71 research outputs found
Deep Learning Segmentation of Spiral Arms and Bars
We present the first deep learning model for segmenting galactic spiral arms
and bars. In a blinded assessment by expert astronomers, our predicted spiral
arm masks are preferred over both current automated methods (99% of
evaluations) and our original volunteer labels (79% of evaluations). Experts
rated our spiral arm masks as `mostly good' to `perfect' in 89% of evaluations.
Bar lengths trivially derived from our predicted bar masks are in excellent
agreement with a dedicated crowdsourcing project. The pixelwise precision of
our masks, previously impossible at scale, will underpin new research into how
spiral arms and bars evolve.Comment: Accepted at Machine Learning and the Physical Sciences Workshop,
NeurIPS 202
Deep Learning Segmentation of Spiral Arms and Bars
Machine Learning and the Physical Sciences Workshop, NeurIPS 2023. This is an open access conference contribution distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/We present the first deep learning model for segmenting galactic spiral arms and bars. In a blinded assessment by expert astronomers, our predicted spiral arm masks are preferred over both current automated methods (99% of evaluations) and our original volunteer labels (79% of evaluations). Experts rated our spiral arm masks as `mostly good' to `perfect' in 89% of evaluations. Bar lengths trivially derived from our predicted bar masks are in excellent agreement with a dedicated crowdsourcing project. The pixelwise precision of our masks, previously impossible at scale, will underpin new research into how spiral arms and bars evolve.Peer reviewe
Modeling with the Crowd: Optimizing the Human-Machine Partnership with Zooniverse
LSST and Euclid must address the daunting challenge of analyzing the
unprecedented volumes of imaging and spectroscopic data that these
next-generation instruments will generate. A promising approach to overcoming
this challenge involves rapid, automatic image processing using appropriately
trained Deep Learning (DL) algorithms. However, reliable application of DL
requires large, accurately labeled samples of training data. Galaxy Zoo Express
(GZX) is a recent experiment that simulated using Bayesian inference to
dynamically aggregate binary responses provided by citizen scientists via the
Zooniverse crowd-sourcing platform in real time. The GZX approach enables
collaboration between human and machine classifiers and provides rapidly
generated, reliably labeled datasets, thereby enabling online training of
accurate machine classifiers. We present selected results from GZX and show how
the Bayesian aggregation engine it uses can be extended to efficiently provide
object-localization and bounding-box annotations of two-dimensional data with
quantified reliability. DL algorithms that are trained using these annotations
will facilitate numerous panchromatic data modeling tasks including
morphological classification and substructure detection in direct imaging, as
well as decontamination and emission line identification for slitless
spectroscopy. Effectively combining the speed of modern computational analyses
with the human capacity to extrapolate from few examples will be critical if
the potential of forthcoming large-scale surveys is to be realized.Comment: 5 pages, 1 figure. To appear in Proceedings of the International
Astronomical Unio
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
Astronomaly at Scale: Searching for Anomalies Amongst 4 Million Galaxies
Modern astronomical surveys are producing datasets of unprecedented size and
richness, increasing the potential for high-impact scientific discovery. This
possibility, coupled with the challenge of exploring a large number of sources,
has led to the development of novel machine-learning-based anomaly detection
approaches, such as Astronomaly. For the first time, we test the scalability of
Astronomaly by applying it to almost 4 million images of galaxies from the Dark
Energy Camera Legacy Survey. We use a trained deep learning algorithm to learn
useful representations of the images and pass these to the anomaly detection
algorithm isolation forest, coupled with Astronomaly's active learning method,
to discover interesting sources. We find that data selection criteria have a
significant impact on the trade-off between finding rare sources such as strong
lenses and introducing artefacts into the dataset. We demonstrate that active
learning is required to identify the most interesting sources and reduce
artefacts, while anomaly detection methods alone are insufficient. Using
Astronomaly, we find 1635 anomalies among the top 2000 sources in the dataset
after applying active learning, including 8 strong gravitational lens
candidates, 1609 galaxy merger candidates, and 18 previously unidentified
sources exhibiting highly unusual morphology. Our results show that by
leveraging the human-machine interface, Astronomaly is able to rapidly identify
sources of scientific interest even in large datasets.Comment: 15 pages, 9 figures. Comments welcome, especially suggestions about
the anomalous source
The rheumatoid foot: a systematic literature review of patient-reported outcome measures
