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
1,200.09
M_2
3,317.85
M_3
2,813.07
mL1
2,695.26
mL3
3,467.45
mE1
3,926.26
mE3
690.34
mQ1
1,324.47
mQ3
1,016.24
mU1
852.80
mU3
3,808.60
mD1
2,224.91
mD3
2,234.53
At
4,025.83
Ab
-2,207.53
Atau
2,367.96
mu
-354.51
mA
968.54
tan(beta)
10.36
The truth!
2
M_1
1,173.02
M_2
-3,078.87
M_3
1,225.71
mL1
1,849.64
mL3
2,619.16
mE1
3,626.95
mE3
3,256.95
mQ1
392.56
mQ3
1,695.43
mU1
1,198.58
mU3
3,581.52
mD1
3,910.03
mD3
473.18
At
4,103.45
Ab
449.82
Atau
1,979.01
mu
388.41
mA
1,778.50
tan(beta)
11.43
The truth!
3
M_1
-846.44
M_2
-3,422.21
M_3
1,719.67
mL1
3,310.86
mL3
2,617.96
mE1
2,977.59
mE3
3,587.07
mQ1
3,861.46
mQ3
3,832.33
mU1
675.67
mU3
2,876.61
mD1
2,111.07
mD3
3,041.61
At
3,698.70
Ab
2,719.27
Atau
-1,367.93
mu
-1,378.88
mA
2,210.67
tan(beta)
19.35
The truth!
4
M_1
-1,007.79
M_2
-3,635.05
M_3
3,919.25
mL1
3,799.21
mL3
3,616.75
mE1
1,463.47
mE3
1,763.87
mQ1
608.89
mQ3
3,960.61
mU1
1,713.69
mU3
3,851.52
mD1
1,424.04
mD3
636.53
At
-5,075.25
Ab
2,718.46
Atau
2,173.46
mu
385.33
mA
2,486.07
tan(beta)
16.13
The truth!
5
M_1
-953.59
M_2
-604.61
M_3
3,175.76
mL1
1,289.44
mL3
2,585.59
mE1
2,552.58
mE3
2,567.28
mQ1
2,956.81
mQ3
2,466.04
mU1
3,808.84
mU3
1,473.93
mD1
1,480.86
mD3
3,603.67
At
3,542.38
Ab
691.92
Atau
3,509.21
mu
2,121.77
mA
3,905.25
tan(beta)
10.32
The truth!
6
M_1
411.76
M_2
-828.11
M_3
2,724.13
mL1
654.06
mL3
488.18
mE1
1,728.47
mE3
2,676.66
mQ1
3,920.43
mQ3
1,406.78
mU1
375.47
mU3
2,210.02
mD1
2,095.70
mD3
2,287.25
At
-5,453.59
Ab
-365.98
Atau
3,390.59
mu
-1,801.82
mA
3,877.49
tan(beta)
13.34
The truth!
7
M_1
2,539.25
M_2
2,562.99
M_3
3,285.09
mL1
390.77
mL3
513.49
mE1
3,466.22
mE3
3,260.93
mQ1
739.06
mQ3
3,839.29
mU1
2,702.28
mU3
2,950.69
mD1
3,789.84
mD3
3,689.78
At
-3,163.19
Ab
-1,357.81
Atau
-487.13
mu
-248.95
mA
2,927.66
tan(beta)
57.44
The truth!
8
M_1
708.60
M_2
1,778.11
M_3
1,517.70
mL1
1,132.59
mL3
999.63
mE1
949.92
mE3
1,920.87
mQ1
862.97
mQ3
3,035.11
mU1
1,527.01
mU3
3,978.45
mD1
2,609.07
mD3
3,430.06
At
5,102.15
Ab
3,387.65
Atau
3,586.30
mu
-2,199.40
mA
1,297.68
tan(beta)
9.58
The truth!
9
M_1
1,236.08
M_2
319.25
M_3
2,927.47
mL1
2,474.84
mL3
1,946.24
mE1
2,119.93
mE3
376.41
mQ1
302.50
mQ3
2,681.35
mU1
473.52
mU3
3,584.20
mD1
3,641.92
mD3
391.36
At
-4,374.58
Ab
-2,157.68
Atau
2,095.47
mu
3,238.54
mA
1,773.29
tan(beta)
29.96
The truth!
10
M_1
3,407.85
M_2
203.97
M_3
3,371.70
mL1
2,740.06
mL3
1,330.97
mE1
662.89
mE3
1,406.33
mQ1
1,550.05
mQ3
2,751.27
mU1
2,144.30
mU3
1,234.15
mD1
3,437.77
mD3
598.56
At
-4,682.97
Ab
-446.09
Atau
-863.33
mu
3,773.11
mA
3,732.77
tan(beta)
20.85
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
