RFI Classification using Neural Networks
Mike McCarty and Mark Whitehead (NRAO)
Introduction
Radio frequency interference with single dish telescopes observations is a well-known problem10,11
which has been restricting the scientific output of telescopes such as the GBT. While some RFI can be
avoided through mitigation techniques such as “Quiet Skies” legislation, RFI will continue to persist due
to satellite and other non-ground emissions. Removal of RFI from the data is, however, a difficult
problem. Many types of RFI are predictable and can be classified and removed from data provided a
good description of the signal properties is known, allowing for an excision algorithm to find the preidentified RFI lines and remove them from the data. This technique works extremely well but can be
extremely time and equipment consuming for the initial analysis of the signal10,11. The other common
technique for RFI removal is to look for anomalies within the data and flag those anomalies as RFI.
Again this technique works well, and is in fact commonly used, but it often leaves behind a small
signature from weak or improperly removed RFI.
One possible means for more effectively and efficiently removing RFI from radio astronomy data is
through the application of neural network classification. Recent work in radio astronomy demonstrates
the efficacy of applying neural network classification to automate radio astronomy data analysis2-9.
Here, we propose to produce a prototype neural network classification system to identify and flag RFI in
archived GBT data. We plan to use existing research and data to construct an RFI classification model,
implement the model via a back propagation neural network and apply the network to existing data to
quantitatively determine if this approach can detect RFI in radio wavelength observations. If successful,
this technique could be used as part of the standard NRAO data reduction techniques to open up
frequency space otherwise not useful to astronomers.
Activities and Goals
We will use archival, high time resolution, GBT data combined with the techniques outlined in Fisher and
Singhal 10,11 to empirically characterize RFI in the 700-900 MHz frequency range. (The frequency range is
chosen such that a successful technique will be immediately usable for the HI Intensity Mapping
project.) This will allow us to identify a set of representative parameters that describe RFI in
observational data. The parameters will then be used to identify an input vector and document any
required pre-processing procedures9. The scope of this part our work will be limited to spectral line data
from the GBT.
Next we will evaluate available packages and libraries useful for constructing classifiers. Some of the
packages we have identified to date are Fast Artificial Neural Network Library (FANN)12, neuralnet13, and
annie14. Once we select a package, we will begin implementing the preprocessing procedures to
produce an input vector, from observational data, in a format suitable for the chosen library and
programming language.
Finally, we will construct prototype classification models and evaluate them to determine a useful
classification method and neural network topology. Once the classification model is constructed using
actual observational data for training, we will test its accuracy using test data sets. Both training and
testing data sets will have been manually identified as either “clean spectrum” or “spectrum containing
RFI”. Standard accuracy measurements found in classification literature9,15 will be calculated using the
test data and neural network output.
We will publish our process and results in a technical memo. These results could be used to define a
strategy for a large-scale neural network to analyze GBT data and potentially a product which, much like
the GUPPI Pulsar Backend, may be shared or sold as a turn-key package to other radio observatories.
References
[1] McCarty, M.; Artifical Neural Network Applications in Astronomy;
http://www.gb.nrao.edu/~mmccarty/ann_astronomy.pdf
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of radio pulsar candidates using artificial neural networks, 10/2010,
http://adsabs.harvard.edu/abs/2010MNRAS.407.2443E
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[9] Bishop C. M., 1995, Neural Networks for Pattern Recognition. Oxford Univ. Press, Oxford
[10] Fisher, J. R.; Singhal, A.; A Study of Radar Signals Received by the Green Bank Telescope; 2006;
http://www.gb.nrao.edu/electronics/edir/edir316.pdf
[11] Fisher, J. R.; Signal Analysis and Blanking Experiments on DME Interference; 2004;
http://www.cv.nrao.edu/~rfisher/DME/dme_analysis.pdf
[12] Günther, Frauke; Fritsch Stefan; neuralnet: Training of Neural Networks; 1/2010; R Journal; http://journal.rproject.org/archive/2010-1/RJournal_2010-1_Guenther+Fritsch.pdf
[13] Fast Artificial Neural Network Library website http://leenissen.dk/fann/wp/
[14] Annie website http://annie.sourceforge.net/
[15] Han J., Kamber M., Pei J., 2012, Data Mining Concepts and Techniques, Morgan Kaufmann