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
733.26
M_2
2,571.51
M_3
2,202.05
mL1
2,366.54
mL3
1,555.03
mE1
559.65
mE3
3,417.81
mQ1
3,117.40
mQ3
2,878.45
mU1
251.54
mU3
2,084.11
mD1
2,718.46
mD3
2,879.72
At
2,937.06
Ab
3,610.81
Atau
-2,901.24
mu
170.75
mA
2,932.04
tan(beta)
45.91
The truth!
2
M_1
3,628.16
M_2
-953.33
M_3
642.25
mL1
3,261.58
mL3
473.18
mE1
3,780.22
mE3
454.02
mQ1
782.68
mQ3
2,558.35
mU1
2,922.19
mU3
3,729.38
mD1
579.50
mD3
574.03
At
3,804.28
Ab
279.89
Atau
3,717.70
mu
321.79
mA
2,190.26
tan(beta)
35.75
The truth!
3
M_1
-3,745.34
M_2
-935.70
M_3
2,677.37
mL1
3,686.85
mL3
3,405.45
mE1
2,297.23
mE3
1,935.37
mQ1
951.04
mQ3
1,964.23
mU1
1,384.12
mU3
1,833.27
mD1
1,250.72
mD3
1,885.03
At
-3,800.53
Ab
-1,961.93
Atau
1,334.13
mu
2,749.11
mA
2,387.45
tan(beta)
24.63
The truth!
4
M_1
547.94
M_2
3,201.95
M_3
416.19
mL1
2,652.22
mL3
3,934.40
mE1
642.45
mE3
2,316.54
mQ1
916.62
mQ3
3,866.04
mU1
2,638.75
mU3
3,711.82
mD1
2,504.46
mD3
907.63
At
-5,895.05
Ab
-3,848.84
Atau
3,273.62
mu
-803.64
mA
2,012.27
tan(beta)
9.29
The truth!
5
M_1
-1,319.48
M_2
2,430.47
M_3
1,642.05
mL1
654.75
mL3
2,890.93
mE1
2,253.73
mE3
3,611.38
mQ1
2,126.75
mQ3
3,225.10
mU1
2,704.74
mU3
3,355.80
mD1
631.89
mD3
1,837.93
At
-6,465.64
Ab
-1,830.71
Atau
-887.72
mu
543.93
mA
3,260.48
tan(beta)
5.03
The truth!
6
M_1
3,063.78
M_2
220.42
M_3
3,592.13
mL1
2,691.21
mL3
653.38
mE1
837.49
mE3
1,626.63
mQ1
3,058.29
mQ3
1,600.40
mU1
3,759.96
mU3
3,302.18
mD1
421.77
mD3
3,003.29
At
-5,736.00
Ab
620.34
Atau
-737.95
mu
3,963.01
mA
1,201.14
tan(beta)
8.98
The truth!
7
M_1
-3,439.41
M_2
2,484.77
M_3
1,858.34
mL1
3,786.22
mL3
2,374.56
mE1
1,955.66
mE3
1,934.71
mQ1
3,165.67
mQ3
3,766.08
mU1
3,860.22
mU3
2,225.40
mD1
2,824.38
mD3
2,297.80
At
-3,477.02
Ab
-913.32
Atau
-3,304.35
mu
-708.75
mA
3,578.43
tan(beta)
22.82
The truth!
8
M_1
-1,529.42
M_2
-1,253.84
M_3
3,329.01
mL1
3,130.99
mL3
2,725.40
mE1
3,587.79
mE3
1,436.32
mQ1
2,904.83
mQ3
2,891.62
mU1
743.50
mU3
2,798.08
mD1
1,966.46
mD3
726.32
At
6,681.24
Ab
-691.52
Atau
154.76
mu
147.49
mA
1,281.25
tan(beta)
3.96
The truth!
9
M_1
535.07
M_2
828.24
M_3
3,815.70
mL1
1,000.43
mL3
1,365.87
mE1
1,119.59
mE3
3,824.17
mQ1
514.36
mQ3
3,544.63
mU1
3,269.54
mU3
3,145.27
mD1
2,366.85
mD3
1,973.73
At
3,261.67
Ab
-1,715.82
Atau
2,284.55
mu
-716.20
mA
958.16
tan(beta)
9.87
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
