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
-1,419.87
M_2
-328.18
M_3
3,842.08
mL1
3,327.61
mL3
1,507.13
mE1
2,728.35
mE3
3,836.24
mQ1
2,419.24
mQ3
3,047.55
mU1
3,147.40
mU3
2,336.12
mD1
3,640.88
mD3
389.23
At
-3,148.00
Ab
-2,396.85
Atau
1,745.36
mu
1,544.70
mA
3,212.58
tan(beta)
13.33
The truth!
2
M_1
3,339.91
M_2
2,249.24
M_3
3,575.30
mL1
2,356.31
mL3
3,621.97
mE1
844.03
mE3
3,056.90
mQ1
3,874.54
mQ3
3,382.43
mU1
830.45
mU3
2,055.55
mD1
2,818.30
mD3
2,871.44
At
-3,120.53
Ab
2,981.44
Atau
1,581.96
mu
650.67
mA
1,851.74
tan(beta)
16.30
The truth!
3
M_1
2,232.28
M_2
2,509.07
M_3
3,119.12
mL1
588.36
mL3
3,789.58
mE1
2,820.41
mE3
1,327.27
mQ1
3,870.80
mQ3
1,691.00
mU1
2,251.46
mU3
3,957.37
mD1
1,353.89
mD3
772.86
At
-4,494.30
Ab
-3,227.45
Atau
-2,009.57
mu
-213.89
mA
1,546.12
tan(beta)
28.43
The truth!
4
M_1
1,271.03
M_2
-1,283.24
M_3
1,505.42
mL1
1,498.64
mL3
3,028.00
mE1
2,745.21
mE3
1,319.28
mQ1
2,775.78
mQ3
3,331.43
mU1
3,410.23
mU3
2,698.00
mD1
1,158.90
mD3
3,805.28
At
-2,815.55
Ab
-675.00
Atau
335.34
mu
2,167.57
mA
2,680.99
tan(beta)
40.93
The truth!
5
M_1
-340.76
M_2
-1,158.09
M_3
2,191.09
mL1
1,940.29
mL3
1,837.62
mE1
688.25
mE3
2,875.23
mQ1
3,615.70
mQ3
2,022.51
mU1
1,879.87
mU3
1,333.07
mD1
2,838.68
mD3
2,740.36
At
-4,022.01
Ab
-3,137.30
Atau
3,367.08
mu
754.88
mA
683.20
tan(beta)
10.31
The truth!
6
M_1
311.07
M_2
-1,697.60
M_3
262.76
mL1
2,757.23
mL3
658.86
mE1
3,021.67
mE3
2,260.32
mQ1
3,140.40
mQ3
903.55
mU1
3,771.31
mU3
3,406.99
mD1
2,291.59
mD3
2,104.39
At
-2,981.92
Ab
2,062.32
Atau
3,552.09
mu
3,108.82
mA
2,835.88
tan(beta)
44.16
The truth!
7
M_1
-574.82
M_2
2,590.89
M_3
552.59
mL1
3,296.04
mL3
1,416.69
mE1
2,292.02
mE3
3,703.36
mQ1
3,738.14
mQ3
892.66
mU1
1,552.15
mU3
2,165.11
mD1
2,732.42
mD3
528.46
At
-3,107.58
Ab
-3,349.48
Atau
-1,953.79
mu
896.22
mA
3,578.28
tan(beta)
23.12
The truth!
8
M_1
-328.93
M_2
3,863.98
M_3
3,889.53
mL1
490.12
mL3
278.62
mE1
3,259.26
mE3
2,220.61
mQ1
872.32
mQ3
3,390.09
mU1
1,038.25
mU3
2,398.58
mD1
2,252.38
mD3
3,211.54
At
-4,664.47
Ab
-3,766.72
Atau
2,120.68
mu
342.82
mA
1,674.57
tan(beta)
14.75
The truth!
9
M_1
1,975.95
M_2
-1,693.05
M_3
1,149.76
mL1
424.86
mL3
1,356.87
mE1
615.20
mE3
2,032.18
mQ1
2,592.75
mQ3
820.96
mU1
1,139.96
mU3
3,214.17
mD1
2,036.27
mD3
3,453.10
At
-5,157.37
Ab
-1,906.52
Atau
761.99
mu
312.06
mA
2,052.54
tan(beta)
5.87
The truth!
10
M_1
-3,064.51
M_2
-612.83
M_3
3,397.95
mL1
2,423.87
mL3
1,088.83
mE1
2,942.96
mE3
698.84
mQ1
2,612.05
mQ3
1,337.21
mU1
3,551.92
mU3
1,900.98
mD1
3,357.51
mD3
1,378.91
At
2,781.16
Ab
-3,677.93
Atau
278.75
mu
-1,571.33
mA
2,628.33
tan(beta)
11.53
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
