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
3,025.06
M_2
-3,124.89
M_3
1,519.35
mL1
1,244.75
mL3
1,322.27
mE1
646.30
mE3
3,918.51
mQ1
1,699.88
mQ3
2,968.96
mU1
3,617.54
mU3
2,597.90
mD1
3,177.06
mD3
1,760.40
At
3,069.65
Ab
3,516.89
Atau
-1,337.49
mu
-182.01
mA
3,455.80
tan(beta)
58.95
The truth!
2
M_1
-1,088.01
M_2
1,019.94
M_3
2,503.56
mL1
1,194.87
mL3
1,597.38
mE1
3,167.17
mE3
2,577.79
mQ1
2,923.87
mQ3
3,200.84
mU1
2,460.17
mU3
3,522.86
mD1
2,748.63
mD3
2,596.07
At
2,912.01
Ab
291.40
Atau
-313.21
mu
-285.07
mA
867.97
tan(beta)
32.74
The truth!
3
M_1
1,270.04
M_2
185.88
M_3
3,528.71
mL1
3,638.42
mL3
1,564.39
mE1
2,670.22
mE3
620.03
mQ1
3,397.97
mQ3
2,813.77
mU1
1,361.18
mU3
1,210.27
mD1
935.38
mD3
2,586.63
At
-4,760.02
Ab
-3,260.05
Atau
-2,385.15
mu
3,985.47
mA
3,646.01
tan(beta)
29.01
The truth!
4
M_1
218.88
M_2
-2,063.94
M_3
3,287.97
mL1
1,118.38
mL3
835.51
mE1
1,135.01
mE3
3,628.24
mQ1
426.86
mQ3
2,489.12
mU1
3,240.91
mU3
3,111.47
mD1
3,249.86
mD3
652.31
At
2,971.47
Ab
-1,487.05
Atau
-3,180.98
mu
-163.44
mA
2,781.27
tan(beta)
30.05
The truth!
5
M_1
1,067.62
M_2
-1,059.65
M_3
2,232.30
mL1
3,656.31
mL3
1,497.99
mE1
3,390.50
mE3
2,061.13
mQ1
3,307.44
mQ3
3,671.64
mU1
425.69
mU3
2,500.14
mD1
1,303.80
mD3
321.84
At
-3,843.00
Ab
1,680.85
Atau
1,120.42
mu
159.41
mA
3,388.95
tan(beta)
10.54
The truth!
6
M_1
2,067.55
M_2
-3,458.26
M_3
789.35
mL1
1,588.49
mL3
936.97
mE1
1,859.05
mE3
3,344.69
mQ1
1,315.34
mQ3
3,043.49
mU1
1,974.44
mU3
2,507.36
mD1
579.46
mD3
1,848.21
At
3,068.75
Ab
-793.33
Atau
2,595.01
mu
-177.74
mA
3,139.43
tan(beta)
33.87
The truth!
7
M_1
-2,908.11
M_2
-637.88
M_3
2,953.79
mL1
854.10
mL3
3,220.32
mE1
594.64
mE3
3,215.05
mQ1
2,382.11
mQ3
2,207.62
mU1
1,052.21
mU3
1,409.72
mD1
1,401.92
mD3
3,996.15
At
-5,393.90
Ab
3,189.55
Atau
1,559.64
mu
254.05
mA
3,064.49
tan(beta)
20.35
The truth!
8
M_1
2,182.49
M_2
1,566.80
M_3
1,842.05
mL1
2,903.14
mL3
142.69
mE1
509.60
mE3
417.34
mQ1
1,293.46
mQ3
3,990.61
mU1
2,435.30
mU3
2,941.36
mD1
752.77
mD3
902.05
At
2,838.02
Ab
1,076.04
Atau
2,434.83
mu
-131.74
mA
1,532.70
tan(beta)
36.00
The truth!
9
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!
10
M_1
592.33
M_2
-2,990.22
M_3
657.94
mL1
855.54
mL3
1,191.69
mE1
3,185.48
mE3
1,874.14
mQ1
1,874.45
mQ3
3,305.30
mU1
2,834.60
mU3
2,406.97
mD1
3,961.13
mD3
767.37
At
3,100.04
Ab
745.78
Atau
-1,505.87
mu
-534.89
mA
1,045.62
tan(beta)
36.60
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
