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
879.31
M_2
-1,405.79
M_3
767.42
mL1
2,180.58
mL3
2,913.66
mE1
2,103.29
mE3
3,263.79
mQ1
2,617.14
mQ3
3,725.08
mU1
2,178.39
mU3
2,312.35
mD1
3,294.29
mD3
3,196.96
At
-3,469.89
Ab
1,493.88
Atau
-1,841.11
mu
823.06
mA
2,806.88
tan(beta)
22.09
The truth!
2
M_1
1,952.18
M_2
674.10
M_3
2,651.29
mL1
1,306.67
mL3
2,812.76
mE1
2,528.13
mE3
1,925.96
mQ1
2,097.62
mQ3
1,611.33
mU1
1,882.11
mU3
2,231.53
mD1
3,558.19
mD3
3,602.32
At
-3,131.44
Ab
-3,264.92
Atau
-2,846.05
mu
2,071.56
mA
4,399.10
tan(beta)
58.55
The truth!
3
M_1
-591.37
M_2
2,540.85
M_3
2,018.12
mL1
1,632.97
mL3
1,743.11
mE1
3,542.33
mE3
2,962.61
mQ1
1,157.05
mQ3
493.05
mU1
961.00
mU3
3,231.03
mD1
3,264.17
mD3
1,317.72
At
2,928.36
Ab
-2,151.51
Atau
2,178.98
mu
-3,087.36
mA
4,230.86
tan(beta)
9.94
The truth!
4
M_1
1,383.25
M_2
2,643.56
M_3
1,063.45
mL1
894.98
mL3
2,490.40
mE1
1,053.84
mE3
3,544.16
mQ1
2,976.04
mQ3
2,200.16
mU1
2,727.22
mU3
2,328.30
mD1
3,704.46
mD3
1,010.52
At
2,874.38
Ab
-411.52
Atau
-3,018.91
mu
-340.78
mA
3,074.75
tan(beta)
19.42
The truth!
5
M_1
-2,602.68
M_2
294.45
M_3
1,613.36
mL1
2,528.34
mL3
1,395.39
mE1
2,549.94
mE3
578.86
mQ1
3,402.53
mQ3
3,941.12
mU1
1,710.49
mU3
3,523.38
mD1
3,879.91
mD3
2,145.45
At
5,938.72
Ab
-2,718.96
Atau
805.64
mu
-1,056.74
mA
2,346.28
tan(beta)
3.74
The truth!
6
M_1
2,146.75
M_2
-2,674.43
M_3
2,149.97
mL1
1,808.02
mL3
654.19
mE1
1,164.62
mE3
3,466.81
mQ1
3,515.68
mQ3
3,015.35
mU1
552.75
mU3
3,179.73
mD1
3,432.75
mD3
336.86
At
-5,201.46
Ab
2,269.26
Atau
391.63
mu
-235.43
mA
3,869.70
tan(beta)
11.60
The truth!
7
M_1
-2,195.57
M_2
-1,159.86
M_3
2,383.87
mL1
3,600.61
mL3
3,789.27
mE1
3,274.51
mE3
3,827.15
mQ1
1,661.96
mQ3
2,599.32
mU1
1,587.70
mU3
3,636.91
mD1
1,965.54
mD3
3,586.89
At
2,988.15
Ab
1,556.23
Atau
181.82
mu
-333.15
mA
1,514.90
tan(beta)
58.09
The truth!
8
M_1
-2,002.38
M_2
-1,534.85
M_3
3,115.82
mL1
3,292.01
mL3
2,753.31
mE1
2,045.94
mE3
3,441.20
mQ1
3,154.70
mQ3
2,635.36
mU1
1,848.63
mU3
2,736.25
mD1
2,168.90
mD3
3,208.22
At
-2,896.06
Ab
-1,384.67
Atau
264.64
mu
2,117.33
mA
1,766.55
tan(beta)
49.72
The truth!
9
M_1
-3,415.07
M_2
3,634.67
M_3
1,461.11
mL1
2,854.29
mL3
1,786.52
mE1
1,636.65
mE3
786.44
mQ1
390.75
mQ3
1,997.05
mU1
1,791.82
mU3
2,047.81
mD1
502.92
mD3
2,675.31
At
-3,630.90
Ab
-670.17
Atau
-2,582.92
mu
185.66
mA
2,168.15
tan(beta)
32.20
The truth!
10
M_1
-664.43
M_2
-3,024.31
M_3
3,577.68
mL1
1,599.39
mL3
759.29
mE1
3,693.98
mE3
923.42
mQ1
2,069.15
mQ3
3,150.23
mU1
454.44
mU3
2,358.80
mD1
2,870.42
mD3
1,115.30
At
4,941.16
Ab
-618.63
Atau
-1,104.95
mu
-1,769.14
mA
1,464.34
tan(beta)
3.98
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
