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
-2,411.23
M_2
260.03
M_3
3,488.26
mL1
569.55
mL3
1,909.26
mE1
2,075.30
mE3
2,753.41
mQ1
2,985.61
mQ3
3,933.47
mU1
2,442.12
mU3
3,649.86
mD1
3,200.86
mD3
3,737.71
At
-3,961.18
Ab
2,626.75
Atau
405.80
mu
2,426.77
mA
2,350.49
tan(beta)
24.80
The truth!
2
M_1
-601.39
M_2
-3,091.51
M_3
2,426.81
mL1
1,012.24
mL3
3,747.08
mE1
2,970.65
mE3
3,578.47
mQ1
1,298.95
mQ3
2,065.89
mU1
1,387.38
mU3
1,606.53
mD1
2,390.57
mD3
491.78
At
3,141.07
Ab
2,105.21
Atau
2,359.90
mu
2,063.80
mA
3,167.15
tan(beta)
32.65
The truth!
3
M_1
-550.53
M_2
530.33
M_3
3,480.46
mL1
2,527.84
mL3
868.49
mE1
3,834.16
mE3
2,789.78
mQ1
2,323.11
mQ3
2,907.76
mU1
2,346.10
mU3
3,371.74
mD1
3,193.48
mD3
3,328.00
At
-4,425.14
Ab
-2,228.51
Atau
3,097.61
mu
2,345.66
mA
3,412.64
tan(beta)
51.05
The truth!
4
M_1
436.73
M_2
-434.77
M_3
2,831.26
mL1
2,118.70
mL3
2,983.03
mE1
2,268.81
mE3
3,483.47
mQ1
435.92
mQ3
2,027.31
mU1
3,153.99
mU3
1,408.64
mD1
2,641.30
mD3
3,606.55
At
-2,751.35
Ab
-3,285.64
Atau
-2,556.24
mu
2,831.22
mA
654.64
tan(beta)
13.78
The truth!
5
M_1
868.31
M_2
-2,619.78
M_3
3,125.42
mL1
3,712.13
mL3
511.15
mE1
905.16
mE3
2,150.15
mQ1
3,248.85
mQ3
2,025.18
mU1
2,918.30
mU3
2,998.91
mD1
2,089.18
mD3
1,489.34
At
-3,663.12
Ab
-179.40
Atau
2,521.22
mu
-491.05
mA
3,437.25
tan(beta)
49.98
The truth!
6
M_1
3,675.27
M_2
-3,162.31
M_3
3,674.77
mL1
3,296.80
mL3
3,950.91
mE1
3,444.26
mE3
2,868.34
mQ1
1,066.79
mQ3
3,573.56
mU1
1,227.81
mU3
1,183.52
mD1
3,069.53
mD3
3,541.71
At
3,381.04
Ab
1,734.68
Atau
3,700.97
mu
-616.07
mA
2,542.10
tan(beta)
45.65
The truth!
7
M_1
-2,391.88
M_2
-286.11
M_3
3,725.28
mL1
3,741.93
mL3
572.17
mE1
3,284.49
mE3
2,412.98
mQ1
1,022.20
mQ3
3,277.58
mU1
1,805.90
mU3
3,279.81
mD1
1,987.89
mD3
160.80
At
-5,704.22
Ab
1,604.24
Atau
-1,462.28
mu
2,045.16
mA
956.11
tan(beta)
7.43
The truth!
8
M_1
-1,589.05
M_2
517.28
M_3
3,852.22
mL1
1,807.22
mL3
2,582.98
mE1
1,798.66
mE3
2,776.43
mQ1
3,748.81
mQ3
3,404.84
mU1
1,663.12
mU3
3,036.01
mD1
2,527.51
mD3
3,136.08
At
3,595.18
Ab
-947.82
Atau
3,649.99
mu
-3,886.54
mA
1,302.60
tan(beta)
9.14
The truth!
9
M_1
-1,105.27
M_2
522.58
M_3
1,172.75
mL1
3,143.71
mL3
931.52
mE1
2,531.04
mE3
1,144.62
mQ1
1,348.01
mQ3
3,024.22
mU1
1,363.72
mU3
3,857.46
mD1
2,099.73
mD3
1,364.52
At
-4,459.23
Ab
-1,660.61
Atau
-1,484.57
mu
3,717.93
mA
1,533.22
tan(beta)
38.58
The truth!
10
M_1
-281.28
M_2
-669.45
M_3
1,128.54
mL1
2,871.34
mL3
510.61
mE1
2,554.50
mE3
456.81
mQ1
1,961.78
mQ3
836.30
mU1
2,080.79
mU3
2,301.13
mD1
2,011.72
mD3
3,595.31
At
-2,489.49
Ab
-853.53
Atau
843.94
mu
4,001.70
mA
1,750.57
tan(beta)
17.36
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
