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
-442.61
M_2
1,559.92
M_3
1,387.40
mL1
1,543.72
mL3
547.33
mE1
1,228.45
mE3
562.69
mQ1
337.18
mQ3
1,353.62
mU1
699.86
mU3
1,647.11
mD1
3,025.78
mD3
1,380.44
At
2,442.88
Ab
-2,417.26
Atau
-3,954.72
mu
1,161.34
mA
2,630.14
tan(beta)
27.73
The truth!
2
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!
3
M_1
2,986.95
M_2
-1,914.36
M_3
1,143.97
mL1
2,870.89
mL3
1,910.65
mE1
1,263.50
mE3
3,247.51
mQ1
2,492.60
mQ3
3,035.11
mU1
3,936.52
mU3
3,075.40
mD1
2,877.13
mD3
2,828.01
At
3,136.79
Ab
1,181.24
Atau
1,839.11
mu
1,041.48
mA
3,504.78
tan(beta)
13.06
The truth!
4
M_1
558.50
M_2
-2,723.54
M_3
1,566.80
mL1
3,167.07
mL3
2,315.19
mE1
788.54
mE3
2,829.09
mQ1
1,759.05
mQ3
551.63
mU1
666.71
mU3
2,180.01
mD1
3,162.60
mD3
2,223.46
At
2,653.94
Ab
-708.00
Atau
1,229.03
mu
2,851.06
mA
4,017.52
tan(beta)
58.04
The truth!
5
M_1
542.18
M_2
2,473.54
M_3
3,115.58
mL1
2,359.34
mL3
760.63
mE1
1,210.67
mE3
1,940.16
mQ1
2,780.67
mQ3
2,847.53
mU1
2,101.90
mU3
3,150.62
mD1
3,973.51
mD3
2,600.75
At
-4,781.42
Ab
-654.92
Atau
-1,296.47
mu
-421.99
mA
3,171.04
tan(beta)
15.02
The truth!
6
M_1
767.92
M_2
-114.40
M_3
1,861.53
mL1
2,178.26
mL3
1,901.18
mE1
196.68
mE3
2,516.89
mQ1
630.57
mQ3
992.51
mU1
280.30
mU3
1,075.75
mD1
2,784.59
mD3
3,454.50
At
2,650.39
Ab
-649.77
Atau
-951.15
mu
3,819.11
mA
3,992.39
tan(beta)
11.84
The truth!
7
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!
8
M_1
535.07
M_2
828.24
M_3
3,815.70
mL1
1,000.43
mL3
1,365.87
mE1
1,119.59
mE3
3,824.17
mQ1
514.36
mQ3
3,544.63
mU1
3,269.54
mU3
3,145.27
mD1
2,366.85
mD3
1,973.73
At
3,261.67
Ab
-1,715.82
Atau
2,284.55
mu
-716.20
mA
958.16
tan(beta)
9.87
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
-2,580.45
M_2
-1,568.21
M_3
2,493.29
mL1
3,165.24
mL3
1,406.59
mE1
2,280.18
mE3
926.53
mQ1
3,459.81
mQ3
2,362.37
mU1
2,653.21
mU3
2,631.05
mD1
3,221.43
mD3
377.29
At
-3,040.27
Ab
-3,455.96
Atau
2,764.26
mu
-141.47
mA
3,125.71
tan(beta)
32.37
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
