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
298.65
M_2
2,206.22
M_3
2,276.40
mL1
2,379.99
mL3
1,588.90
mE1
351.84
mE3
274.96
mQ1
637.40
mQ3
3,833.93
mU1
3,573.87
mU3
907.59
mD1
3,812.10
mD3
3,242.29
At
4,140.87
Ab
-181.85
Atau
-1,221.12
mu
-307.12
mA
2,197.38
tan(beta)
12.60
The truth!
2
M_1
-640.29
M_2
-2,197.07
M_3
1,286.13
mL1
3,097.85
mL3
1,807.35
mE1
1,620.26
mE3
1,132.70
mQ1
1,032.65
mQ3
3,081.81
mU1
2,423.11
mU3
1,847.94
mD1
517.08
mD3
1,144.65
At
3,599.02
Ab
-1,131.06
Atau
-246.96
mu
-801.17
mA
3,752.22
tan(beta)
36.12
The truth!
3
M_1
-574.82
M_2
2,590.89
M_3
552.59
mL1
3,296.04
mL3
1,416.69
mE1
2,292.02
mE3
3,703.36
mQ1
3,738.14
mQ3
892.66
mU1
1,552.15
mU3
2,165.11
mD1
2,732.42
mD3
528.46
At
-3,107.58
Ab
-3,349.48
Atau
-1,953.79
mu
896.22
mA
3,578.28
tan(beta)
23.12
The truth!
4
M_1
3,442.66
M_2
-3,032.45
M_3
3,734.45
mL1
1,832.90
mL3
2,618.74
mE1
2,298.06
mE3
3,447.83
mQ1
3,068.14
mQ3
3,002.64
mU1
2,751.75
mU3
1,259.08
mD1
2,682.10
mD3
1,017.89
At
3,135.15
Ab
2,101.61
Atau
1,674.97
mu
95.88
mA
2,205.99
tan(beta)
8.78
The truth!
5
M_1
3,120.58
M_2
1,978.25
M_3
3,432.19
mL1
870.31
mL3
684.77
mE1
2,969.01
mE3
1,636.71
mQ1
2,235.13
mQ3
1,722.53
mU1
1,546.00
mU3
3,787.36
mD1
1,538.73
mD3
585.40
At
3,707.13
Ab
3,617.58
Atau
-3,531.63
mu
-558.67
mA
1,358.07
tan(beta)
11.98
The truth!
6
M_1
-2,998.84
M_2
-604.06
M_3
2,668.09
mL1
1,821.76
mL3
3,138.14
mE1
1,488.94
mE3
2,108.04
mQ1
2,785.66
mQ3
1,598.07
mU1
3,892.45
mU3
1,975.89
mD1
623.96
mD3
2,798.71
At
3,192.14
Ab
-2,072.19
Atau
2,368.78
mu
1,999.23
mA
3,957.87
tan(beta)
40.78
The truth!
7
M_1
629.92
M_2
3,466.70
M_3
825.96
mL1
2,822.36
mL3
3,066.53
mE1
1,486.91
mE3
2,414.70
mQ1
751.39
mQ3
1,686.80
mU1
1,387.43
mU3
3,760.96
mD1
3,649.31
mD3
681.67
At
-3,213.33
Ab
-445.03
Atau
539.44
mu
251.69
mA
3,405.44
tan(beta)
16.53
The truth!
8
M_1
-2,736.65
M_2
-2,309.99
M_3
3,722.90
mL1
2,006.61
mL3
2,852.76
mE1
3,954.96
mE3
3,451.47
mQ1
3,622.92
mQ3
2,746.60
mU1
1,823.98
mU3
3,767.44
mD1
2,471.54
mD3
1,133.04
At
2,653.50
Ab
927.16
Atau
353.42
mu
-562.52
mA
3,958.64
tan(beta)
36.34
The truth!
9
M_1
672.47
M_2
284.10
M_3
844.14
mL1
1,121.52
mL3
405.61
mE1
2,027.38
mE3
2,735.61
mQ1
2,651.03
mQ3
3,426.82
mU1
1,108.85
mU3
3,450.17
mD1
1,348.83
mD3
939.11
At
5,139.50
Ab
-348.04
Atau
-1,631.88
mu
-1,998.08
mA
806.63
tan(beta)
6.19
The truth!
10
M_1
2,583.73
M_2
632.26
M_3
1,464.42
mL1
3,819.08
mL3
1,846.25
mE1
2,767.73
mE3
1,368.07
mQ1
2,636.66
mQ3
3,329.00
mU1
985.09
mU3
3,821.63
mD1
3,472.76
mD3
1,200.83
At
-3,637.87
Ab
1,302.32
Atau
-3,439.18
mu
2,160.06
mA
2,170.16
tan(beta)
24.76
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