<p>Abstract</p> <p>Background</p> <p>The foot is often the first area of the body to be systematically affected by rheumatoid arthritis. The multidimensional consequences of foot problems for patients can be subjectively evaluated using patient-reported outcome measures (PROMs). However, there is currently no systematic review which has focused specifically upon the PROMs available for the foot with rheumatoid arthritis. The aim of this systematic review was to appraise the foot-specific PROMs available for the assessment and/or evaluation of the foot affected with rheumatoid arthritis.</p> <p>Methods</p> <p>A systematic search of databases was conducted according to pre-defined inclusion/exclusion criteria. PROMs identified were reviewed in terms of: conceptual bases, quality of construction, measurement aims and evidence to support their measurement properties.</p> <p>Results</p> <p>A total of 11 PROMs were identified and 5 papers that provided evidence for the measurement properties of some of the PROMs. Only one of the PROMs was found to be RA disease-specific. The quality of construction, pretesting and presence of evidence for their measurement properties was found to be highly variable. Conceptual bases of many of the PROMs was either restricted or based on reductionist biomedical models. All of the PROMs were found to consist of fixed scales.</p> <p>Conclusions</p> <p>There is a need to develop an RA-disease and foot-specific PROM with a greater emphasis on a biopsychosocial conceptual basis, cognitive pre-testing methods, patient preference-based qualities and evidence to support the full complement of measurement properties.</p
Practical galaxy morphology tools from deep supervised representation learning
Astronomers have typically set out to solve supervised machine learning problems by creating their own representations
from scratch. We show that deep learning models trained to answer every Galaxy Zoo DECaLS question learn meaningful
semantic representations of galaxies that are useful for new tasks on which the models were never trained. We exploit these
representations to outperform several recent approaches at practical tasks crucial for investigating large galaxy samples. The
first task is identifying galaxies of similar morphology to a query galaxy. Given a single galaxy assigned a free text tag by
humans (e.g. â#diffuseâ), we can find galaxies matching that tag for most tags. The second task is identifying the most interesting
anomalies to a particular researcher. Our approach is 100 per cent accurate at identifying the most interesting 100 anomalies (as
judged by Galaxy Zoo 2 volunteers). The third task is adapting a model to solve a new task using only a small number of newly
labelled galaxies. Models fine-tuned from our representation are better able to identify ring galaxies than models fine-tuned from
terrestrial images (ImageNet) or trained from scratch. We solve each task with very few new labels; either one (for the similarity
search) or several hundred (for anomaly detection or fine-tuning)
Radio Galaxy Zoo: Towards building the first multi-purpose foundation model for radio astronomy with self-supervised learning
In this work, we apply self-supervised learning with instance differentiation
to learn a robust, multi-purpose representation for image analysis of resolved
extragalactic continuum images. We train a multi-use model which compresses our
unlabelled data into a structured, low dimensional representation which can be
used for a variety of downstream tasks (e.g. classification, similarity
search). We exceed baseline supervised Fanaroff-Riley classification
performance by a statistically significant margin, with our model reducing the
test set error by up to half. Our model is also able to maintain high
classification accuracy with very few labels, with only 7.79% error when only
using 145 labels. We further demonstrate that by using our foundation model,
users can efficiently trade off compute, human labelling cost and test set
accuracy according to their respective budgets, allowing for efficient
classification in a wide variety of scenarios. We highlight the
generalizability of our model by showing that it enables accurate
classification in a label scarce regime with data from the new MIGHTEE survey
without any hyper-parameter tuning, where it improves upon the baseline by ~8%.
Visualizations of our labelled and un-labelled data show that our model's
representation space is structured with respect to physical properties of the
sources, such as angular source extent. We show that the learned representation
is scientifically useful even if no labels are available by performing a
similarity search, finding hybrid sources in the RGZ DR1 data-set without any
labels. We show that good augmentation design and hyper-parameter choice can
help achieve peak performance, while emphasising that optimal hyper-parameters
are not required to obtain benefits from self-supervised pre-training
Identification of Low Surface Brightness Tidal Features in Galaxies Using Convolutional Neural Networks
Faint tidal features around galaxies record their merger and interaction
histories over cosmic time. Due to their low surface brightnesses and complex
morphologies, existing automated methods struggle to detect such features and
most work to date has heavily relied on visual inspection. This presents a
major obstacle to quantitative study of tidal debris features in large
statistical samples, and hence the ability to be able to use these features to
advance understanding of the galaxy population as a whole. This paper uses
convolutional neural networks (CNNs) with dropout and augmentation to identify
galaxies in the CFHTLS-Wide Survey that have faint tidal features. Evaluating
the performance of the CNNs against previously-published expert visual
classifications, we find that our method achieves high (76%) completeness and
low (20%) contamination, and also performs considerably better than other
automated methods recently applied in the literature. We argue that CNNs offer
a promising approach to effective automatic identification of low surface
brightness tidal debris features in and around galaxies. When applied to
forthcoming deep wide-field imaging surveys (e.g. LSST, Euclid), CNNs have the
potential to provide a several order-of-magnitude increase in the sample size
of morphologically-perturbed galaxies and thereby facilitate a much-anticipated
revolution in terms of quantitative low surface brightness science.Comment: 16 pages, 18 figures, accepted for publication in MNRA
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