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
250.20
M_2
-1,592.83
M_3
3,537.05
mL1
2,085.34
mL3
818.84
mE1
2,656.11
mE3
3,616.46
mQ1
2,215.24
mQ3
3,937.90
mU1
1,983.41
mU3
933.01
mD1
3,649.58
mD3
3,681.87
At
3,218.42
Ab
1,419.34
Atau
1,766.43
mu
-252.07
mA
1,735.19
tan(beta)
58.49
The truth!
2
M_1
-501.08
M_2
1,978.39
M_3
2,774.62
mL1
1,473.18
mL3
2,557.75
mE1
1,863.09
mE3
2,259.30
mQ1
3,077.22
mQ3
777.15
mU1
2,752.12
mU3
3,606.04
mD1
2,907.67
mD3
493.81
At
2,632.37
Ab
-2,449.17
Atau
-3,205.95
mu
-513.44
mA
1,882.02
tan(beta)
15.40
The truth!
3
M_1
1,320.05
M_2
3,138.17
M_3
1,759.92
mL1
583.29
mL3
2,525.59
mE1
3,641.97
mE3
3,956.58
mQ1
283.95
mQ3
973.75
mU1
2,036.12
mU3
2,774.86
mD1
3,509.96
mD3
3,140.05
At
2,659.51
Ab
-3,449.83
Atau
1,292.33
mu
-383.01
mA
1,154.86
tan(beta)
32.94
The truth!
4
M_1
-781.65
M_2
3,423.42
M_3
537.64
mL1
1,198.96
mL3
3,502.34
mE1
3,906.72
mE3
767.05
mQ1
1,809.88
mQ3
2,892.29
mU1
1,107.06
mU3
2,068.80
mD1
3,964.69
mD3
1,486.63
At
3,132.57
Ab
39.62
Atau
77.96
mu
-203.41
mA
2,439.94
tan(beta)
27.22
The truth!
5
M_1
1,636.06
M_2
-835.28
M_3
2,654.24
mL1
2,783.41
mL3
2,503.89
mE1
1,413.49
mE3
1,021.94
mQ1
2,798.49
mQ3
3,744.39
mU1
2,539.67
mU3
3,436.59
mD1
938.76
mD3
248.87
At
876.30
Ab
3,062.41
Atau
-1,246.91
mu
-490.71
mA
3,032.77
tan(beta)
47.80
The truth!
6
M_1
-884.70
M_2
1,060.60
M_3
3,201.40
mL1
2,627.31
mL3
1,989.18
mE1
3,872.41
mE3
1,885.42
mQ1
1,334.92
mQ3
1,332.15
mU1
3,605.27
mU3
1,192.26
mD1
2,252.27
mD3
2,714.02
At
-1,977.69
Ab
-14.55
Atau
343.16
mu
3,153.55
mA
1,587.39
tan(beta)
21.04
The truth!
7
M_1
-266.05
M_2
3,942.36
M_3
1,641.19
mL1
2,676.33
mL3
1,836.00
mE1
3,846.84
mE3
3,068.64
mQ1
828.79
mQ3
1,148.30
mU1
1,382.82
mU3
3,418.30
mD1
3,283.42
mD3
208.16
At
-2,937.67
Ab
1,868.35
Atau
1,193.35
mu
274.29
mA
1,692.92
tan(beta)
44.80
The truth!
8
M_1
620.55
M_2
-653.64
M_3
2,508.40
mL1
3,608.18
mL3
3,123.82
mE1
2,883.87
mE3
1,564.78
mQ1
638.75
mQ3
650.48
mU1
854.75
mU3
3,064.96
mD1
3,673.71
mD3
1,576.29
At
-2,554.81
Ab
-3,148.44
Atau
-2,834.08
mu
1,070.89
mA
1,584.35
tan(beta)
22.35
The truth!
9
M_1
-1,529.42
M_2
-1,253.84
M_3
3,329.01
mL1
3,130.99
mL3
2,725.40
mE1
3,587.79
mE3
1,436.32
mQ1
2,904.83
mQ3
2,891.62
mU1
743.50
mU3
2,798.08
mD1
1,966.46
mD3
726.32
At
6,681.24
Ab
-691.52
Atau
154.76
mu
147.49
mA
1,281.25
tan(beta)
3.96
The truth!
10
M_1
3,735.28
M_2
-276.99
M_3
3,493.33
mL1
1,012.85
mL3
3,910.15
mE1
1,669.69
mE3
521.09
mQ1
3,834.59
mQ3
3,047.73
mU1
2,883.71
mU3
2,920.53
mD1
1,627.29
mD3
2,220.16
At
3,659.90
Ab
-134.37
Atau
805.25
mu
-3,994.61
mA
1,024.69
tan(beta)
14.04
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
