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
1,312.63
M_2
-1,313.72
M_3
2,450.13
mL1
3,547.00
mL3
3,140.31
mE1
1,802.21
mE3
3,052.51
mQ1
3,507.09
mQ3
3,367.22
mU1
1,366.45
mU3
2,516.96
mD1
1,219.90
mD3
1,155.08
At
4,541.88
Ab
-3,629.17
Atau
-695.91
mu
-3,133.39
mA
3,763.53
tan(beta)
31.67
The truth!
2
M_1
546.95
M_2
3,596.30
M_3
2,552.92
mL1
1,757.25
mL3
2,613.12
mE1
3,044.24
mE3
2,585.38
mQ1
3,062.95
mQ3
2,324.92
mU1
814.61
mU3
2,819.35
mD1
287.07
mD3
2,916.04
At
3,434.50
Ab
941.87
Atau
-2,055.51
mu
-2,501.84
mA
2,593.28
tan(beta)
20.94
The truth!
3
M_1
-266.02
M_2
1,343.03
M_3
1,599.77
mL1
2,609.44
mL3
1,464.76
mE1
436.33
mE3
1,715.64
mQ1
2,389.36
mQ3
3,165.44
mU1
1,819.40
mU3
2,266.91
mD1
1,602.10
mD3
1,268.70
At
2,754.68
Ab
-186.98
Atau
1,871.06
mu
-267.43
mA
2,597.73
tan(beta)
24.65
The truth!
4
M_1
2,826.64
M_2
-390.06
M_3
1,416.06
mL1
3,401.66
mL3
2,957.72
mE1
2,546.76
mE3
2,810.66
mQ1
2,133.33
mQ3
3,667.17
mU1
1,258.97
mU3
3,073.49
mD1
2,946.08
mD3
3,691.12
At
2,182.49
Ab
2,538.23
Atau
439.43
mu
233.50
mA
2,692.00
tan(beta)
12.31
The truth!
5
M_1
553.86
M_2
3,728.93
M_3
1,294.34
mL1
1,888.45
mL3
3,204.86
mE1
1,383.10
mE3
837.02
mQ1
735.69
mQ3
2,154.20
mU1
404.31
mU3
3,245.12
mD1
1,191.78
mD3
1,749.18
At
-4,779.16
Ab
-3,894.66
Atau
-212.02
mu
4,041.48
mA
1,069.10
tan(beta)
22.15
The truth!
6
M_1
147.18
M_2
622.95
M_3
1,512.02
mL1
2,757.74
mL3
1,032.83
mE1
664.24
mE3
3,266.68
mQ1
366.68
mQ3
3,373.16
mU1
2,504.84
mU3
3,305.62
mD1
2,938.84
mD3
1,329.66
At
4,451.93
Ab
1,057.22
Atau
-2,428.35
mu
-171.39
mA
2,196.60
tan(beta)
11.80
The truth!
7
M_1
1,406.82
M_2
479.11
M_3
2,752.75
mL1
1,237.56
mL3
3,397.80
mE1
855.43
mE3
1,051.00
mQ1
3,298.53
mQ3
1,714.20
mU1
3,423.92
mU3
3,461.49
mD1
298.39
mD3
1,780.29
At
-4,891.53
Ab
-3,732.95
Atau
826.74
mu
-1,515.66
mA
2,327.56
tan(beta)
6.50
The truth!
8
M_1
272.76
M_2
-3,969.64
M_3
3,016.49
mL1
2,272.74
mL3
3,181.80
mE1
992.50
mE3
569.84
mQ1
2,900.37
mQ3
2,928.12
mU1
1,158.14
mU3
3,843.75
mD1
2,532.85
mD3
765.36
At
3,872.65
Ab
3,468.17
Atau
-427.14
mu
216.69
mA
2,946.69
tan(beta)
15.12
The truth!
9
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!
10
M_1
-771.98
M_2
3,104.28
M_3
635.67
mL1
1,136.26
mL3
3,194.62
mE1
2,455.23
mE3
1,026.54
mQ1
2,273.97
mQ3
1,872.39
mU1
3,230.92
mU3
3,763.96
mD1
2,337.27
mD3
982.27
At
-6,132.60
Ab
1,345.35
Atau
-3,311.07
mu
-2,352.22
mA
2,718.70
tan(beta)
5.19
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
