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
1,290.44
M_2
446.69
M_3
1,591.18
mL1
3,809.19
mL3
2,530.85
mE1
3,440.50
mE3
1,783.18
mQ1
3,373.30
mQ3
3,095.33
mU1
2,069.91
mU3
3,680.15
mD1
377.07
mD3
3,339.19
At
-3,929.05
Ab
-965.60
Atau
-1,724.85
mu
777.63
mA
3,625.03
tan(beta)
25.14
The truth!
2
M_1
-442.61
M_2
1,559.92
M_3
1,387.40
mL1
1,543.72
mL3
547.33
mE1
1,228.45
mE3
562.69
mQ1
337.18
mQ3
1,353.62
mU1
699.86
mU3
1,647.11
mD1
3,025.78
mD3
1,380.44
At
2,442.88
Ab
-2,417.26
Atau
-3,954.72
mu
1,161.34
mA
2,630.14
tan(beta)
27.73
The truth!
3
M_1
-3,028.88
M_2
-313.94
M_3
2,239.41
mL1
2,557.54
mL3
1,932.98
mE1
2,857.47
mE3
403.44
mQ1
2,822.05
mQ3
1,475.65
mU1
3,635.51
mU3
3,081.26
mD1
1,774.99
mD3
2,615.11
At
3,098.93
Ab
-2,092.99
Atau
3,687.93
mu
3,154.29
mA
3,385.83
tan(beta)
18.76
The truth!
4
M_1
1,344.51
M_2
-3,815.23
M_3
3,578.27
mL1
2,940.75
mL3
1,115.36
mE1
3,358.07
mE3
3,966.86
mQ1
3,146.52
mQ3
1,652.51
mU1
671.29
mU3
2,827.79
mD1
1,913.34
mD3
3,261.46
At
3,356.60
Ab
2,047.21
Atau
2,539.99
mu
-119.79
mA
513.32
tan(beta)
36.67
The truth!
5
M_1
-2,465.45
M_2
288.69
M_3
2,538.52
mL1
3,019.77
mL3
2,663.19
mE1
2,818.55
mE3
2,924.84
mQ1
817.81
mQ3
908.99
mU1
2,984.79
mU3
3,092.74
mD1
3,660.72
mD3
2,987.30
At
2,813.37
Ab
681.22
Atau
-1,113.86
mu
-922.42
mA
956.61
tan(beta)
20.24
The truth!
6
M_1
3,975.34
M_2
226.10
M_3
3,947.65
mL1
2,249.33
mL3
2,236.18
mE1
3,998.36
mE3
3,370.67
mQ1
3,652.44
mQ3
3,963.52
mU1
325.22
mU3
3,097.17
mD1
735.99
mD3
2,046.78
At
-3,967.49
Ab
-2,026.05
Atau
572.99
mu
1,193.19
mA
2,280.07
tan(beta)
42.67
The truth!
7
M_1
-941.10
M_2
-3,562.43
M_3
2,419.15
mL1
1,004.18
mL3
2,482.24
mE1
2,128.03
mE3
1,910.48
mQ1
629.78
mQ3
1,922.62
mU1
1,111.78
mU3
1,563.06
mD1
3,085.87
mD3
2,711.80
At
-4,882.22
Ab
-3,846.93
Atau
-2,616.41
mu
234.70
mA
1,200.77
tan(beta)
24.66
The truth!
8
M_1
-3,705.74
M_2
-535.24
M_3
2,519.42
mL1
3,871.18
mL3
3,505.97
mE1
2,207.62
mE3
3,690.37
mQ1
937.78
mQ3
3,791.77
mU1
3,371.39
mU3
3,582.26
mD1
2,460.55
mD3
182.86
At
-2,825.16
Ab
2,286.28
Atau
2,272.16
mu
1,641.06
mA
3,396.54
tan(beta)
26.15
The truth!
9
M_1
1,474.89
M_2
-632.30
M_3
2,505.22
mL1
3,842.15
mL3
889.31
mE1
2,585.48
mE3
978.13
mQ1
2,901.75
mQ3
3,283.93
mU1
3,377.64
mU3
3,821.48
mD1
768.58
mD3
2,256.21
At
3,592.02
Ab
1,520.09
Atau
-3,872.14
mu
514.63
mA
3,009.64
tan(beta)
37.96
The truth!
10
M_1
-738.04
M_2
-3,980.17
M_3
3,862.87
mL1
3,075.59
mL3
3,704.88
mE1
1,720.73
mE3
1,014.17
mQ1
330.05
mQ3
2,734.03
mU1
512.14
mU3
3,961.80
mD1
1,433.29
mD3
3,669.73
At
4,127.03
Ab
2,452.18
Atau
2,638.84
mu
-3,078.85
mA
1,734.03
tan(beta)
35.97
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
