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
-3,651.31
M_2
-1,499.40
M_3
2,523.86
mL1
3,746.72
mL3
1,696.85
mE1
580.01
mE3
1,236.05
mQ1
2,492.01
mQ3
3,234.51
mU1
2,407.69
mU3
3,896.84
mD1
1,467.00
mD3
3,646.73
At
4,554.27
Ab
3,387.40
Atau
3,672.75
mu
562.65
mA
3,918.02
tan(beta)
27.86
The truth!
2
M_1
-1,214.23
M_2
706.86
M_3
1,676.70
mL1
2,374.11
mL3
3,700.75
mE1
3,553.53
mE3
744.34
mQ1
2,700.53
mQ3
1,157.95
mU1
1,855.38
mU3
3,914.31
mD1
1,484.38
mD3
3,600.80
At
-2,968.37
Ab
2,293.12
Atau
1,641.20
mu
648.77
mA
3,306.89
tan(beta)
33.34
The truth!
3
M_1
-1,679.62
M_2
-473.87
M_3
927.58
mL1
3,114.39
mL3
3,065.70
mE1
2,316.96
mE3
399.11
mQ1
3,412.24
mQ3
2,062.29
mU1
2,109.99
mU3
3,696.27
mD1
2,322.04
mD3
2,583.54
At
-2,765.87
Ab
-2,207.76
Atau
-3,158.63
mu
-235.31
mA
3,746.19
tan(beta)
12.60
The truth!
4
M_1
3,149.16
M_2
1,807.36
M_3
1,515.98
mL1
479.67
mL3
901.18
mE1
1,347.74
mE3
2,859.76
mQ1
550.46
mQ3
1,917.19
mU1
264.73
mU3
3,371.59
mD1
1,032.17
mD3
734.68
At
-4,670.18
Ab
-1,265.97
Atau
-2,951.68
mu
-186.81
mA
2,461.70
tan(beta)
11.79
The truth!
5
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!
6
M_1
1,244.19
M_2
-2,052.85
M_3
2,772.03
mL1
2,870.85
mL3
2,734.40
mE1
2,382.08
mE3
898.44
mQ1
2,901.58
mQ3
2,304.40
mU1
1,028.99
mU3
1,413.08
mD1
580.52
mD3
2,610.83
At
3,310.16
Ab
2,507.78
Atau
-3,171.90
mu
492.23
mA
3,170.78
tan(beta)
9.38
The truth!
7
M_1
853.27
M_2
-817.02
M_3
3,266.59
mL1
2,847.28
mL3
2,524.18
mE1
3,859.60
mE3
3,063.43
mQ1
3,643.46
mQ3
2,716.63
mU1
1,858.35
mU3
1,454.94
mD1
1,239.27
mD3
2,740.79
At
-3,903.78
Ab
3,917.23
Atau
649.76
mu
2,690.89
mA
3,450.15
tan(beta)
34.33
The truth!
8
M_1
1,877.75
M_2
-3,497.50
M_3
3,334.31
mL1
341.13
mL3
552.67
mE1
2,635.25
mE3
2,173.44
mQ1
2,292.23
mQ3
3,448.96
mU1
2,066.12
mU3
2,885.23
mD1
3,914.23
mD3
2,448.98
At
-3,367.86
Ab
-2,511.76
Atau
-1,012.57
mu
342.67
mA
2,382.06
tan(beta)
51.76
The truth!
9
M_1
2,778.77
M_2
159.86
M_3
1,539.45
mL1
3,525.69
mL3
2,086.90
mE1
1,280.90
mE3
3,010.49
mQ1
2,067.91
mQ3
3,035.86
mU1
1,535.49
mU3
2,198.39
mD1
1,582.98
mD3
2,109.15
At
2,959.36
Ab
-1,804.43
Atau
-2,251.91
mu
907.86
mA
2,848.36
tan(beta)
31.37
The truth!
10
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!
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
