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
42.66
M_2
-533.61
M_3
1,995.79
mL1
485.17
mL3
2,505.16
mE1
963.03
mE3
2,260.26
mQ1
666.26
mQ3
1,910.73
mU1
1,289.53
mU3
3,621.01
mD1
1,534.85
mD3
2,577.04
At
3,967.67
Ab
2,588.85
Atau
-3,816.80
mu
-148.63
mA
813.88
tan(beta)
4.88
The truth!
2
M_1
-3,720.15
M_2
860.07
M_3
3,050.40
mL1
2,456.67
mL3
3,760.36
mE1
2,361.23
mE3
2,326.61
mQ1
884.71
mQ3
2,147.18
mU1
3,410.18
mU3
2,515.78
mD1
2,650.40
mD3
2,688.76
At
-5,653.72
Ab
3,611.82
Atau
566.22
mu
-3,250.43
mA
3,341.73
tan(beta)
6.35
The truth!
3
M_1
-441.04
M_2
3,366.63
M_3
3,238.26
mL1
3,358.00
mL3
2,230.00
mE1
2,701.46
mE3
1,430.55
mQ1
333.51
mQ3
3,466.62
mU1
473.48
mU3
1,328.74
mD1
399.41
mD3
1,521.04
At
-4,360.27
Ab
-3,897.06
Atau
1,448.91
mu
1,294.32
mA
3,322.20
tan(beta)
58.77
The truth!
4
M_1
3,502.37
M_2
1,066.27
M_3
2,713.55
mL1
2,310.02
mL3
1,065.65
mE1
1,880.35
mE3
2,176.92
mQ1
3,066.30
mQ3
2,780.65
mU1
3,961.46
mU3
2,661.24
mD1
2,124.02
mD3
2,471.71
At
-2,738.98
Ab
3,679.82
Atau
1,545.50
mu
196.84
mA
1,715.01
tan(beta)
17.52
The truth!
5
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!
6
M_1
-740.66
M_2
3,612.59
M_3
3,982.16
mL1
2,771.25
mL3
2,718.49
mE1
2,282.71
mE3
2,986.50
mQ1
1,893.00
mQ3
3,782.87
mU1
3,926.98
mU3
2,709.69
mD1
476.66
mD3
1,460.18
At
3,607.38
Ab
-1,093.99
Atau
-1,838.38
mu
-1,380.74
mA
2,201.21
tan(beta)
56.12
The truth!
7
M_1
-2,646.76
M_2
3,071.01
M_3
689.90
mL1
2,431.43
mL3
896.89
mE1
734.86
mE3
2,136.03
mQ1
1,549.02
mQ3
3,583.96
mU1
3,824.79
mU3
3,075.25
mD1
3,756.95
mD3
1,048.83
At
3,492.30
Ab
-1,643.92
Atau
-2,586.01
mu
-629.90
mA
2,339.83
tan(beta)
19.13
The truth!
8
M_1
-738.04
M_2
-3,980.17
M_3
3,862.87
mL1
3,075.59
mL3
3,704.88
mE1
1,720.73
mE3
1,014.17
mQ1
330.05
mQ3
2,734.03
mU1
512.14
mU3
3,961.80
mD1
1,433.29
mD3
3,669.73
At
4,127.03
Ab
2,452.18
Atau
2,638.84
mu
-3,078.85
mA
1,734.03
tan(beta)
35.97
The truth!
9
M_1
2,479.16
M_2
366.99
M_3
2,031.24
mL1
1,240.84
mL3
3,357.73
mE1
646.99
mE3
2,199.85
mQ1
3,927.17
mQ3
1,165.23
mU1
3,921.39
mU3
2,640.02
mD1
2,321.65
mD3
3,456.32
At
-3,087.31
Ab
3,498.91
Atau
3,919.29
mu
-2,243.13
mA
3,202.75
tan(beta)
28.28
The truth!
10
M_1
140.60
M_2
-2,371.23
M_3
839.76
mL1
2,135.60
mL3
3,991.96
mE1
1,023.51
mE3
3,146.79
mQ1
329.13
mQ3
3,261.38
mU1
965.77
mU3
3,418.26
mD1
1,796.88
mD3
3,807.84
At
4,293.80
Ab
-3,866.17
Atau
649.41
mu
140.10
mA
2,141.49
tan(beta)
26.80
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
