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
2,914.03
M_2
-509.06
M_3
3,940.37
mL1
1,239.04
mL3
2,932.30
mE1
2,610.01
mE3
2,047.21
mQ1
2,236.36
mQ3
2,920.00
mU1
1,614.68
mU3
3,064.24
mD1
476.07
mD3
1,051.37
At
-3,782.73
Ab
-420.19
Atau
2,902.25
mu
774.14
mA
1,409.97
tan(beta)
36.19
The truth!
2
M_1
1,087.47
M_2
-996.82
M_3
3,346.24
mL1
447.25
mL3
2,876.04
mE1
3,320.73
mE3
889.37
mQ1
2,490.76
mQ3
2,227.36
mU1
2,179.22
mU3
3,094.54
mD1
1,375.33
mD3
3,559.61
At
-2,952.77
Ab
2,336.25
Atau
1,874.42
mu
416.58
mA
1,074.58
tan(beta)
22.70
The truth!
3
M_1
-3,728.73
M_2
-3,899.52
M_3
1,406.35
mL1
2,711.00
mL3
2,975.22
mE1
3,778.27
mE3
1,263.62
mQ1
1,858.32
mQ3
2,227.51
mU1
2,741.55
mU3
3,805.75
mD1
3,827.97
mD3
782.55
At
-3,778.07
Ab
2,587.41
Atau
-1,088.14
mu
-603.19
mA
2,186.92
tan(beta)
8.24
The truth!
4
M_1
346.83
M_2
-1,002.07
M_3
3,824.80
mL1
2,769.16
mL3
3,559.74
mE1
1,980.90
mE3
1,230.41
mQ1
2,922.19
mQ3
3,057.64
mU1
2,942.28
mU3
3,536.20
mD1
2,121.64
mD3
2,766.11
At
-2,762.27
Ab
-3,159.96
Atau
1,706.35
mu
340.31
mA
1,001.32
tan(beta)
35.37
The truth!
5
M_1
646.50
M_2
1,208.48
M_3
1,205.90
mL1
3,287.70
mL3
2,216.49
mE1
2,295.29
mE3
1,521.53
mQ1
765.40
mQ3
2,954.29
mU1
2,929.61
mU3
2,523.74
mD1
526.20
mD3
3,392.85
At
-3,860.52
Ab
83.81
Atau
2,221.14
mu
-937.37
mA
3,376.18
tan(beta)
41.36
The truth!
6
M_1
513.46
M_2
-1,644.59
M_3
1,137.07
mL1
3,553.20
mL3
1,214.71
mE1
3,567.61
mE3
3,778.25
mQ1
1,650.02
mQ3
3,641.33
mU1
3,744.83
mU3
3,507.73
mD1
3,409.93
mD3
326.59
At
-4,766.28
Ab
2,957.71
Atau
2,020.72
mu
3,015.14
mA
1,858.52
tan(beta)
29.18
The truth!
7
M_1
-619.53
M_2
1,587.33
M_3
2,787.56
mL1
3,134.99
mL3
1,153.77
mE1
2,642.35
mE3
2,790.00
mQ1
1,773.00
mQ3
3,424.88
mU1
640.17
mU3
3,532.48
mD1
1,739.97
mD3
772.66
At
3,774.65
Ab
-1,975.54
Atau
3,323.91
mu
-1,041.50
mA
1,068.74
tan(beta)
19.79
The truth!
8
M_1
711.16
M_2
-1,112.29
M_3
1,919.01
mL1
2,777.50
mL3
1,999.32
mE1
752.02
mE3
1,992.70
mQ1
2,927.16
mQ3
3,620.72
mU1
3,710.97
mU3
2,918.17
mD1
717.99
mD3
475.78
At
-3,426.70
Ab
-3,910.25
Atau
-933.84
mu
3,242.17
mA
2,617.73
tan(beta)
26.61
The truth!
9
M_1
-2,581.33
M_2
359.93
M_3
3,119.93
mL1
3,419.13
mL3
3,099.08
mE1
1,255.93
mE3
2,303.87
mQ1
1,275.71
mQ3
805.80
mU1
994.63
mU3
1,519.15
mD1
1,183.47
mD3
2,949.87
At
2,065.25
Ab
-2,599.94
Atau
1,877.87
mu
2,080.88
mA
3,976.83
tan(beta)
44.60
The truth!
10
M_1
2,197.32
M_2
102.11
M_3
2,526.63
mL1
3,102.57
mL3
500.10
mE1
1,414.32
mE3
183.53
mQ1
2,064.92
mQ3
2,508.08
mU1
2,669.55
mU3
2,366.41
mD1
3,703.77
mD3
3,312.02
At
2,989.93
Ab
-711.24
Atau
-2,153.82
mu
-1,479.14
mA
1,766.05
tan(beta)
31.65
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
