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
1,826.61
M_2
1,065.22
M_3
2,644.00
mL1
1,414.83
mL3
2,005.92
mE1
2,750.48
mE3
1,105.85
mQ1
3,143.59
mQ3
2,655.81
mU1
2,376.58
mU3
3,514.91
mD1
3,701.11
mD3
2,805.47
At
-7,412.47
Ab
-102.75
Atau
-305.57
mu
-3,300.01
mA
3,446.89
tan(beta)
4.40
The truth!
2
M_1
3,357.77
M_2
325.98
M_3
3,187.74
mL1
3,838.85
mL3
3,641.55
mE1
829.90
mE3
3,642.25
mQ1
530.03
mQ3
2,715.86
mU1
1,552.90
mU3
2,751.11
mD1
3,364.17
mD3
886.77
At
-3,420.70
Ab
1,566.75
Atau
-3,522.93
mu
2,519.34
mA
3,348.45
tan(beta)
40.29
The truth!
3
M_1
553.86
M_2
3,728.93
M_3
1,294.34
mL1
1,888.45
mL3
3,204.86
mE1
1,383.10
mE3
837.02
mQ1
735.69
mQ3
2,154.20
mU1
404.31
mU3
3,245.12
mD1
1,191.78
mD3
1,749.18
At
-4,779.16
Ab
-3,894.66
Atau
-212.02
mu
4,041.48
mA
1,069.10
tan(beta)
22.15
The truth!
4
M_1
753.97
M_2
2,264.17
M_3
2,122.12
mL1
2,838.53
mL3
3,231.03
mE1
3,527.31
mE3
3,748.08
mQ1
3,318.05
mQ3
2,894.45
mU1
3,899.08
mU3
2,421.78
mD1
3,538.87
mD3
1,248.40
At
4,124.30
Ab
2,649.17
Atau
-1,198.36
mu
-804.03
mA
1,393.69
tan(beta)
17.37
The truth!
5
M_1
1,600.45
M_2
3,957.56
M_3
2,993.45
mL1
3,122.17
mL3
1,959.11
mE1
1,195.59
mE3
1,501.64
mQ1
1,314.28
mQ3
3,219.42
mU1
288.82
mU3
2,023.48
mD1
866.24
mD3
3,664.67
At
4,284.74
Ab
1,574.15
Atau
688.07
mu
486.72
mA
3,968.21
tan(beta)
52.20
The truth!
6
M_1
-1,124.63
M_2
184.22
M_3
3,218.17
mL1
3,981.45
mL3
3,152.66
mE1
2,958.97
mE3
2,542.81
mQ1
3,826.38
mQ3
3,297.19
mU1
1,109.96
mU3
3,288.74
mD1
366.15
mD3
2,226.65
At
-1,932.59
Ab
3,448.24
Atau
-834.54
mu
1,050.21
mA
2,308.12
tan(beta)
36.59
The truth!
7
M_1
1,399.06
M_2
-221.47
M_3
1,671.05
mL1
2,118.43
mL3
1,068.69
mE1
3,316.29
mE3
3,653.04
mQ1
3,570.97
mQ3
3,706.31
mU1
2,234.79
mU3
2,629.10
mD1
2,179.37
mD3
2,093.66
At
4,912.62
Ab
3,354.22
Atau
-1,205.52
mu
3,169.39
mA
3,899.47
tan(beta)
10.05
The truth!
8
M_1
-3,802.83
M_2
219.48
M_3
1,546.50
mL1
3,874.94
mL3
3,553.31
mE1
1,803.27
mE3
758.33
mQ1
3,521.37
mQ3
3,797.73
mU1
1,489.39
mU3
3,219.38
mD1
1,512.95
mD3
3,332.05
At
-2,551.03
Ab
3,405.93
Atau
3,584.03
mu
786.91
mA
3,801.88
tan(beta)
14.73
The truth!
9
M_1
-1,089.61
M_2
2,305.31
M_3
2,565.36
mL1
2,004.91
mL3
2,511.15
mE1
3,063.33
mE3
2,712.65
mQ1
1,786.14
mQ3
1,082.59
mU1
1,820.53
mU3
2,773.94
mD1
2,796.92
mD3
1,592.86
At
-4,728.53
Ab
2,416.73
Atau
3,620.13
mu
1,907.94
mA
793.25
tan(beta)
11.84
The truth!
10
M_1
-3,743.13
M_2
276.46
M_3
567.38
mL1
2,512.49
mL3
1,457.12
mE1
2,307.29
mE3
2,769.39
mQ1
3,088.40
mQ3
1,718.22
mU1
1,836.93
mU3
2,732.55
mD1
3,494.11
mD3
2,441.96
At
-3,764.48
Ab
-2,003.00
Atau
-529.03
mu
3,112.82
mA
3,308.19
tan(beta)
10.34
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
