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
3,630.28
M_2
-381.86
M_3
3,757.81
mL1
3,980.20
mL3
3,647.61
mE1
2,444.86
mE3
1,800.42
mQ1
844.33
mQ3
3,954.82
mU1
3,245.40
mU3
3,064.95
mD1
2,873.34
mD3
1,344.12
At
-4,974.86
Ab
3,683.97
Atau
-232.69
mu
2,834.05
mA
3,859.09
tan(beta)
15.83
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
-1,728.40
M_2
3,474.84
M_3
1,648.89
mL1
3,378.87
mL3
3,979.90
mE1
3,285.58
mE3
3,480.75
mQ1
2,274.45
mQ3
3,566.33
mU1
715.56
mU3
3,616.51
mD1
2,364.56
mD3
1,043.97
At
4,529.41
Ab
-1,145.78
Atau
522.71
mu
-207.14
mA
588.95
tan(beta)
15.39
The truth!
4
M_1
-468.04
M_2
3,363.01
M_3
892.87
mL1
702.90
mL3
1,918.39
mE1
3,408.05
mE3
2,957.32
mQ1
524.73
mQ3
707.91
mU1
3,441.03
mU3
2,305.54
mD1
1,881.35
mD3
1,495.12
At
3,423.00
Ab
-2,827.97
Atau
-3,284.35
mu
-3,780.74
mA
1,675.46
tan(beta)
6.50
The truth!
5
M_1
-326.54
M_2
3,859.85
M_3
2,719.63
mL1
3,463.70
mL3
3,457.39
mE1
3,725.64
mE3
2,081.75
mQ1
2,411.08
mQ3
2,683.05
mU1
3,367.44
mU3
3,228.86
mD1
3,936.17
mD3
2,690.05
At
-3,015.07
Ab
2,047.24
Atau
-1,899.09
mu
-118.56
mA
704.54
tan(beta)
29.89
The truth!
6
M_1
-2,616.14
M_2
-1,738.23
M_3
3,199.54
mL1
1,424.78
mL3
2,888.02
mE1
3,551.64
mE3
1,430.58
mQ1
1,424.61
mQ3
2,401.42
mU1
1,115.27
mU3
2,680.66
mD1
1,484.34
mD3
3,083.13
At
5,325.74
Ab
-3,911.42
Atau
-1,318.06
mu
378.27
mA
3,726.18
tan(beta)
4.17
The truth!
7
M_1
3,214.35
M_2
1,873.11
M_3
494.51
mL1
536.32
mL3
2,852.29
mE1
892.34
mE3
484.03
mQ1
1,289.47
mQ3
1,962.68
mU1
1,134.59
mU3
2,071.01
mD1
1,971.44
mD3
1,115.29
At
-3,433.38
Ab
496.37
Atau
2,538.56
mu
329.62
mA
901.38
tan(beta)
10.18
The truth!
8
M_1
1,344.51
M_2
-3,815.23
M_3
3,578.27
mL1
2,940.75
mL3
1,115.36
mE1
3,358.07
mE3
3,966.86
mQ1
3,146.52
mQ3
1,652.51
mU1
671.29
mU3
2,827.79
mD1
1,913.34
mD3
3,261.46
At
3,356.60
Ab
2,047.21
Atau
2,539.99
mu
-119.79
mA
513.32
tan(beta)
36.67
The truth!
9
M_1
2,500.88
M_2
-2,184.32
M_3
1,238.34
mL1
710.26
mL3
3,269.72
mE1
2,543.91
mE3
2,042.27
mQ1
593.46
mQ3
1,984.88
mU1
1,152.30
mU3
1,819.10
mD1
1,985.28
mD3
1,229.23
At
2,686.81
Ab
2,448.32
Atau
1,933.09
mu
-115.69
mA
3,353.80
tan(beta)
21.35
The truth!
10
M_1
909.02
M_2
867.12
M_3
2,177.77
mL1
2,130.51
mL3
2,680.04
mE1
2,360.95
mE3
1,085.96
mQ1
2,567.26
mQ3
3,918.27
mU1
852.55
mU3
3,088.43
mD1
1,369.61
mD3
918.88
At
-3,911.18
Ab
-1,513.69
Atau
-2,134.46
mu
1,438.80
mA
855.99
tan(beta)
17.16
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
