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
1,200.09
M_2
3,317.85
M_3
2,813.07
mL1
2,695.26
mL3
3,467.45
mE1
3,926.26
mE3
690.34
mQ1
1,324.47
mQ3
1,016.24
mU1
852.80
mU3
3,808.60
mD1
2,224.91
mD3
2,234.53
At
4,025.83
Ab
-2,207.53
Atau
2,367.96
mu
-354.51
mA
968.54
tan(beta)
10.36
The truth!
2
M_1
2,773.37
M_2
1,362.08
M_3
1,491.05
mL1
2,791.73
mL3
3,173.70
mE1
3,246.68
mE3
1,110.86
mQ1
2,052.43
mQ3
639.57
mU1
1,336.27
mU3
3,641.67
mD1
2,768.13
mD3
527.70
At
-4,889.45
Ab
-2,658.50
Atau
-1,713.30
mu
592.86
mA
1,248.82
tan(beta)
4.11
The truth!
3
M_1
-2,838.80
M_2
-2,334.98
M_3
2,669.25
mL1
3,428.53
mL3
2,645.19
mE1
2,823.61
mE3
2,317.10
mQ1
2,488.08
mQ3
3,989.24
mU1
3,297.30
mU3
2,747.09
mD1
3,531.03
mD3
2,947.79
At
-3,659.75
Ab
3,607.50
Atau
2,408.02
mu
632.21
mA
3,643.56
tan(beta)
49.51
The truth!
4
M_1
3,560.01
M_2
-366.85
M_3
1,970.11
mL1
3,258.69
mL3
3,236.27
mE1
2,068.06
mE3
3,352.66
mQ1
1,826.30
mQ3
766.83
mU1
2,633.80
mU3
2,519.57
mD1
3,090.65
mD3
1,444.55
At
-3,000.11
Ab
-2,443.52
Atau
2,703.98
mu
3,327.53
mA
3,195.74
tan(beta)
37.00
The truth!
5
M_1
547.94
M_2
3,201.95
M_3
416.19
mL1
2,652.22
mL3
3,934.40
mE1
642.45
mE3
2,316.54
mQ1
916.62
mQ3
3,866.04
mU1
2,638.75
mU3
3,711.82
mD1
2,504.46
mD3
907.63
At
-5,895.05
Ab
-3,848.84
Atau
3,273.62
mu
-803.64
mA
2,012.27
tan(beta)
9.29
The truth!
6
M_1
-302.70
M_2
-423.38
M_3
639.67
mL1
2,588.37
mL3
1,234.93
mE1
1,120.71
mE3
2,333.45
mQ1
234.49
mQ3
1,538.66
mU1
2,421.49
mU3
1,968.37
mD1
2,569.21
mD3
498.31
At
-2,973.31
Ab
-3,234.50
Atau
-1,231.46
mu
1,982.04
mA
3,527.68
tan(beta)
11.45
The truth!
7
M_1
2,249.80
M_2
3,417.56
M_3
3,982.28
mL1
3,492.91
mL3
3,877.70
mE1
3,828.80
mE3
1,405.25
mQ1
1,934.10
mQ3
3,277.15
mU1
3,772.19
mU3
3,490.29
mD1
371.48
mD3
1,104.29
At
-3,141.99
Ab
-1,989.12
Atau
-2,415.18
mu
337.96
mA
2,448.19
tan(beta)
52.93
The truth!
8
M_1
-46.72
M_2
2,469.40
M_3
3,811.88
mL1
279.85
mL3
3,959.32
mE1
3,693.07
mE3
3,530.45
mQ1
1,367.76
mQ3
2,952.65
mU1
537.33
mU3
2,131.56
mD1
1,227.75
mD3
3,227.82
At
-4,179.70
Ab
-3,643.28
Atau
316.92
mu
257.92
mA
1,925.22
tan(beta)
57.34
The truth!
9
M_1
-604.26
M_2
2,314.55
M_3
2,344.92
mL1
2,635.56
mL3
877.70
mE1
1,096.45
mE3
1,821.81
mQ1
3,186.63
mQ3
2,352.03
mU1
511.78
mU3
1,588.43
mD1
3,654.23
mD3
899.26
At
3,533.70
Ab
-1,885.43
Atau
-424.76
mu
-3,454.76
mA
1,812.19
tan(beta)
19.92
The truth!
10
M_1
-1,038.19
M_2
-1,662.68
M_3
3,277.52
mL1
568.67
mL3
1,898.78
mE1
1,007.80
mE3
2,417.17
mQ1
3,349.00
mQ3
2,594.42
mU1
3,761.85
mU3
3,876.84
mD1
1,298.71
mD3
3,753.28
At
3,802.26
Ab
-2,451.06
Atau
1,329.27
mu
481.80
mA
3,907.25
tan(beta)
43.20
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
