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
-1,728.40
M_2
3,474.84
M_3
1,648.89
mL1
3,378.87
mL3
3,979.90
mE1
3,285.58
mE3
3,480.75
mQ1
2,274.45
mQ3
3,566.33
mU1
715.56
mU3
3,616.51
mD1
2,364.56
mD3
1,043.97
At
4,529.41
Ab
-1,145.78
Atau
522.71
mu
-207.14
mA
588.95
tan(beta)
15.39
The truth!
2
M_1
239.59
M_2
-1,839.53
M_3
2,557.65
mL1
618.08
mL3
1,443.64
mE1
144.16
mE3
1,945.23
mQ1
906.31
mQ3
3,104.46
mU1
3,238.35
mU3
3,706.91
mD1
562.80
mD3
3,015.07
At
-4,230.37
Ab
3,997.99
Atau
-1,330.78
mu
264.88
mA
1,597.13
tan(beta)
46.45
The truth!
3
M_1
2,950.56
M_2
-3,176.31
M_3
2,107.60
mL1
1,103.28
mL3
1,220.53
mE1
1,081.67
mE3
3,089.32
mQ1
338.25
mQ3
3,588.56
mU1
791.79
mU3
1,306.22
mD1
1,233.35
mD3
1,377.90
At
3,353.53
Ab
-633.42
Atau
-1,967.94
mu
-235.74
mA
1,243.09
tan(beta)
29.20
The truth!
4
M_1
1,572.96
M_2
-3,906.64
M_3
2,686.05
mL1
3,037.44
mL3
3,710.95
mE1
948.21
mE3
3,315.67
mQ1
1,889.71
mQ3
3,800.50
mU1
2,123.63
mU3
1,526.71
mD1
1,309.45
mD3
3,167.22
At
3,635.45
Ab
1,727.47
Atau
2,142.02
mu
709.91
mA
3,472.25
tan(beta)
33.27
The truth!
5
M_1
-596.85
M_2
3,104.49
M_3
1,820.69
mL1
967.09
mL3
3,526.77
mE1
2,565.04
mE3
1,042.41
mQ1
1,945.13
mQ3
1,127.36
mU1
1,952.32
mU3
2,548.48
mD1
3,511.94
mD3
2,650.92
At
2,932.42
Ab
-1,240.40
Atau
-1,718.86
mu
-1,845.33
mA
1,659.88
tan(beta)
24.83
The truth!
6
M_1
-2,535.80
M_2
613.08
M_3
3,033.06
mL1
601.76
mL3
3,795.28
mE1
974.51
mE3
3,645.37
mQ1
3,805.86
mQ3
2,358.00
mU1
3,543.85
mU3
2,515.24
mD1
2,396.12
mD3
2,512.12
At
3,457.16
Ab
-1,127.23
Atau
-1,710.97
mu
-685.78
mA
2,630.21
tan(beta)
58.84
The truth!
7
M_1
-604.26
M_2
2,314.55
M_3
2,344.92
mL1
2,635.56
mL3
877.70
mE1
1,096.45
mE3
1,821.81
mQ1
3,186.63
mQ3
2,352.03
mU1
511.78
mU3
1,588.43
mD1
3,654.23
mD3
899.26
At
3,533.70
Ab
-1,885.43
Atau
-424.76
mu
-3,454.76
mA
1,812.19
tan(beta)
19.92
The truth!
8
M_1
-3,541.16
M_2
-222.41
M_3
3,105.73
mL1
1,055.72
mL3
2,083.31
mE1
1,557.04
mE3
1,836.75
mQ1
2,954.11
mQ3
3,139.91
mU1
2,524.57
mU3
2,512.66
mD1
701.84
mD3
2,852.08
At
-3,062.52
Ab
-3,211.13
Atau
-2,312.00
mu
293.06
mA
2,830.18
tan(beta)
28.87
The truth!
9
M_1
236.56
M_2
-1,455.77
M_3
3,972.09
mL1
370.46
mL3
368.57
mE1
3,670.97
mE3
1,409.85
mQ1
1,171.41
mQ3
2,176.74
mU1
1,671.17
mU3
3,953.55
mD1
250.97
mD3
2,311.29
At
-3,532.51
Ab
1,959.98
Atau
1,199.44
mu
268.34
mA
2,470.47
tan(beta)
51.82
The truth!
10
M_1
618.92
M_2
-1,312.34
M_3
3,218.12
mL1
900.60
mL3
614.34
mE1
1,067.59
mE3
987.72
mQ1
2,953.69
mQ3
1,452.51
mU1
1,012.82
mU3
2,716.55
mD1
1,303.76
mD3
586.78
At
4,384.60
Ab
498.54
Atau
-3,049.21
mu
2,046.18
mA
1,948.78
tan(beta)
5.88
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
