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
-2,214.95
M_2
2,811.56
M_3
3,528.71
mL1
953.22
mL3
3,592.23
mE1
3,900.92
mE3
3,214.97
mQ1
3,988.61
mQ3
3,284.02
mU1
302.18
mU3
3,185.92
mD1
3,769.34
mD3
437.39
At
3,753.89
Ab
-40.91
Atau
-955.59
mu
-337.30
mA
2,707.49
tan(beta)
24.23
The truth!
2
M_1
2,484.94
M_2
2,498.06
M_3
3,263.40
mL1
1,124.59
mL3
2,708.02
mE1
3,018.31
mE3
1,461.65
mQ1
2,541.94
mQ3
2,067.30
mU1
1,783.18
mU3
1,386.54
mD1
939.32
mD3
2,270.91
At
2,893.76
Ab
-3,379.69
Atau
713.13
mu
-754.38
mA
2,139.14
tan(beta)
39.45
The truth!
3
M_1
1,134.70
M_2
2,413.30
M_3
866.80
mL1
1,382.57
mL3
3,686.84
mE1
2,313.77
mE3
1,486.44
mQ1
2,887.81
mQ3
2,162.36
mU1
2,593.13
mU3
1,536.68
mD1
335.62
mD3
2,063.88
At
2,944.77
Ab
3,710.08
Atau
2,579.99
mu
-279.93
mA
1,400.00
tan(beta)
10.08
The truth!
4
M_1
580.57
M_2
1,154.13
M_3
3,898.17
mL1
699.23
mL3
527.74
mE1
3,023.49
mE3
1,996.04
mQ1
2,380.64
mQ3
3,136.00
mU1
231.15
mU3
2,941.76
mD1
2,319.57
mD3
3,789.71
At
-6,347.31
Ab
494.80
Atau
-1,937.47
mu
1,329.43
mA
3,983.39
tan(beta)
5.57
The truth!
5
M_1
315.13
M_2
316.17
M_3
2,677.54
mL1
2,197.19
mL3
441.16
mE1
2,915.02
mE3
2,927.92
mQ1
1,487.44
mQ3
1,744.59
mU1
621.46
mU3
2,928.77
mD1
527.53
mD3
487.60
At
-3,757.37
Ab
-378.22
Atau
-2,053.18
mu
2,226.74
mA
3,401.86
tan(beta)
34.73
The truth!
6
M_1
394.82
M_2
-219.23
M_3
3,462.06
mL1
3,438.79
mL3
1,233.29
mE1
1,522.51
mE3
2,442.34
mQ1
3,086.05
mQ3
3,925.49
mU1
2,777.90
mU3
3,780.05
mD1
3,170.39
mD3
2,967.60
At
-5,700.09
Ab
2,435.16
Atau
197.56
mu
4,037.93
mA
1,468.64
tan(beta)
6.96
The truth!
7
M_1
2,961.80
M_2
-517.11
M_3
3,186.70
mL1
1,953.20
mL3
3,649.45
mE1
3,064.79
mE3
3,263.66
mQ1
3,345.65
mQ3
1,901.98
mU1
822.34
mU3
1,143.60
mD1
1,941.14
mD3
3,157.69
At
-2,788.69
Ab
-1,581.52
Atau
2,537.20
mu
1,815.67
mA
3,414.67
tan(beta)
30.43
The truth!
8
M_1
-449.40
M_2
-2,190.85
M_3
3,194.16
mL1
1,091.81
mL3
3,322.16
mE1
2,680.03
mE3
3,697.09
mQ1
2,235.30
mQ3
909.46
mU1
498.99
mU3
3,657.04
mD1
494.78
mD3
2,541.93
At
3,439.20
Ab
3,449.54
Atau
2,671.32
mu
1,539.94
mA
3,579.76
tan(beta)
10.23
The truth!
9
M_1
2,912.73
M_2
3,663.08
M_3
870.38
mL1
3,909.48
mL3
2,211.13
mE1
3,457.76
mE3
3,272.51
mQ1
613.05
mQ3
3,811.78
mU1
2,951.95
mU3
1,708.50
mD1
3,795.79
mD3
2,640.78
At
-3,279.14
Ab
-2,889.45
Atau
2,306.93
mu
239.93
mA
3,094.60
tan(beta)
32.79
The truth!
10
M_1
-294.64
M_2
-291.70
M_3
3,516.29
mL1
2,083.54
mL3
3,970.02
mE1
502.36
mE3
3,232.52
mQ1
2,304.63
mQ3
1,877.10
mU1
3,271.68
mU3
3,770.29
mD1
3,137.85
mD3
1,465.31
At
-3,636.04
Ab
817.64
Atau
2,261.59
mu
-1,001.07
mA
3,430.80
tan(beta)
24.89
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
