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
235.45
M_2
-658.08
M_3
3,301.83
mL1
1,380.73
mL3
3,711.40
mE1
178.32
mE3
1,177.58
mQ1
1,076.77
mQ3
3,604.43
mU1
893.13
mU3
3,003.90
mD1
1,270.80
mD3
2,010.65
At
-4,050.98
Ab
1,853.18
Atau
-2,471.09
mu
468.49
mA
1,454.20
tan(beta)
40.29
The truth!
2
M_1
-570.12
M_2
-958.36
M_3
2,746.16
mL1
1,648.86
mL3
3,662.90
mE1
618.31
mE3
2,151.53
mQ1
2,756.23
mQ3
3,085.66
mU1
549.69
mU3
894.28
mD1
2,309.53
mD3
3,277.98
At
-3,584.06
Ab
593.50
Atau
-2,224.01
mu
2,325.86
mA
4,101.33
tan(beta)
35.78
The truth!
3
M_1
-1,722.67
M_2
429.14
M_3
2,647.45
mL1
1,527.17
mL3
1,298.70
mE1
670.23
mE3
3,488.81
mQ1
1,934.06
mQ3
2,385.78
mU1
2,354.56
mU3
1,542.80
mD1
3,897.32
mD3
3,537.72
At
-2,458.02
Ab
2,987.76
Atau
-408.33
mu
-168.59
mA
2,129.20
tan(beta)
58.95
The truth!
4
M_1
3,279.41
M_2
772.11
M_3
2,040.51
mL1
1,126.07
mL3
1,892.56
mE1
2,540.80
mE3
2,742.23
mQ1
1,664.43
mQ3
3,917.88
mU1
3,609.28
mU3
2,242.95
mD1
2,611.15
mD3
3,735.98
At
-3,711.05
Ab
3,810.06
Atau
477.17
mu
1,000.74
mA
3,228.40
tan(beta)
10.98
The truth!
5
M_1
311.85
M_2
-1,868.14
M_3
249.19
mL1
1,193.83
mL3
3,062.79
mE1
1,505.11
mE3
2,504.64
mQ1
260.64
mQ3
3,168.73
mU1
3,206.38
mU3
3,869.34
mD1
1,050.89
mD3
1,646.00
At
-3,857.94
Ab
-2,542.28
Atau
3,314.88
mu
1,415.98
mA
3,193.45
tan(beta)
22.23
The truth!
6
M_1
285.88
M_2
1,845.64
M_3
3,566.07
mL1
3,523.40
mL3
914.39
mE1
2,600.69
mE3
365.05
mQ1
3,609.65
mQ3
2,159.17
mU1
2,052.93
mU3
3,667.54
mD1
3,946.67
mD3
3,752.22
At
-3,611.03
Ab
-306.93
Atau
-492.57
mu
2,005.04
mA
784.16
tan(beta)
8.78
The truth!
7
M_1
1,325.73
M_2
-1,735.91
M_3
1,188.64
mL1
1,312.33
mL3
1,703.19
mE1
3,834.31
mE3
1,780.36
mQ1
1,359.07
mQ3
1,358.36
mU1
3,710.92
mU3
2,021.46
mD1
1,815.17
mD3
1,739.65
At
-3,711.74
Ab
-2,866.03
Atau
-2,867.82
mu
2,358.71
mA
2,807.97
tan(beta)
7.74
The truth!
8
M_1
-137.75
M_2
2,769.03
M_3
1,385.61
mL1
474.89
mL3
1,639.81
mE1
3,584.71
mE3
135.13
mQ1
1,652.28
mQ3
3,406.45
mU1
3,036.92
mU3
3,534.23
mD1
3,358.00
mD3
1,818.82
At
-3,739.30
Ab
3,637.33
Atau
-2,122.66
mu
2,966.86
mA
3,471.20
tan(beta)
14.43
The truth!
9
M_1
-1,394.37
M_2
574.05
M_3
3,089.46
mL1
3,242.00
mL3
2,408.88
mE1
679.64
mE3
2,910.14
mQ1
1,693.00
mQ3
3,820.26
mU1
3,349.63
mU3
3,300.87
mD1
1,477.54
mD3
1,292.24
At
-2,288.94
Ab
464.31
Atau
-2,963.94
mu
1,889.43
mA
1,937.84
tan(beta)
26.14
The truth!
10
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!
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
