Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Supervised Neural Networks for RFI Flagging   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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The categories of this paper are: nucl-ex
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The categories of this paper are: patt-sol
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The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
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The categories of this paper are: econ
12
The categories of this paper are: nucl-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom