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
-3,176.91
M_2
3,087.63
M_3
3,909.03
mL1
3,194.02
mL3
3,091.42
mE1
1,724.86
mE3
1,867.95
mQ1
3,135.60
mQ3
2,540.91
mU1
1,503.46
mU3
3,367.89
mD1
3,769.28
mD3
1,499.44
At
-3,955.18
Ab
-338.49
Atau
52.05
mu
-294.16
mA
2,199.33
tan(beta)
35.88
The truth!
2
M_1
-3,866.41
M_2
141.20
M_3
2,095.40
mL1
2,541.59
mL3
2,363.10
mE1
2,658.90
mE3
2,841.12
mQ1
2,460.32
mQ3
3,696.23
mU1
850.54
mU3
3,542.39
mD1
3,114.84
mD3
781.28
At
-3,703.46
Ab
-913.97
Atau
-562.19
mu
3,814.15
mA
2,811.81
tan(beta)
52.81
The truth!
3
M_1
-1,679.62
M_2
-473.87
M_3
927.58
mL1
3,114.39
mL3
3,065.70
mE1
2,316.96
mE3
399.11
mQ1
3,412.24
mQ3
2,062.29
mU1
2,109.99
mU3
3,696.27
mD1
2,322.04
mD3
2,583.54
At
-2,765.87
Ab
-2,207.76
Atau
-3,158.63
mu
-235.31
mA
3,746.19
tan(beta)
12.60
The truth!
4
M_1
1,518.53
M_2
-2,880.53
M_3
3,616.77
mL1
796.43
mL3
1,732.61
mE1
3,079.72
mE3
1,042.94
mQ1
3,192.40
mQ3
3,897.99
mU1
2,058.41
mU3
1,869.50
mD1
2,979.87
mD3
3,018.18
At
4,119.91
Ab
2,368.57
Atau
-65.99
mu
418.73
mA
2,720.49
tan(beta)
37.96
The truth!
5
M_1
-530.28
M_2
-2,058.47
M_3
712.36
mL1
2,113.19
mL3
3,967.25
mE1
1,801.97
mE3
1,410.29
mQ1
3,231.80
mQ3
2,867.53
mU1
3,228.18
mU3
2,908.93
mD1
1,438.29
mD3
625.84
At
3,308.30
Ab
-1,093.98
Atau
3,566.32
mu
-1,421.34
mA
1,488.14
tan(beta)
29.23
The truth!
6
M_1
650.78
M_2
640.90
M_3
3,499.24
mL1
3,007.57
mL3
2,258.15
mE1
3,923.34
mE3
3,755.89
mQ1
3,536.64
mQ3
1,504.36
mU1
3,330.50
mU3
2,973.09
mD1
1,891.33
mD3
1,061.37
At
-3,325.37
Ab
2,197.46
Atau
1,792.14
mu
1,124.18
mA
2,693.27
tan(beta)
9.07
The truth!
7
M_1
781.59
M_2
-1,949.53
M_3
1,064.92
mL1
3,469.36
mL3
2,267.71
mE1
1,746.78
mE3
3,436.14
mQ1
1,567.95
mQ3
727.86
mU1
2,715.08
mU3
3,142.62
mD1
2,861.56
mD3
3,832.18
At
-2,708.02
Ab
443.87
Atau
-1,185.53
mu
3,742.80
mA
2,565.58
tan(beta)
9.41
The truth!
8
M_1
-2,998.68
M_2
3,580.68
M_3
3,902.96
mL1
982.40
mL3
2,168.46
mE1
1,130.02
mE3
2,014.11
mQ1
3,829.36
mQ3
3,510.72
mU1
1,726.75
mU3
2,415.32
mD1
2,598.31
mD3
3,090.98
At
4,291.24
Ab
-1,986.07
Atau
3,900.36
mu
-185.67
mA
3,219.91
tan(beta)
59.50
The truth!
9
M_1
-495.72
M_2
1,761.40
M_3
3,990.06
mL1
1,100.11
mL3
1,556.19
mE1
1,965.23
mE3
655.63
mQ1
1,545.86
mQ3
3,393.81
mU1
663.48
mU3
1,191.27
mD1
1,294.30
mD3
3,880.07
At
-5,354.93
Ab
2,238.61
Atau
2,391.29
mu
937.89
mA
1,159.60
tan(beta)
7.75
The truth!
10
M_1
2,481.93
M_2
3,636.16
M_3
3,077.74
mL1
505.54
mL3
1,950.93
mE1
3,813.17
mE3
2,810.09
mQ1
3,122.62
mQ3
2,951.72
mU1
1,371.23
mU3
3,915.85
mD1
3,035.21
mD3
2,738.51
At
-5,889.05
Ab
-907.02
Atau
1,259.39
mu
312.08
mA
1,580.93
tan(beta)
7.36
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
