207 research outputs found
Streamlined Lensed Quasar Identification in Multiband Images via Ensemble Networks
Quasars experiencing strong lensing offer unique viewpoints on subjects
related to the cosmic expansion rate, the dark matter profile within the
foreground deflectors, and the quasar host galaxies. Unfortunately, identifying
them in astronomical images is challenging since they are overwhelmed by the
abundance of non-lenses. To address this, we have developed a novel approach by
ensembling cutting-edge convolutional networks (CNNs) -- for instance, ResNet,
Inception, NASNet, MobileNet, EfficientNet, and RegNet -- along with vision
transformers (ViTs) trained on realistic galaxy-quasar lens simulations based
on the Hyper Suprime-Cam (HSC) multiband images. While the individual model
exhibits remarkable performance when evaluated against the test dataset,
achieving an area under the receiver operating characteristic curve of 97.3%
and a median false positive rate of 3.6%, it struggles to generalize in real
data, indicated by numerous spurious sources picked by each classifier. A
significant improvement is achieved by averaging these CNNs and ViTs, resulting
in the impurities being downsized by factors up to 50. Subsequently, combining
the HSC images with the UKIRT, VISTA, and unWISE data, we retrieve
approximately 60 million sources as parent samples and reduce this to 892,609
after employing a photometry preselection to discover lensed quasars
with Einstein radii of arcsec. Afterward, the ensemble
classifier indicates 3080 sources with a high probability of being lenses, for
which we visually inspect, yielding 210 prevailing candidates awaiting
spectroscopic confirmation. These outcomes suggest that automated deep learning
pipelines hold great potential in effectively detecting strong lenses in vast
datasets with minimal manual visual inspection involved.Comment: Accepted for publication in the Astronomy & Astrophysics journal. 28
pages, 11 figures, and 3 tables. We welcome comments from the reade
The BarYon CYCLE Project (ByCycle): Identifying and Localizing MgII Metal Absorbers with Machine Learning
The upcoming ByCycle project on the VISTA/4MOST multi-object spectrograph
will offer new prospects of using a massive sample of million high
spectral resolution ( = 20,000) background quasars to map the circumgalactic
metal content of foreground galaxies (observed at = 4000 - 7000), as traced
by metal absorption. Such large surveys require specialized analysis
methodologies. In the absence of early data, we instead produce synthetic 4MOST
high-resolution fibre quasar spectra. To do so, we use the TNG50 cosmological
magnetohydrodynamical simulation, combining photo-ionization post-processing
and ray tracing, to capture MgII (, ) absorbers. We
then use this sample to train a Convolutional Neural Network (CNN) which
searches for, and estimates the redshift of, MgII absorbers within these
spectra. For a test sample of quasar spectra with uniformly distributed
properties (, \AA, ), the algorithm has a robust
classification accuracy of 98.6 per cent and a mean wavelength accuracy of 6.9
\AA. For high signal-to-noise spectra (), the algorithm robustly
detects and localizes MgII absorbers down to equivalent widths of
\AA. For the lowest SNR spectra
(), the CNN reliably recovers and localizes
EW 0.75 \AA\, absorbers. This is more
than sufficient for subsequent Voigt profile fitting to characterize the
detected MgII absorbers. We make the code publicly available through GitHub.
Our work provides a proof-of-concept for future analyses of quasar spectra
datasets numbering in the millions, soon to be delivered by the next generation
of surveys.Comment: 13 pages, 9 figures, 1 table. Accepted for publication in MNRA
Extragalactic Radio Continuum Surveys and the Transformation of Radio Astronomy
Next-generation radio surveys are about to transform radio astronomy by
discovering and studying tens of millions of previously unknown radio sources.
