Supervised Neural Networks for RFI Flagging. (arXiv:2007.14996v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Harrison_K/0/1/0/all/0/1">Kyle Harrison</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Mishra_A/0/1/0/all/0/1">Amit Kumar Mishra</a>
Neural network (NN) based methods are applied to the detection of radio
frequency interference (RFI) in post-correlation,post-calibration
time/frequency data. While calibration doesaffect RFI for the sake of this work
a reduced dataset inpost-calibration is used. Two machine learning
approachesfor flagging real measurement data are demonstrated usingthe existing
RFI flagging technique AOFlagger as a groundtruth. It is shown that a single
layer fully connects networkcan be trained using each time/frequency sample
individuallywith the magnitude and phase of each polarization and
Stokesvisibilities as features. This method was able to predict aBoolean flag
map for each baseline to a high degree of accuracy achieving a Recall of 0.69
and Precision of 0.83 and anF1-Score of 0.75.
Neural network (NN) based methods are applied to the detection of radio
frequency interference (RFI) in post-correlation,post-calibration
time/frequency data. While calibration doesaffect RFI for the sake of this work
a reduced dataset inpost-calibration is used. Two machine learning
approachesfor flagging real measurement data are demonstrated usingthe existing
RFI flagging technique AOFlagger as a groundtruth. It is shown that a single
layer fully connects networkcan be trained using each time/frequency sample
individuallywith the magnitude and phase of each polarization and
Stokesvisibilities as features. This method was able to predict aBoolean flag
map for each baseline to a high degree of accuracy achieving a Recall of 0.69
and Precision of 0.83 and anF1-Score of 0.75.
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