175 research outputs found
Transfer learning for radio galaxy classification
In the context of radio galaxy classification, most state-of-the-art neural
network algorithms have been focused on single survey data. The question of
whether these trained algorithms have cross-survey identification ability or
can be adapted to develop classification networks for future surveys is still
unclear. One possible solution to address this issue is transfer learning,
which re-uses elements of existing machine learning models for different
applications. Here we present radio galaxy classification based on a 13-layer
Deep Convolutional Neural Network (DCNN) using transfer learning methods
between different radio surveys. We find that our machine learning models
trained from a random initialization achieve accuracies comparable to those
found elsewhere in the literature. When using transfer learning methods, we
find that inheriting model weights pre-trained on FIRST images can boost model
performance when re-training on lower resolution NVSS data, but that inheriting
pre-trained model weights from NVSS and re-training on FIRST data impairs the
performance of the classifier. We consider the implication of these results in
the context of future radio surveys planned for next-generation radio
telescopes such as ASKAP, MeerKAT, and SKA1-MID
Testing protoplanetary disc dispersal with radio emission
We consider continuum free-free radio emission from the upper atmosphere of
protoplanetary discs as a probe of the ionized luminosity impinging upon the
disc. Making use of previously computed hydrodynamic models of disc
photoevaporation within the framework of EUV and X-ray irradiation, we use
radiative transfer post-processing techniques to predict the expected free-free
emission from protoplanetary discs. In general, the free-free luminosity scales
roughly linearly with ionizing luminosity in both EUV and X-ray driven
scenarios, where the emission dominates over the dust tail of the disc and is
partial optically thin at cm wavelengths. We perform a test observation of GM
Aur at 14-18 Ghz and detect an excess of radio emission above the dust tail to
a very high level of confidence. The observed flux density and spectral index
are consistent with free-free emission from the ionized disc in either the EUV
or X-ray driven scenario. Finally, we suggest a possible route to testing the
EUV and X-ray driven dispersal model of protoplanetary discs, by combining
observed free-free flux densities with measurements of mass-accretion rates. On
the point of disc dispersal one would expect to find a M_dot^2 scaling with
free-free flux in the case of EUV driven disc dispersal or a M_dot scaling in
the case of X-ray driven disc dispersal.Comment: Accepted MNRAS, 12 pages, 11 figures, (pdf generation fixed
MCMC to address model misspecification in Deep Learning classification of Radio Galaxies
The radio astronomy community is adopting deep learning techniques to deal
with the huge data volumes expected from the next-generation of radio
observatories. Bayesian neural networks (BNNs) provide a principled way to
model uncertainty in the predictions made by deep learning models and will play
an important role in extracting well-calibrated uncertainty estimates from the
outputs of these models. However, most commonly used approximate Bayesian
inference techniques such as variational inference and MCMC-based algorithms
experience a "cold posterior effect (CPE)", according to which the posterior
must be down-weighted in order to get good predictive performance. The CPE has
been linked to several factors such as data augmentation or dataset curation
leading to a misspecified likelihood and prior misspecification. In this work
we use MCMC sampling to show that a Gaussian parametric family is a poor
variational approximation to the true posterior and gives rise to the CPE
previously observed in morphological classification of radio galaxies using
variational inference based BNNs.Comment: Accepted in Machine Learning and the Physical Sciences Workshop at
NeurIPS 2023; 6 pages, 1 figure, 1 tabl
Limits on the validity of the thin-layer model of the ionosphere for radio interferometric calibration
For a ground-based radio interferometer observing at low frequencies, the
ionosphere causes propagation delays and refraction of cosmic radio waves which
result in phase errors in the received signal. These phase errors can be
corrected using a calibration method that assumes a two-dimensional phase
screen at a fixed altitude above the surface of the Earth, known as the
thin-layer model. Here we investigate the validity of the thin-layer model and
provide a simple equation with which users can check when this approximation
can be applied to observations for varying time of day, zenith angle,
interferometer latitude, baseline length, ionospheric electron content and
observing frequency.Comment: 8 pages, 10 figures, accepted MNRA
Efficient Source Finding for Radio Interferometric Images
Object detection in astronomical images, generically referred to as source
finding, is often performed before the object characterisation stage in
astrophysical processing work flows. In radio astronomy, source finding has
historically been performed by bespoke off-line systems; however, modern data
acquisition systems as well as those proposed for upcoming observatories such
as the Square Kilometre Array (SKA), will make this approach unfeasible. One
area where a change of approach is particularly necessary is in the design of
fast imaging systems for transient studies. This paper presents a number of
advances in accelerating and automating the source finding in such systems.Comment: submitted to Astronomy & Computin
Rare Galaxy Classes Identified In Foundation Model Representations
We identify rare and visually distinctive galaxy populations by searching for
structure within the learned representations of pretrained models. We show that
these representations arrange galaxies by appearance in patterns beyond those
needed to predict the pretraining labels. We design a clustering approach to
isolate specific local patterns, revealing groups of galaxies with rare and
scientifically-interesting morphologies.Comment: Accepted at Machine Learning and the Physical Sciences Workshop,
NeurIPS 202
Sub-arcsecond high sensitivity measurements of the DG~Tau jet with e-MERLIN
We present very high spatial resolution deep radio continuum observations at
5 GHz (6 cm) made with e-MERLIN of the young stars DG Tau A and B. Assuming it
is launched very close (~=1 au) from the star, our results suggest that the DG
Tau A outflow initially starts as a poorly focused wind and undergoes
significant collimation further along the jet (~=50 au). We derive jet
parameters for DG Tau A and find an initial jet opening angle of 86 degrees
within 2 au of the source, a mass-loss rate of 1.5x10^-8 solar masses/yr for
the ionised component of the jet, and the total ejection/accretion ratio to
range from 0.06-0.3. These results are in line with predictions from MHD
jet-launching theories.Comment: Accepted MNRAS Letter
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