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
1,354.77
M_2
2,581.33
M_3
2,596.24
mL1
1,325.26
mL3
1,825.43
mE1
688.99
mE3
1,452.85
mQ1
2,448.70
mQ3
1,449.58
mU1
314.05
mU3
2,929.29
mD1
2,957.18
mD3
1,572.68
At
-3,434.59
Ab
274.10
Atau
-186.70
mu
211.18
mA
2,794.39
tan(beta)
15.04
The truth!
2
M_1
553.86
M_2
3,728.93
M_3
1,294.34
mL1
1,888.45
mL3
3,204.86
mE1
1,383.10
mE3
837.02
mQ1
735.69
mQ3
2,154.20
mU1
404.31
mU3
3,245.12
mD1
1,191.78
mD3
1,749.18
At
-4,779.16
Ab
-3,894.66
Atau
-212.02
mu
4,041.48
mA
1,069.10
tan(beta)
22.15
The truth!
3
M_1
-3,272.07
M_2
-608.38
M_3
1,041.64
mL1
1,937.68
mL3
2,380.99
mE1
457.05
mE3
3,927.19
mQ1
876.87
mQ3
3,265.63
mU1
3,888.87
mU3
3,079.46
mD1
3,688.45
mD3
1,813.16
At
-3,295.97
Ab
-1,336.87
Atau
-913.39
mu
-350.50
mA
2,323.75
tan(beta)
13.47
The truth!
4
M_1
411.76
M_2
-828.11
M_3
2,724.13
mL1
654.06
mL3
488.18
mE1
1,728.47
mE3
2,676.66
mQ1
3,920.43
mQ3
1,406.78
mU1
375.47
mU3
2,210.02
mD1
2,095.70
mD3
2,287.25
At
-5,453.59
Ab
-365.98
Atau
3,390.59
mu
-1,801.82
mA
3,877.49
tan(beta)
13.34
The truth!
5
M_1
128.47
M_2
-2,016.64
M_3
2,682.05
mL1
1,584.21
mL3
1,081.11
mE1
3,709.27
mE3
2,824.21
mQ1
3,403.63
mQ3
3,028.50
mU1
1,705.98
mU3
3,859.17
mD1
2,074.52
mD3
3,175.00
At
-4,455.99
Ab
-2,924.15
Atau
238.98
mu
-128.06
mA
2,765.18
tan(beta)
55.31
The truth!
6
M_1
3,546.18
M_2
1,217.09
M_3
821.38
mL1
1,618.29
mL3
2,219.65
mE1
2,081.86
mE3
1,317.74
mQ1
1,520.14
mQ3
1,077.17
mU1
2,851.37
mU3
2,966.27
mD1
3,424.68
mD3
362.86
At
2,803.29
Ab
-3,060.30
Atau
-252.78
mu
190.94
mA
2,838.44
tan(beta)
20.58
The truth!
7
M_1
2,727.49
M_2
-1,476.82
M_3
2,918.83
mL1
217.45
mL3
2,578.88
mE1
2,160.20
mE3
2,280.69
mQ1
2,564.93
mQ3
1,567.98
mU1
3,936.56
mU3
3,179.59
mD1
2,560.87
mD3
1,150.66
At
-6,919.84
Ab
-1,664.73
Atau
3,305.92
mu
-104.31
mA
3,759.70
tan(beta)
31.29
The truth!
8
M_1
1,474.89
M_2
-632.30
M_3
2,505.22
mL1
3,842.15
mL3
889.31
mE1
2,585.48
mE3
978.13
mQ1
2,901.75
mQ3
3,283.93
mU1
3,377.64
mU3
3,821.48
mD1
768.58
mD3
2,256.21
At
3,592.02
Ab
1,520.09
Atau
-3,872.14
mu
514.63
mA
3,009.64
tan(beta)
37.96
The truth!
9
M_1
929.70
M_2
2,301.11
M_3
2,235.91
mL1
3,974.48
mL3
3,372.09
mE1
2,607.85
mE3
1,781.68
mQ1
899.90
mQ3
1,836.22
mU1
2,090.29
mU3
1,355.25
mD1
2,342.51
mD3
1,362.10
At
-4,300.57
Ab
1,960.52
Atau
-2,231.63
mu
-1,421.04
mA
2,799.08
tan(beta)
19.37
The truth!
10
M_1
-1,703.89
M_2
1,506.48
M_3
3,489.93
mL1
3,996.05
mL3
2,793.38
mE1
1,478.96
mE3
3,306.92
mQ1
1,923.46
mQ3
3,703.86
mU1
1,709.51
mU3
1,662.91
mD1
3,058.14
mD3
3,985.74
At
-3,898.77
Ab
-534.92
Atau
567.36
mu
-180.56
mA
2,577.24
tan(beta)
15.71
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
