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
-2,402.20
M_2
-218.26
M_3
2,029.23
mL1
489.05
mL3
419.92
mE1
2,008.66
mE3
784.87
mQ1
1,750.69
mQ3
3,543.22
mU1
2,337.41
mU3
3,686.85
mD1
533.32
mD3
1,635.73
At
2,707.00
Ab
3,255.97
Atau
-2,499.12
mu
1,744.41
mA
3,040.12
tan(beta)
16.13
The truth!
2
M_1
61.40
M_2
945.92
M_3
2,320.26
mL1
991.32
mL3
250.11
mE1
1,127.10
mE3
2,152.65
mQ1
3,838.53
mQ3
1,814.78
mU1
3,565.36
mU3
2,920.93
mD1
2,187.94
mD3
873.44
At
-5,405.80
Ab
-2,569.90
Atau
103.35
mu
1,034.28
mA
1,632.83
tan(beta)
3.79
The truth!
3
M_1
-233.77
M_2
3,341.86
M_3
838.78
mL1
1,826.62
mL3
748.67
mE1
1,430.49
mE3
3,496.50
mQ1
656.05
mQ3
2,597.18
mU1
456.77
mU3
2,957.34
mD1
2,341.37
mD3
1,131.52
At
-3,288.70
Ab
-1,105.52
Atau
-2,803.49
mu
232.05
mA
2,710.79
tan(beta)
52.38
The truth!
4
M_1
2,230.84
M_2
2,902.14
M_3
3,421.47
mL1
3,607.59
mL3
3,435.10
mE1
2,010.74
mE3
1,838.86
mQ1
3,282.74
mQ3
3,936.11
mU1
1,346.50
mU3
1,594.31
mD1
332.08
mD3
2,681.68
At
4,040.21
Ab
2,263.39
Atau
-1,404.53
mu
-290.01
mA
927.27
tan(beta)
23.78
The truth!
5
M_1
3,109.69
M_2
-112.56
M_3
2,752.52
mL1
228.21
mL3
2,953.57
mE1
1,327.25
mE3
2,823.67
mQ1
1,087.28
mQ3
1,534.41
mU1
2,788.02
mU3
2,582.24
mD1
2,560.56
mD3
2,543.42
At
3,057.94
Ab
-3,247.85
Atau
1,644.12
mu
-1,519.34
mA
3,615.08
tan(beta)
23.06
The truth!
6
M_1
-825.98
M_2
1,317.56
M_3
702.61
mL1
1,336.03
mL3
3,304.15
mE1
1,202.50
mE3
1,754.53
mQ1
3,559.77
mQ3
3,371.49
mU1
1,154.55
mU3
3,571.67
mD1
2,411.54
mD3
2,591.96
At
-5,115.68
Ab
851.24
Atau
-1,258.21
mu
4,015.20
mA
2,424.47
tan(beta)
8.24
The truth!
7
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!
8
M_1
-44.52
M_2
1,923.81
M_3
3,790.58
mL1
2,210.29
mL3
1,450.87
mE1
977.75
mE3
1,445.44
mQ1
1,252.23
mQ3
3,249.44
mU1
3,106.83
mU3
2,560.23
mD1
614.83
mD3
939.32
At
-3,986.85
Ab
2,115.57
Atau
-449.87
mu
143.41
mA
3,313.31
tan(beta)
22.39
The truth!
9
M_1
-475.25
M_2
3,031.48
M_3
388.65
mL1
555.26
mL3
2,895.87
mE1
2,146.64
mE3
1,017.75
mQ1
2,972.11
mQ3
3,186.32
mU1
3,167.44
mU3
3,423.85
mD1
2,357.72
mD3
3,106.22
At
-4,756.79
Ab
-706.40
Atau
-517.62
mu
-2,265.42
mA
3,301.82
tan(beta)
11.81
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
