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
-72.66
M_2
-469.88
M_3
2,787.73
mL1
358.08
mL3
2,177.03
mE1
3,104.27
mE3
2,859.19
mQ1
2,340.52
mQ3
3,531.24
mU1
2,323.18
mU3
2,508.02
mD1
393.08
mD3
2,408.60
At
3,031.48
Ab
-191.49
Atau
-1,225.42
mu
-117.02
mA
3,489.39
tan(beta)
17.81
The truth!
2
M_1
3,560.59
M_2
617.85
M_3
1,399.73
mL1
1,404.43
mL3
1,232.36
mE1
1,367.45
mE3
913.07
mQ1
2,055.62
mQ3
653.05
mU1
3,218.03
mU3
3,924.76
mD1
1,822.15
mD3
3,102.59
At
-3,607.39
Ab
1,074.85
Atau
539.68
mu
2,843.52
mA
2,524.30
tan(beta)
22.35
The truth!
3
M_1
-1,072.84
M_2
-246.73
M_3
2,578.06
mL1
1,675.32
mL3
1,385.26
mE1
2,916.44
mE3
1,775.01
mQ1
919.47
mQ3
1,909.29
mU1
3,445.49
mU3
3,901.61
mD1
1,434.30
mD3
2,633.29
At
-4,149.88
Ab
580.01
Atau
982.71
mu
2,225.51
mA
1,661.92
tan(beta)
33.38
The truth!
4
M_1
970.87
M_2
-1,369.10
M_3
1,330.74
mL1
3,717.82
mL3
2,976.09
mE1
2,784.52
mE3
1,052.82
mQ1
3,015.37
mQ3
2,806.00
mU1
3,510.32
mU3
2,745.97
mD1
1,590.91
mD3
874.23
At
-3,974.32
Ab
620.96
Atau
-183.38
mu
2,100.80
mA
2,758.04
tan(beta)
41.01
The truth!
5
M_1
-398.66
M_2
362.38
M_3
2,938.85
mL1
1,040.01
mL3
1,086.06
mE1
3,322.01
mE3
812.45
mQ1
3,105.86
mQ3
3,142.59
mU1
3,892.29
mU3
3,449.55
mD1
3,559.40
mD3
1,056.62
At
5,273.52
Ab
-2,161.34
Atau
1,145.93
mu
1,410.24
mA
2,423.47
tan(beta)
8.15
The truth!
6
M_1
573.24
M_2
-2,164.76
M_3
1,754.22
mL1
2,695.57
mL3
3,663.27
mE1
873.54
mE3
2,570.26
mQ1
474.06
mQ3
1,221.28
mU1
2,537.83
mU3
2,401.02
mD1
1,584.31
mD3
3,989.33
At
-4,220.27
Ab
-1,244.12
Atau
-2,264.13
mu
3,223.13
mA
2,281.73
tan(beta)
28.17
The truth!
7
M_1
-647.35
M_2
2,503.75
M_3
2,078.00
mL1
1,867.37
mL3
1,291.99
mE1
2,507.62
mE3
745.73
mQ1
2,555.62
mQ3
2,865.90
mU1
2,420.50
mU3
3,557.21
mD1
1,301.63
mD3
1,330.42
At
-3,183.70
Ab
-113.54
Atau
3,050.49
mu
685.57
mA
1,557.55
tan(beta)
23.24
The truth!
8
M_1
3,415.22
M_2
271.83
M_3
2,363.69
mL1
2,001.17
mL3
3,124.05
mE1
2,390.84
mE3
3,550.92
mQ1
2,946.36
mQ3
1,349.61
mU1
2,556.01
mU3
1,933.91
mD1
1,742.71
mD3
305.87
At
-2,516.63
Ab
-3,660.53
Atau
1,384.72
mu
2,769.13
mA
1,445.49
tan(beta)
22.24
The truth!
9
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!
10
M_1
2,394.33
M_2
469.83
M_3
2,852.57
mL1
2,456.88
mL3
2,867.85
mE1
331.65
mE3
2,505.70
mQ1
3,791.69
mQ3
3,898.10
mU1
3,437.30
mU3
3,801.03
mD1
2,820.47
mD3
1,158.76
At
-2,929.76
Ab
676.91
Atau
3,274.36
mu
166.02
mA
1,030.34
tan(beta)
8.63
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
