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
-590.65
M_2
-2,632.15
M_3
3,499.00
mL1
1,972.59
mL3
1,972.23
mE1
637.18
mE3
3,275.26
mQ1
3,492.73
mQ3
1,921.11
mU1
1,551.87
mU3
2,043.19
mD1
734.38
mD3
632.31
At
3,067.94
Ab
-705.24
Atau
-2,894.52
mu
3,000.59
mA
2,921.11
tan(beta)
29.41
The truth!
2
M_1
370.57
M_2
2,549.35
M_3
341.47
mL1
3,067.90
mL3
702.85
mE1
3,944.66
mE3
3,211.03
mQ1
2,591.50
mQ3
295.97
mU1
1,977.40
mU3
2,825.50
mD1
1,699.73
mD3
555.58
At
-2,578.07
Ab
-3,125.17
Atau
-353.12
mu
919.79
mA
1,887.97
tan(beta)
18.69
The truth!
3
M_1
1,877.73
M_2
-3,078.52
M_3
2,131.99
mL1
2,697.62
mL3
2,754.45
mE1
2,951.63
mE3
3,395.69
mQ1
2,091.18
mQ3
3,570.81
mU1
2,779.08
mU3
3,935.12
mD1
3,645.88
mD3
2,869.04
At
3,985.82
Ab
-688.28
Atau
-274.19
mu
-1,952.80
mA
3,955.00
tan(beta)
10.45
The truth!
4
M_1
1,439.03
M_2
-3,962.93
M_3
3,696.47
mL1
3,178.76
mL3
3,858.81
mE1
2,864.50
mE3
3,604.26
mQ1
654.68
mQ3
3,908.27
mU1
2,861.62
mU3
3,886.83
mD1
1,947.73
mD3
1,650.81
At
-4,365.09
Ab
-2,622.23
Atau
2,152.06
mu
157.87
mA
1,729.02
tan(beta)
31.28
The truth!
5
M_1
-326.54
M_2
3,859.85
M_3
2,719.63
mL1
3,463.70
mL3
3,457.39
mE1
3,725.64
mE3
2,081.75
mQ1
2,411.08
mQ3
2,683.05
mU1
3,367.44
mU3
3,228.86
mD1
3,936.17
mD3
2,690.05
At
-3,015.07
Ab
2,047.24
Atau
-1,899.09
mu
-118.56
mA
704.54
tan(beta)
29.89
The truth!
6
M_1
239.59
M_2
-1,839.53
M_3
2,557.65
mL1
618.08
mL3
1,443.64
mE1
144.16
mE3
1,945.23
mQ1
906.31
mQ3
3,104.46
mU1
3,238.35
mU3
3,706.91
mD1
562.80
mD3
3,015.07
At
-4,230.37
Ab
3,997.99
Atau
-1,330.78
mu
264.88
mA
1,597.13
tan(beta)
46.45
The truth!
7
M_1
-150.83
M_2
-3,683.99
M_3
3,034.99
mL1
2,665.78
mL3
2,159.17
mE1
3,860.04
mE3
3,918.70
mQ1
1,042.01
mQ3
2,208.48
mU1
563.06
mU3
2,618.89
mD1
3,678.29
mD3
2,016.04
At
4,104.26
Ab
220.41
Atau
-3,943.25
mu
-164.97
mA
2,505.91
tan(beta)
10.45
The truth!
8
M_1
-2,588.97
M_2
-2,408.05
M_3
3,302.17
mL1
418.36
mL3
1,585.10
mE1
2,877.33
mE3
3,165.43
mQ1
632.90
mQ3
1,540.28
mU1
3,637.50
mU3
1,962.77
mD1
3,598.79
mD3
2,499.34
At
2,546.09
Ab
-1,765.07
Atau
255.71
mu
-432.53
mA
2,896.88
tan(beta)
32.74
The truth!
9
M_1
-850.01
M_2
-919.45
M_3
1,451.51
mL1
1,478.83
mL3
3,266.37
mE1
2,097.39
mE3
1,032.41
mQ1
934.58
mQ3
3,199.68
mU1
1,880.73
mU3
3,949.67
mD1
3,530.86
mD3
3,532.85
At
3,000.16
Ab
2,534.35
Atau
1,681.55
mu
-685.05
mA
2,642.06
tan(beta)
30.04
The truth!
10
M_1
-647.58
M_2
-2,686.09
M_3
631.93
mL1
1,976.54
mL3
3,282.76
mE1
2,624.08
mE3
2,006.51
mQ1
3,242.71
mQ3
1,172.83
mU1
609.77
mU3
2,612.47
mD1
2,804.45
mD3
1,238.21
At
-2,729.41
Ab
-40.21
Atau
1,408.66
mu
-1,097.16
mA
3,879.07
tan(beta)
18.15
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
