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
3,080.08
M_2
1,015.62
M_3
2,031.97
mL1
903.57
mL3
1,302.41
mE1
1,833.38
mE3
1,916.17
mQ1
1,545.58
mQ3
3,618.53
mU1
3,804.50
mU3
1,192.33
mD1
828.41
mD3
3,062.21
At
-3,353.57
Ab
-507.83
Atau
2,321.86
mu
123.20
mA
2,471.29
tan(beta)
30.76
The truth!
2
M_1
-912.92
M_2
3,737.79
M_3
917.47
mL1
3,104.89
mL3
2,574.94
mE1
3,565.71
mE3
3,481.06
mQ1
2,142.19
mQ3
3,524.38
mU1
1,785.24
mU3
2,508.05
mD1
2,916.57
mD3
861.18
At
-3,292.55
Ab
3,558.65
Atau
-1,820.48
mu
3,224.68
mA
2,438.78
tan(beta)
27.76
The truth!
3
M_1
-494.13
M_2
1,361.78
M_3
2,219.23
mL1
1,220.45
mL3
3,673.02
mE1
1,696.28
mE3
1,768.07
mQ1
1,289.86
mQ3
1,167.81
mU1
2,486.38
mU3
624.97
mD1
1,633.49
mD3
3,339.16
At
1,477.87
Ab
2,859.85
Atau
-287.89
mu
-1,157.57
mA
2,974.26
tan(beta)
15.74
The truth!
4
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!
5
M_1
-1,858.52
M_2
-2,432.99
M_3
3,530.48
mL1
507.44
mL3
2,269.51
mE1
1,851.22
mE3
2,522.07
mQ1
925.91
mQ3
2,927.80
mU1
3,559.60
mU3
2,719.27
mD1
3,959.14
mD3
3,311.93
At
3,142.30
Ab
551.54
Atau
-3,104.32
mu
-471.89
mA
2,987.24
tan(beta)
23.35
The truth!
6
M_1
2,904.82
M_2
779.68
M_3
2,511.44
mL1
659.80
mL3
986.90
mE1
2,230.58
mE3
970.68
mQ1
3,058.94
mQ3
2,272.00
mU1
658.33
mU3
2,983.12
mD1
3,387.97
mD3
2,639.81
At
-3,366.07
Ab
670.51
Atau
610.49
mu
255.25
mA
2,616.44
tan(beta)
37.27
The truth!
7
M_1
62.91
M_2
3,169.75
M_3
2,914.80
mL1
1,887.83
mL3
2,748.09
mE1
3,194.78
mE3
2,083.18
mQ1
1,067.79
mQ3
2,585.23
mU1
769.56
mU3
3,359.90
mD1
3,614.71
mD3
3,602.08
At
-3,424.94
Ab
1,211.42
Atau
-2,585.65
mu
-170.35
mA
1,324.62
tan(beta)
49.86
The truth!
8
M_1
-1,453.50
M_2
256.81
M_3
1,663.94
mL1
2,465.96
mL3
3,702.24
mE1
3,973.65
mE3
853.92
mQ1
1,531.38
mQ3
1,190.27
mU1
2,533.03
mU3
1,979.35
mD1
2,133.35
mD3
2,519.05
At
3,518.39
Ab
3,932.36
Atau
3,936.27
mu
1,401.94
mA
2,297.98
tan(beta)
4.25
The truth!
9
M_1
313.57
M_2
-3,397.21
M_3
2,255.33
mL1
2,690.77
mL3
3,486.13
mE1
1,675.64
mE3
3,072.44
mQ1
1,781.79
mQ3
1,093.19
mU1
2,989.39
mU3
767.35
mD1
508.91
mD3
1,914.71
At
2,111.30
Ab
-1,024.59
Atau
3,764.03
mu
2,003.24
mA
2,713.47
tan(beta)
14.81
The truth!
10
M_1
-2,338.84
M_2
-269.27
M_3
3,305.30
mL1
1,131.59
mL3
1,396.25
mE1
2,687.53
mE3
3,143.53
mQ1
653.33
mQ3
1,349.03
mU1
236.33
mU3
3,160.53
mD1
3,056.73
mD3
1,147.75
At
-6,097.58
Ab
778.86
Atau
1,879.47
mu
1,134.99
mA
2,694.82
tan(beta)
9.13
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
