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
-620.55
M_2
1,016.48
M_3
568.83
mL1
3,302.13
mL3
3,650.16
mE1
1,987.74
mE3
2,546.30
mQ1
3,849.70
mQ3
3,100.09
mU1
3,716.49
mU3
3,920.49
mD1
3,092.28
mD3
3,319.05
At
-1,727.82
Ab
2,664.96
Atau
-3,199.95
mu
-637.64
mA
605.91
tan(beta)
53.19
The truth!
2
M_1
-3,590.32
M_2
3,242.78
M_3
2,278.99
mL1
2,186.35
mL3
2,289.62
mE1
1,134.63
mE3
1,328.71
mQ1
2,292.53
mQ3
1,152.78
mU1
1,916.51
mU3
1,908.92
mD1
2,244.55
mD3
1,130.96
At
2,267.89
Ab
-3,412.00
Atau
-3,327.96
mu
-154.19
mA
843.34
tan(beta)
24.45
The truth!
3
M_1
988.90
M_2
1,532.22
M_3
1,604.28
mL1
2,095.10
mL3
1,519.83
mE1
2,288.08
mE3
2,590.07
mQ1
2,491.82
mQ3
3,360.05
mU1
2,213.95
mU3
3,498.22
mD1
3,452.34
mD3
1,812.51
At
3,565.03
Ab
2,827.71
Atau
2,789.14
mu
-1,120.83
mA
1,901.42
tan(beta)
14.37
The truth!
4
M_1
-955.81
M_2
2,561.53
M_3
1,950.63
mL1
1,646.76
mL3
3,937.67
mE1
1,845.18
mE3
3,849.16
mQ1
1,004.48
mQ3
2,175.85
mU1
800.90
mU3
3,001.77
mD1
2,056.06
mD3
1,123.82
At
-3,692.00
Ab
-1,768.29
Atau
946.52
mu
-1,715.61
mA
3,656.98
tan(beta)
12.00
The truth!
5
M_1
-1,007.79
M_2
-3,635.05
M_3
3,919.25
mL1
3,799.21
mL3
3,616.75
mE1
1,463.47
mE3
1,763.87
mQ1
608.89
mQ3
3,960.61
mU1
1,713.69
mU3
3,851.52
mD1
1,424.04
mD3
636.53
At
-5,075.25
Ab
2,718.46
Atau
2,173.46
mu
385.33
mA
2,486.07
tan(beta)
16.13
The truth!
6
M_1
-568.39
M_2
3,907.71
M_3
454.19
mL1
1,957.55
mL3
1,155.44
mE1
561.46
mE3
2,477.63
mQ1
2,785.83
mQ3
1,214.00
mU1
1,636.56
mU3
3,568.34
mD1
3,816.02
mD3
2,269.91
At
-3,130.52
Ab
-1,339.12
Atau
252.43
mu
667.62
mA
1,594.66
tan(beta)
24.80
The truth!
7
M_1
-451.96
M_2
1,893.35
M_3
3,055.46
mL1
622.55
mL3
3,495.60
mE1
1,074.23
mE3
3,302.87
mQ1
3,167.58
mQ3
2,400.65
mU1
462.62
mU3
1,356.56
mD1
1,669.82
mD3
3,653.43
At
3,261.24
Ab
-3,063.13
Atau
-2,384.51
mu
-1,277.38
mA
1,653.60
tan(beta)
23.66
The truth!
8
M_1
3,526.52
M_2
718.16
M_3
3,426.70
mL1
3,146.19
mL3
511.34
mE1
2,829.46
mE3
2,988.67
mQ1
3,148.74
mQ3
2,397.27
mU1
1,492.89
mU3
2,117.46
mD1
3,691.05
mD3
3,394.95
At
-2,839.95
Ab
-3,624.34
Atau
514.91
mu
248.96
mA
2,003.81
tan(beta)
50.18
The truth!
9
M_1
290.28
M_2
-293.50
M_3
1,192.12
mL1
3,322.77
mL3
1,156.79
mE1
3,958.97
mE3
1,395.05
mQ1
2,423.42
mQ3
1,774.99
mU1
733.60
mU3
3,990.20
mD1
341.74
mD3
2,960.41
At
-2,992.36
Ab
12.60
Atau
1,602.51
mu
503.94
mA
930.89
tan(beta)
43.16
The truth!
10
M_1
-327.73
M_2
3,958.86
M_3
2,770.04
mL1
2,527.44
mL3
2,535.93
mE1
2,523.64
mE3
2,334.48
mQ1
564.58
mQ3
2,119.84
mU1
2,894.08
mU3
2,562.56
mD1
2,095.68
mD3
2,421.79
At
-3,279.09
Ab
-2,661.46
Atau
-1,707.31
mu
-336.97
mA
2,128.04
tan(beta)
44.38
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
