3
3,416.39
mL1
3,112.13
mL3
3,034.10
mE1
1,911.90
mE3
1,102.72
mQ1
2,260.99
mQ3
3,119.34
mU1
3,764.02
mU3
2,995.76
mD1
3,464.58
mD3
3,511.19
At
4,296.71
Ab
-423.42
Atau
-2,017.62
mu
-181.70
mA
1,877.29
tan(beta)
23.19
The truth!
8
M_1
2,538.64
M_2
126.27
M_3
1,102.76
mL1
1,901.21
mL3
3,856.08
mE1
705.14
mE3
3,426.26
mQ1
1,508.78
mQ3
2,906.43
mU1
770.63
mU3
2,309.70
mD1
3,999.52
mD3
3,626.78
At
2,673.18
Ab
-1,822.31
Atau
-2,434.30
mu
-3,867.51
mA
3,607.65
tan(beta)
13.48
The truth!
9
M_1
272.76
M_2
-3,969.64
M_3
3,016.49
mL1
2,272.74
mL3
3,181.80
mE1
992.50
mE3
569.84
mQ1
2,900.37
mQ3
2,928.12
mU1
1,158.14
mU3
3,843.75
mD1
2,532.85
mD3
765.36
At
3,872.65
Ab
3,468.17
Atau
-427.14
mu
216.69
mA
2,946.69
tan(beta)
15.12
The truth!
10
M_1
779.47
M_2
1,498.80
M_3
624.23
mL1
2,133.46
mL3
2,444.67
mE1
3,200.72
mE3
2,964.42
mQ1
888.59
mQ3
3,952.70
mU1
3,699.39
mU3
3,837.23
mD1
2,207.15
mD3
1,830.43
At
-4,819.46
Ab
673.79
Atau
-2,553.51
mu
2,023.27
mA
3,706.66
tan(beta)
41.24
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
