Optimal trajectories for planetary pole-sitter missions

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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

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. ‘#diffuse’), 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

A New Task: Deriving Semantic Class Targets for the Physical Sciences

We define deriving semantic class targets as a novel multi-modal task. By doing so, we aim to improve classification schemes in the physical sciences which can be severely abstracted and obfuscating. We address this task for upcoming radio astronomy surveys and present the derived semantic radio galaxy morphology class targets.Comment: 6 pages, 1 figure, Accepted at Fifth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2022), Neural Information Processing Systems 202

Galaxy Zoo: Kinematics of strongly and weakly barred galaxies

We study the bar pattern speeds and corotation radii of 225 barred galaxies, using IFU data from MaNGA and the Tremaine-Weinberg method. Our sample, which is divided between strongly and weakly barred galaxies identified via Galaxy Zoo, is the largest that this method has been applied to. We find lower pattern speeds for strongly barred galaxies than for weakly barred galaxies. As simulations show that the pattern speed decreases as the bar exchanges angular momentum with its host, these results suggest that strong bars are more evolved than weak bars. Interestingly, the corotation radius is not different between weakly and strongly barred galaxies, despite being proportional to bar length. We also find that the corotation radius is significantly different between quenching and star forming galaxies. Additionally, we find that strongly barred galaxies have significantly lower values for R, the ratio between the corotation radius and the bar radius, than weakly barred galaxies, despite a big overlap in both distributions. This ratio classifies bars into ultrafast bars (R < 1.0; 11% of our sample), fast bars (1.0 < R < 1.4; 27%) and slow bars (R > 1.4; 62%). Simulations show that R is correlated with the bar formation mechanism, so our results suggest that strong bars are more likely to be formed by different mechanisms than weak bars. Finally, we find a lower fraction of ultrafast bars than most other studies, which decreases the recently claimed tension with {\Lambda}CDM. However, the median value of R is still lower than what is predicted by simulations.Comment: 20 pages, 16 figure

The effect of minor and major mergers on the evolution of low excitation radio galaxies

We use deep, μ r lesssim 28 mag arcsec−2, r-band imaging from the Dark Energy Camera Legacy Survey to search for past, or ongoing, merger activity in a sample of 282 low-excitation radio galaxies (LERGs) at z 4σ excess of major mergers in the LERGs with M * lesssim 1011 M⊙, with 10 ± 1.5% of these active galactic nuclei involved in such large-scale interactions compared to 3.2 ± 0.4% of control galaxies. This excess of major mergers in LERGs decreases with increasing stellar mass, vanishing by M * > 1011.3 M⊙. These observations show that minor mergers do not fuel LERGs, and are consistent with typical LERGs being powered by accretion of matter from their halo. Where LERGs are associated with major mergers, these objects may evolve into more efficiently accreting active galactic nuclei as the merger progresses and more gas falls on to the central engine