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
2,914.03
M_2
-509.06
M_3
3,940.37
mL1
1,239.04
mL3
2,932.30
mE1
2,610.01
mE3
2,047.21
mQ1
2,236.36
mQ3
2,920.00
mU1
1,614.68
mU3
3,064.24
mD1
476.07
mD3
1,051.37
At
-3,782.73
Ab
-420.19
Atau
2,902.25
mu
774.14
mA
1,409.97
tan(beta)
36.19
The truth!
2
M_1
-601.39
M_2
-3,091.51
M_3
2,426.81
mL1
1,012.24
mL3
3,747.08
mE1
2,970.65
mE3
3,578.47
mQ1
1,298.95
mQ3
2,065.89
mU1
1,387.38
mU3
1,606.53
mD1
2,390.57
mD3
491.78
At
3,141.07
Ab
2,105.21
Atau
2,359.90
mu
2,063.80
mA
3,167.15
tan(beta)
32.65
The truth!
3
M_1
-505.89
M_2
467.80
M_3
882.57
mL1
2,433.18
mL3
2,623.39
mE1
3,662.28
mE3
1,196.48
mQ1
3,915.83
mQ3
2,597.23
mU1
3,759.60
mU3
2,002.11
mD1
3,218.49
mD3
3,409.07
At
3,353.82
Ab
-652.20
Atau
-2,631.25
mu
-1,956.53
mA
3,311.64
tan(beta)
4.95
The truth!
4
M_1
-1,734.00
M_2
2,061.73
M_3
1,585.27
mL1
1,504.32
mL3
1,623.43
mE1
3,645.83
mE3
1,146.68
mQ1
768.25
mQ3
2,725.13
mU1
378.75
mU3
3,865.35
mD1
599.86
mD3
2,516.20
At
4,414.24
Ab
3,106.45
Atau
3,846.04
mu
-118.04
mA
3,516.36
tan(beta)
42.52
The truth!
5
M_1
712.68
M_2
1,669.69
M_3
1,189.84
mL1
1,346.29
mL3
1,790.62
mE1
3,393.41
mE3
1,874.50
mQ1
616.34
mQ3
1,407.44
mU1
2,275.76
mU3
3,888.08
mD1
1,961.50
mD3
1,129.22
At
-3,471.82
Ab
3,419.87
Atau
-1,080.12
mu
3,147.28
mA
2,514.73
tan(beta)
32.94
The truth!
6
M_1
-580.97
M_2
553.03
M_3
1,822.09
mL1
3,893.58
mL3
674.34
mE1
1,437.11
mE3
2,296.76
mQ1
1,726.02
mQ3
3,961.41
mU1
932.29
mU3
3,013.50
mD1
928.16
mD3
3,229.00
At
3,930.58
Ab
274.91
Atau
1,950.69
mu
-1,239.58
mA
1,822.11
tan(beta)
8.06
The truth!
7
M_1
-2,456.28
M_2
3,319.81
M_3
2,091.59
mL1
933.63
mL3
1,605.02
mE1
2,947.71
mE3
2,871.35
mQ1
2,032.68
mQ3
3,080.70
mU1
408.95
mU3
3,160.48
mD1
575.48
mD3
414.38
At
-4,266.83
Ab
-917.33
Atau
-1,682.60
mu
-249.68
mA
3,904.99
tan(beta)
30.80
The truth!
8
M_1
1,406.82
M_2
479.11
M_3
2,752.75
mL1
1,237.56
mL3
3,397.80
mE1
855.43
mE3
1,051.00
mQ1
3,298.53
mQ3
1,714.20
mU1
3,423.92
mU3
3,461.49
mD1
298.39
mD3
1,780.29
At
-4,891.53
Ab
-3,732.95
Atau
826.74
mu
-1,515.66
mA
2,327.56
tan(beta)
6.50
The truth!
9
M_1
787.69
M_2
1,846.44
M_3
3,079.49
mL1
3,934.83
mL3
2,222.51
mE1
2,398.82
mE3
3,423.65
mQ1
3,482.92
mQ3
2,376.16
mU1
1,627.67
mU3
2,130.96
mD1
2,494.31
mD3
869.62
At
-3,836.29
Ab
-1,918.11
Atau
-1,948.26
mu
757.59
mA
3,307.75
tan(beta)
48.18
The truth!
10
M_1
498.99
M_2
872.89
M_3
1,691.15
mL1
2,052.85
mL3
787.10
mE1
2,905.38
mE3
2,402.03
mQ1
1,370.91
mQ3
1,056.24
mU1
335.67
mU3
3,619.18
mD1
953.39
mD3
3,559.62
At
3,394.48
Ab
413.85
Atau
-2,398.76
mu
-1,926.01
mA
2,193.44
tan(beta)
33.46
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