These surveys will provide new insights to understand the evolution of
galaxies, measuring the evolution of the cosmic star formation rate, and
rivalling traditional techniques in the measurement of fundamental cosmological
parameters. By observing a new volume of observational parameter space, they
are also likely to discover unexpected new phenomena. This review traces the
evolution of extragalactic radio continuum surveys from the earliest days of
radio astronomy to the present, and identifies the challenges that must be
overcome to achieve this transformational change.Comment: To be published in Nature Astronomy 18 Sept 201
On the application of machine learning in astronomy and astrophysics: A text-mining-based scientometric analysis
Since the beginning of the 21st century, the fields of astronomy and astrophysics
have experienced significant growth at observational and computational levels,
leading to the acquisition of increasingly huge volumes of data. In order to process
this vast quantity of information, artificial intelligence (AI) techniques are being
combined with data mining to detect patterns with the aim of modeling, classifying
or predicting the behavior of certain astronomical phenomena or objects.
Parallel to the exponential development of the aforementioned techniques, the
scientific output related to the application of AI and machine learning (ML) in
astronomy and astrophysics has also experienced considerable growth in recent
years. Therefore, the increasingly abundant articles make it difficult to monitor
this field in terms of which research topics are the most prolific or novel, or which
countries or authors are leading them. In this article, a text-mining-based
scientometric analysis of scientific documents published over the last three
decades on the application of AI and ML in the fields of astronomy and astrophysics
is presented. The VOSviewer software and data from the Web of Science
(WoS) are used to elucidate the evolution of publications in this research field,
their distribution by country (including co-authorship), the most relevant topics
addressed, and the most cited elements and most significant co-citations according
to publication source and authorship. The obtained results demonstrate how
application of AI/ML to the fields of astronomy/astrophysics represents an
established and rapidly growing field of research that is crucial to obtaining
scientific understanding of the universe
Intelligence of Astronomical Optical Telescope: Present Status and Future Perspectives
Artificial intelligence technology has been widely used in astronomy, and new
artificial intelligence technologies and application scenarios are constantly
emerging. There have been a large number of papers reviewing the application of
artificial intelligence technology in astronomy. However, relevant articles
seldom mention telescope intelligence separately, and it is difficult to
understand the current development status and research hotspots of telescope
intelligence from these papers. This paper combines the development history of
artificial intelligence technology and the difficulties of critical
technologies of telescopes, comprehensively introduces the development and
research hotspots of telescope intelligence, then conducts statistical analysis
on various research directions of telescope intelligence and defines the
research directions' merits. All kinds of research directions are evaluated,
and the research trend of each telescope's intelligence is pointed out.
Finally, according to the advantages of artificial intelligence technology and
the development trend of telescopes, future research hotspots of telescope
intelligence are given.Comment: 19 pages, 6 figure, for questions or comments, please email
[email protected]
Finding strong lenses in CFHTLS using convolutional neural networks
We train and apply convolutional neural networks, a machine learning
technique developed to learn from and classify image data, to
Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) imaging for the
identification of potential strong lensing systems. An ensemble of four
convolutional neural networks was trained on images of simulated galaxy-galaxy
lenses. The training sets consisted of a total of 62,406 simulated lenses and
64,673 non-lens negative examples generated with two different methodologies.
The networks were able to learn the features of simulated lenses with accuracy
of up to 99.8% and a purity and completeness of 94-100% on a test set of 2000
simulations. An ensemble of trained networks was applied to all of the 171
square degrees of the CFHTLS wide field image data, identifying 18,861
candidates including 63 known and 139 other potential lens candidates. A second
search of 1.4 million early type galaxies selected from the survey catalog as
potential deflectors, identified 2,465 candidates including 117 previously
known lens candidates, 29 confirmed lenses/high-quality lens candidates, 266
novel probable or potential lenses and 2097 candidates we classify as false
positives. For the catalog-based search we estimate a completeness of 21-28%
with respect to detectable lenses and a purity of 15%, with a false-positive
rate of 1 in 671 images tested. We predict a human astronomer reviewing
candidates produced by the system would identify ~20 probable lenses and 100
possible lenses per hour in a sample selected by the robot. Convolutional
neural networks are therefore a promising tool for use in the search for lenses
in current and forthcoming surveys such as the Dark Energy Survey and the Large
Synoptic Survey Telescope.Comment: 16 pages, 8 figures. Accepted by MNRA
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