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
382.39
M_2
-2,177.05
M_3
891.44
mL1
1,965.76
mL3
2,369.65
mE1
2,093.48
mE3
3,824.57
mQ1
838.94
mQ3
1,752.53
mU1
259.93
mU3
3,976.18
mD1
713.21
mD3
319.99
At
-4,098.87
Ab
-3,254.21
Atau
3,433.30
mu
650.84
mA
2,417.23
tan(beta)
12.91
The truth!
2
M_1
455.78
M_2
-1,831.62
M_3
1,997.02
mL1
617.54
mL3
497.64
mE1
1,690.52
mE3
1,494.55
mQ1
3,271.71
mQ3
2,072.41
mU1
2,239.64
mU3
1,650.31
mD1
301.17
mD3
2,830.30
At
-3,033.11
Ab
-2,353.11
Atau
-699.04
mu
1,459.76
mA
3,047.65
tan(beta)
23.89
The truth!
3
M_1
-3,104.35
M_2
-3,315.08
M_3
3,375.28
mL1
3,525.21
mL3
3,630.05
mE1
3,768.42
mE3
438.38
mQ1
2,190.06
mQ3
3,098.87
mU1
1,205.66
mU3
1,652.33
mD1
3,360.29
mD3
3,190.24
At
-3,486.60
Ab
-184.76
Atau
3,294.48
mu
262.61
mA
730.96
tan(beta)
26.63
The truth!
4
M_1
-740.53
M_2
-1,173.12
M_3
3,331.66
mL1
1,751.02
mL3
1,294.80
mE1
3,980.74
mE3
2,913.72
mQ1
1,293.11
mQ3
1,302.53
mU1
1,627.72
mU3
3,568.21
mD1
1,810.24
mD3
2,029.38
At
5,410.64
Ab
1,803.20
Atau
-848.01
mu
-177.35
mA
938.37
tan(beta)
3.18
The truth!
5
M_1
-1,550.25
M_2
314.93
M_3
687.14
mL1
1,309.55
mL3
1,077.66
mE1
1,816.64
mE3
1,850.74
mQ1
3,556.86
mQ3
3,552.22
mU1
2,989.67
mU3
3,993.89
mD1
647.91
mD3
2,895.67
At
-1,567.74
Ab
2,812.42
Atau
-3,369.74
mu
1,971.20
mA
3,716.80
tan(beta)
27.37
The truth!
6
M_1
311.85
M_2
-1,868.14
M_3
249.19
mL1
1,193.83
mL3
3,062.79
mE1
1,505.11
mE3
2,504.64
mQ1
260.64
mQ3
3,168.73
mU1
3,206.38
mU3
3,869.34
mD1
1,050.89
mD3
1,646.00
At
-3,857.94
Ab
-2,542.28
Atau
3,314.88
mu
1,415.98
mA
3,193.45
tan(beta)
22.23
The truth!
7
M_1
1,067.62
M_2
-1,059.65
M_3
2,232.30
mL1
3,656.31
mL3
1,497.99
mE1
3,390.50
mE3
2,061.13
mQ1
3,307.44
mQ3
3,671.64
mU1
425.69
mU3
2,500.14
mD1
1,303.80
mD3
321.84
At
-3,843.00
Ab
1,680.85
Atau
1,120.42
mu
159.41
mA
3,388.95
tan(beta)
10.54
The truth!
8
M_1
-1,714.95
M_2
353.26
M_3
2,007.80
mL1
1,076.91
mL3
2,326.70
mE1
3,094.13
mE3
1,415.83
mQ1
3,247.87
mQ3
2,527.01
mU1
3,437.04
mU3
3,354.03
mD1
2,454.74
mD3
1,307.36
At
-3,675.15
Ab
602.82
Atau
-802.65
mu
3,811.06
mA
1,699.33
tan(beta)
32.92
The truth!
9
M_1
-472.67
M_2
3,737.72
M_3
700.06
mL1
2,991.52
mL3
3,342.58
mE1
2,719.13
mE3
446.99
mQ1
2,148.97
mQ3
867.80
mU1
416.60
mU3
3,104.57
mD1
729.94
mD3
1,240.07
At
2,729.20
Ab
3,233.67
Atau
3,382.67
mu
-841.77
mA
2,511.24
tan(beta)
37.91
The truth!
10
M_1
290.28
M_2
-293.50
M_3
1,192.12
mL1
3,322.77
mL3
1,156.79
mE1
3,958.97
mE3
1,395.05
mQ1
2,423.42
mQ3
1,774.99
mU1
733.60
mU3
3,990.20
mD1
341.74
mD3
2,960.41
At
-2,992.36
Ab
12.60
Atau
1,602.51
mu
503.94
mA
930.89
tan(beta)
43.16
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
