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
546.95
M_2
3,596.30
M_3
2,552.92
mL1
1,757.25
mL3
2,613.12
mE1
3,044.24
mE3
2,585.38
mQ1
3,062.95
mQ3
2,324.92
mU1
814.61
mU3
2,819.35
mD1
287.07
mD3
2,916.04
At
3,434.50
Ab
941.87
Atau
-2,055.51
mu
-2,501.84
mA
2,593.28
tan(beta)
20.94
The truth!
2
M_1
-50.46
M_2
1,245.09
M_3
2,813.64
mL1
400.33
mL3
461.45
mE1
3,499.92
mE3
3,705.93
mQ1
3,251.75
mQ3
3,827.64
mU1
1,055.15
mU3
3,548.28
mD1
3,084.23
mD3
1,041.99
At
-4,128.54
Ab
-1,155.73
Atau
2,392.80
mu
-145.79
mA
993.15
tan(beta)
14.29
The truth!
3
M_1
-1,509.33
M_2
3,351.45
M_3
562.79
mL1
1,713.29
mL3
1,306.43
mE1
2,876.56
mE3
3,707.27
mQ1
3,109.64
mQ3
250.01
mU1
3,616.29
mU3
2,901.57
mD1
3,805.58
mD3
3,931.12
At
2,663.46
Ab
2,907.25
Atau
-2,035.01
mu
99.20
mA
3,672.29
tan(beta)
11.55
The truth!
4
M_1
2,199.73
M_2
1,739.39
M_3
3,701.54
mL1
3,500.36
mL3
377.00
mE1
2,478.24
mE3
981.24
mQ1
1,132.06
mQ3
1,055.55
mU1
3,311.69
mU3
2,604.10
mD1
2,617.94
mD3
3,007.82
At
2,485.24
Ab
762.10
Atau
147.04
mu
-226.58
mA
2,187.61
tan(beta)
8.29
The truth!
5
M_1
566.90
M_2
-547.77
M_3
2,786.19
mL1
2,258.92
mL3
1,473.88
mE1
926.68
mE3
1,668.34
mQ1
3,856.31
mQ3
3,136.41
mU1
697.52
mU3
3,163.01
mD1
3,423.19
mD3
712.52
At
-3,902.12
Ab
-3,100.74
Atau
1,930.45
mu
2,938.07
mA
2,891.00
tan(beta)
57.44
The truth!
6
M_1
3,123.06
M_2
-256.44
M_3
1,228.54
mL1
3,263.53
mL3
2,107.76
mE1
2,740.54
mE3
1,236.85
mQ1
3,589.73
mQ3
468.49
mU1
3,682.34
mU3
3,814.63
mD1
3,063.98
mD3
3,353.92
At
-3,375.83
Ab
-2,687.98
Atau
3,837.51
mu
3,782.89
mA
4,664.84
tan(beta)
26.05
The truth!
7
M_1
-1,894.40
M_2
-3,713.20
M_3
3,607.66
mL1
1,725.58
mL3
3,109.44
mE1
2,336.25
mE3
1,556.47
mQ1
702.60
mQ3
2,649.70
mU1
1,527.57
mU3
3,220.60
mD1
1,964.58
mD3
2,996.35
At
-4,619.87
Ab
-3,210.59
Atau
2,837.47
mu
218.60
mA
1,478.49
tan(beta)
26.59
The truth!
8
M_1
-1,526.13
M_2
1,070.57
M_3
1,944.18
mL1
2,767.25
mL3
1,308.09
mE1
1,839.41
mE3
1,511.93
mQ1
3,176.73
mQ3
3,467.00
mU1
3,026.18
mU3
2,804.33
mD1
3,892.87
mD3
3,702.95
At
3,379.44
Ab
1,239.31
Atau
-1,367.97
mu
-2,499.47
mA
3,140.76
tan(beta)
41.00
The truth!
9
M_1
-159.46
M_2
-3,449.53
M_3
662.61
mL1
549.14
mL3
2,683.64
mE1
2,604.12
mE3
2,547.79
mQ1
2,583.28
mQ3
3,191.11
mU1
986.39
mU3
3,653.14
mD1
521.02
mD3
823.55
At
-3,601.90
Ab
70.58
Atau
3,703.97
mu
163.62
mA
1,042.50
tan(beta)
59.46
The truth!
10
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!
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
