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
580.97
M_2
576.35
M_3
1,173.44
mL1
2,151.42
mL3
1,402.04
mE1
1,425.11
mE3
3,034.46
mQ1
820.95
mQ3
3,792.50
mU1
2,746.58
mU3
3,274.16
mD1
3,123.54
mD3
2,712.35
At
4,475.52
Ab
109.82
Atau
-3,396.91
mu
-1,728.06
mA
429.52
tan(beta)
13.44
The truth!
2
M_1
-200.53
M_2
-2,598.60
M_3
3,125.73
mL1
1,671.64
mL3
3,784.91
mE1
1,858.38
mE3
2,163.68
mQ1
785.44
mQ3
3,185.75
mU1
3,484.48
mU3
2,640.25
mD1
3,226.94
mD3
3,356.86
At
3,130.05
Ab
3,215.15
Atau
-2,850.75
mu
-216.78
mA
1,854.82
tan(beta)
28.75
The truth!
3
M_1
3,560.59
M_2
617.85
M_3
1,399.73
mL1
1,404.43
mL3
1,232.36
mE1
1,367.45
mE3
913.07
mQ1
2,055.62
mQ3
653.05
mU1
3,218.03
mU3
3,924.76
mD1
1,822.15
mD3
3,102.59
At
-3,607.39
Ab
1,074.85
Atau
539.68
mu
2,843.52
mA
2,524.30
tan(beta)
22.35
The truth!
4
M_1
-3,886.40
M_2
1,559.88
M_3
2,708.18
mL1
1,065.55
mL3
2,725.71
mE1
552.40
mE3
3,101.36
mQ1
1,296.52
mQ3
1,341.41
mU1
3,790.76
mU3
3,614.35
mD1
445.15
mD3
458.42
At
2,490.75
Ab
-2,868.59
Atau
-3,229.34
mu
342.07
mA
1,821.53
tan(beta)
52.16
The truth!
5
M_1
-3,715.10
M_2
3,896.60
M_3
1,052.05
mL1
602.81
mL3
574.25
mE1
2,423.49
mE3
804.68
mQ1
2,094.89
mQ3
3,021.80
mU1
2,014.16
mU3
2,925.87
mD1
1,019.00
mD3
2,497.80
At
-3,566.38
Ab
3,060.25
Atau
1,219.05
mu
197.85
mA
2,796.67
tan(beta)
14.81
The truth!
6
M_1
1,219.76
M_2
233.46
M_3
1,605.56
mL1
2,231.86
mL3
3,011.45
mE1
1,778.50
mE3
303.06
mQ1
3,912.73
mQ3
2,614.60
mU1
3,073.40
mU3
1,743.22
mD1
2,157.08
mD3
1,297.50
At
2,340.10
Ab
2,509.26
Atau
-3,676.30
mu
-432.34
mA
608.47
tan(beta)
13.22
The truth!
7
M_1
-778.15
M_2
-196.25
M_3
1,452.68
mL1
3,749.79
mL3
3,548.60
mE1
3,974.20
mE3
2,407.84
mQ1
594.68
mQ3
3,836.50
mU1
2,039.34
mU3
2,478.55
mD1
2,182.94
mD3
3,062.96
At
-3,687.81
Ab
-920.14
Atau
3,342.67
mu
2,028.53
mA
2,531.30
tan(beta)
32.08
The truth!
8
M_1
311.69
M_2
244.00
M_3
2,533.46
mL1
1,360.60
mL3
2,602.96
mE1
2,618.86
mE3
2,922.82
mQ1
2,751.66
mQ3
3,062.14
mU1
340.36
mU3
2,244.51
mD1
2,881.56
mD3
3,432.38
At
-2,517.63
Ab
1,243.42
Atau
1,453.28
mu
-415.22
mA
1,266.64
tan(beta)
11.19
The truth!
9
M_1
-740.66
M_2
3,612.59
M_3
3,982.16
mL1
2,771.25
mL3
2,718.49
mE1
2,282.71
mE3
2,986.50
mQ1
1,893.00
mQ3
3,782.87
mU1
3,926.98
mU3
2,709.69
mD1
476.66
mD3
1,460.18
At
3,607.38
Ab
-1,093.99
Atau
-1,838.38
mu
-1,380.74
mA
2,201.21
tan(beta)
56.12
The truth!
10
M_1
-643.24
M_2
728.54
M_3
534.95
mL1
1,543.46
mL3
1,430.38
mE1
3,285.42
mE3
3,224.95
mQ1
3,179.69
mQ3
1,818.34
mU1
3,843.39
mU3
1,975.88
mD1
2,587.85
mD3
3,237.76
At
2,583.00
Ab
-1,727.65
Atau
-2,800.79
mu
2,091.72
mA
3,626.04
tan(beta)
25.15
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
