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
1,567.51
M_2
3,191.53
M_3
1,110.91
mL1
1,520.92
mL3
3,453.55
mE1
2,773.57
mE3
2,666.47
mQ1
3,635.19
mQ3
1,656.37
mU1
3,927.25
mU3
3,357.02
mD1
490.59
mD3
2,386.28
At
3,062.78
Ab
-3,901.11
Atau
2,168.79
mu
359.10
mA
3,341.28
tan(beta)
36.83
The truth!
2
M_1
-2,677.95
M_2
1,091.67
M_3
1,804.83
mL1
602.18
mL3
2,860.28
mE1
3,254.40
mE3
2,623.86
mQ1
3,997.21
mQ3
3,519.31
mU1
924.87
mU3
3,103.16
mD1
2,831.31
mD3
2,607.14
At
2,364.81
Ab
-472.41
Atau
2,935.46
mu
511.66
mA
3,316.20
tan(beta)
34.99
The truth!
3
M_1
-2,616.14
M_2
-1,738.23
M_3
3,199.54
mL1
1,424.78
mL3
2,888.02
mE1
3,551.64
mE3
1,430.58
mQ1
1,424.61
mQ3
2,401.42
mU1
1,115.27
mU3
2,680.66
mD1
1,484.34
mD3
3,083.13
At
5,325.74
Ab
-3,911.42
Atau
-1,318.06
mu
378.27
mA
3,726.18
tan(beta)
4.17
The truth!
4
M_1
61.40
M_2
945.92
M_3
2,320.26
mL1
991.32
mL3
250.11
mE1
1,127.10
mE3
2,152.65
mQ1
3,838.53
mQ3
1,814.78
mU1
3,565.36
mU3
2,920.93
mD1
2,187.94
mD3
873.44
At
-5,405.80
Ab
-2,569.90
Atau
103.35
mu
1,034.28
mA
1,632.83
tan(beta)
3.79
The truth!
5
M_1
750.07
M_2
3,520.62
M_3
3,766.51
mL1
3,138.10
mL3
1,949.54
mE1
2,558.19
mE3
2,377.93
mQ1
2,741.17
mQ3
2,550.35
mU1
1,299.30
mU3
3,552.16
mD1
516.21
mD3
2,874.97
At
4,880.02
Ab
2,117.95
Atau
-1,017.43
mu
-3,269.31
mA
3,135.74
tan(beta)
34.14
The truth!
6
M_1
595.43
M_2
3,048.58
M_3
1,901.09
mL1
3,783.60
mL3
1,978.43
mE1
2,748.64
mE3
3,876.94
mQ1
1,760.94
mQ3
1,043.78
mU1
2,367.20
mU3
1,987.63
mD1
3,191.13
mD3
3,968.89
At
3,429.48
Ab
-324.97
Atau
-1,627.01
mu
-2,005.24
mA
1,342.00
tan(beta)
4.90
The truth!
7
M_1
-3,039.13
M_2
378.29
M_3
2,252.16
mL1
1,284.01
mL3
2,611.94
mE1
2,076.07
mE3
2,571.82
mQ1
3,347.09
mQ3
3,972.30
mU1
1,876.57
mU3
2,826.82
mD1
3,738.59
mD3
3,929.43
At
3,641.79
Ab
-291.02
Atau
2,017.59
mu
-1,880.04
mA
3,454.92
tan(beta)
20.50
The truth!
8
M_1
-259.47
M_2
-3,719.40
M_3
1,005.30
mL1
1,443.69
mL3
825.16
mE1
1,753.49
mE3
3,545.48
mQ1
2,805.49
mQ3
3,763.89
mU1
1,453.71
mU3
3,852.43
mD1
2,371.82
mD3
731.98
At
-3,513.19
Ab
-1,008.05
Atau
-853.05
mu
287.58
mA
946.22
tan(beta)
18.36
The truth!
9
M_1
2,857.80
M_2
2,833.88
M_3
3,004.49
mL1
599.94
mL3
2,875.76
mE1
3,767.17
mE3
1,720.23
mQ1
1,775.71
mQ3
2,096.55
mU1
1,448.84
mU3
3,563.89
mD1
716.10
mD3
1,332.17
At
3,370.11
Ab
-294.07
Atau
-3,974.95
mu
624.22
mA
3,799.03
tan(beta)
15.30
The truth!
10
M_1
-3,069.09
M_2
3,405.13
M_3
391.90
mL1
698.47
mL3
3,829.65
mE1
2,980.51
mE3
2,894.82
mQ1
1,581.25
mQ3
1,910.00
mU1
1,122.65
mU3
3,063.03
mD1
1,941.24
mD3
1,006.54
At
3,899.88
Ab
2,090.44
Atau
-47.33
mu
141.09
mA
1,288.21
tan(beta)
5.97
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
