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
2,761.41
M_2
787.76
M_3
2,477.35
mL1
2,331.65
mL3
2,943.11
mE1
3,401.77
mE3
2,466.80
mQ1
1,426.94
mQ3
2,907.25
mU1
3,399.42
mU3
2,976.77
mD1
3,855.40
mD3
2,137.79
At
4,170.04
Ab
-3,840.83
Atau
-303.55
mu
-1,251.96
mA
3,751.49
tan(beta)
40.68
The truth!
2
M_1
-381.65
M_2
151.97
M_3
1,950.33
mL1
3,666.91
mL3
558.01
mE1
271.90
mE3
2,100.32
mQ1
3,939.95
mQ3
3,276.26
mU1
2,694.78
mU3
1,985.90
mD1
3,550.84
mD3
3,670.82
At
3,506.13
Ab
2,411.67
Atau
205.01
mu
-1,437.75
mA
3,454.31
tan(beta)
31.18
The truth!
3
M_1
-3,614.02
M_2
-2,712.94
M_3
3,908.43
mL1
3,270.84
mL3
2,266.63
mE1
1,011.75
mE3
1,019.96
mQ1
1,629.85
mQ3
3,558.16
mU1
3,152.57
mU3
1,341.92
mD1
2,939.55
mD3
3,699.36
At
-4,215.55
Ab
-2,342.92
Atau
-2,247.09
mu
193.26
mA
3,055.17
tan(beta)
49.56
The truth!
4
M_1
-3,983.83
M_2
-1,659.24
M_3
3,639.30
mL1
2,163.58
mL3
3,981.95
mE1
403.54
mE3
987.02
mQ1
2,966.68
mQ3
3,795.38
mU1
431.47
mU3
3,347.88
mD1
601.64
mD3
485.13
At
3,560.00
Ab
-1,638.35
Atau
-572.32
mu
-176.32
mA
3,029.18
tan(beta)
42.73
The truth!
5
M_1
-250.36
M_2
-2,264.89
M_3
1,441.77
mL1
3,073.81
mL3
3,986.83
mE1
447.80
mE3
3,757.12
mQ1
1,202.64
mQ3
541.21
mU1
400.78
mU3
2,670.35
mD1
2,807.96
mD3
3,604.06
At
-3,004.84
Ab
-3,073.35
Atau
-1,911.60
mu
287.21
mA
1,078.49
tan(beta)
17.06
The truth!
6
M_1
-3,731.57
M_2
802.72
M_3
745.28
mL1
3,398.36
mL3
3,051.05
mE1
1,464.55
mE3
1,748.71
mQ1
3,465.57
mQ3
3,562.70
mU1
2,874.81
mU3
3,368.46
mD1
3,921.91
mD3
2,953.39
At
3,820.46
Ab
2,904.47
Atau
-1,384.72
mu
930.46
mA
2,979.07
tan(beta)
8.39
The truth!
7
M_1
-3,142.14
M_2
198.43
M_3
3,025.53
mL1
1,472.92
mL3
1,207.91
mE1
1,762.59
mE3
2,703.17
mQ1
1,612.09
mQ3
3,236.33
mU1
1,875.55
mU3
3,576.47
mD1
3,194.44
mD3
1,475.28
At
-7,187.62
Ab
-1,745.87
Atau
2,748.05
mu
2,750.63
mA
1,816.82
tan(beta)
4.55
The truth!
8
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!
9
M_1
-2,336.37
M_2
-136.18
M_3
3,578.93
mL1
3,009.14
mL3
1,756.68
mE1
1,465.90
mE3
621.81
mQ1
3,522.04
mQ3
3,179.60
mU1
3,435.45
mU3
2,865.52
mD1
510.82
mD3
1,384.89
At
4,398.71
Ab
-3,646.75
Atau
3,899.31
mu
-3,243.57
mA
3,579.08
tan(beta)
11.22
The truth!
10
M_1
767.92
M_2
-114.40
M_3
1,861.53
mL1
2,178.26
mL3
1,901.18
mE1
196.68
mE3
2,516.89
mQ1
630.57
mQ3
992.51
mU1
280.30
mU3
1,075.75
mD1
2,784.59
mD3
3,454.50
At
2,650.39
Ab
-649.77
Atau
-951.15
mu
3,819.11
mA
3,992.39
tan(beta)
11.84
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
