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
636.67
M_2
-1,338.95
M_3
1,131.03
mL1
952.16
mL3
2,259.32
mE1
2,582.26
mE3
3,683.93
mQ1
747.56
mQ3
3,686.18
mU1
1,029.63
mU3
2,741.83
mD1
3,982.27
mD3
2,578.70
At
3,668.92
Ab
3,536.60
Atau
651.89
mu
-273.17
mA
3,056.02
tan(beta)
21.37
The truth!
2
M_1
434.97
M_2
3,891.91
M_3
1,451.41
mL1
2,748.16
mL3
3,495.59
mE1
1,732.86
mE3
3,066.78
mQ1
1,549.65
mQ3
3,384.52
mU1
2,461.94
mU3
3,733.16
mD1
1,245.41
mD3
2,047.96
At
-4,143.73
Ab
-1,561.25
Atau
1,672.18
mu
352.90
mA
1,651.71
tan(beta)
30.87
The truth!
3
M_1
-569.03
M_2
-1,540.69
M_3
2,067.42
mL1
2,729.04
mL3
2,520.31
mE1
2,109.54
mE3
3,497.88
mQ1
3,633.17
mQ3
598.19
mU1
1,252.74
mU3
2,529.86
mD1
2,899.87
mD3
1,444.90
At
-2,891.88
Ab
-2,153.38
Atau
-505.51
mu
3,456.28
mA
2,309.17
tan(beta)
27.67
The truth!
4
M_1
659.72
M_2
-2,261.73
M_3
3,090.13
mL1
3,172.41
mL3
1,468.62
mE1
2,316.32
mE3
1,036.56
mQ1
3,377.16
mQ3
3,520.09
mU1
2,958.11
mU3
3,657.13
mD1
737.33
mD3
2,368.61
At
5,098.75
Ab
1,338.31
Atau
-1,604.79
mu
-1,043.57
mA
1,411.93
tan(beta)
10.55
The truth!
5
M_1
3,526.52
M_2
718.16
M_3
3,426.70
mL1
3,146.19
mL3
511.34
mE1
2,829.46
mE3
2,988.67
mQ1
3,148.74
mQ3
2,397.27
mU1
1,492.89
mU3
2,117.46
mD1
3,691.05
mD3
3,394.95
At
-2,839.95
Ab
-3,624.34
Atau
514.91
mu
248.96
mA
2,003.81
tan(beta)
50.18
The truth!
6
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!
7
M_1
163.41
M_2
3,800.43
M_3
1,404.49
mL1
2,475.36
mL3
1,292.08
mE1
2,071.49
mE3
2,689.78
mQ1
3,048.21
mQ3
2,823.87
mU1
231.56
mU3
1,121.10
mD1
2,579.65
mD3
2,800.31
At
-2,909.10
Ab
-6.00
Atau
-3,479.93
mu
164.72
mA
1,816.40
tan(beta)
18.85
The truth!
8
M_1
-3,069.09
M_2
3,405.13
M_3
391.90
mL1
698.47
mL3
3,829.65
mE1
2,980.51
mE3
2,894.82
mQ1
1,581.25
mQ3
1,910.00
mU1
1,122.65
mU3
3,063.03
mD1
1,941.24
mD3
1,006.54
At
3,899.88
Ab
2,090.44
Atau
-47.33
mu
141.09
mA
1,288.21
tan(beta)
5.97
The truth!
9
M_1
-3,454.02
M_2
-3,985.22
M_3
2,819.34
mL1
1,373.67
mL3
3,859.04
mE1
1,464.08
mE3
1,526.09
mQ1
1,370.69
mQ3
3,844.36
mU1
3,642.06
mU3
3,411.53
mD1
1,121.71
mD3
2,013.85
At
3,969.05
Ab
-2,123.09
Atau
-2,282.86
mu
158.42
mA
709.07
tan(beta)
38.62
The truth!
10
M_1
-1,377.34
M_2
-1,701.05
M_3
255.41
mL1
2,026.09
mL3
3,201.58
mE1
838.32
mE3
1,975.93
mQ1
3,934.33
mQ3
3,483.02
mU1
1,228.55
mU3
3,503.75
mD1
2,013.82
mD3
1,799.81
At
-3,132.20
Ab
-341.74
Atau
-94.65
mu
-181.85
mA
3,696.05
tan(beta)
21.45
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
