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We present the MULTIMODAL UNIVERSE, a large-scale multimodal dataset of scientific astronomical data, compiled specifically to facilitate machine learning research. Overall, the MULTIMODAL UNIVERSE contains hundreds of millions of astronomical observations, constituting 100\,TB of multi-channel and hyper-spectral images, spectra, multivariate time series, as well as a wide variety of associated scientific measurements and "metadata". In addition, we include a range of benchmark tasks representative of standard practices for machine learning methods in astrophysics. This massive dataset will enable the development of large multi-modal models specifically targeted towards scientific applications. All codes used to compile the MULTIMODAL UNIVERSE and a description of how to access the data is available at https://github.com/MultimodalUniverse/MultimodalUniverse, Comment: Accepted at NeurIPS Datasets and Benchmarks track

Future optical quantum technologies, including quantum networks and distributed quantum computing and sensing, demand efficient, broadband quantum memories. However, achieving high efficiencies in optical quantum memory protocols is a significant challenge, and typical methods to increase the efficiency can often introduce noise, reduce the bandwidth, or limit scalability. Here, we present a new approach to enhancing quantum memory protocols by leveraging constructive light-matter interference. We implement this method in a Raman quantum memory in warm Cesium vapor, and achieve a more than three-fold improvement in total efficiency reaching $(34.3\pm8.4)\%$, while retaining GHz-bandwidth operation and low noise levels. Numerical simulations predict that this approach can boost efficiencies in systems limited by atomic density, such as cold atomic ensembles, from $65\%$ to beyond $96\%$, while in warm atomic vapors it could reduce the laser intensity to reach a given efficiency by over an order-of-magnitude, and exceed $95\%$ total efficiency. Furthermore, we find that our method preserves the single-mode nature of the memory at significantly higher efficiencies. This new protocol is applicable to various memory architectures, paving the way toward scalable, efficient, low-noise, and high-bandwidth quantum memories.

The Euclid Wide Survey (EWS) is predicted to find approximately 170 000 galaxy-galaxy strong lenses from its lifetime observation of 14 000 deg^2 of the sky. Detecting this many lenses by visual inspection with professional astronomers and citizen scientists alone is infeasible. Machine learning algorithms, particularly convolutional neural networks (CNNs), have been used as an automated method of detecting strong lenses, and have proven fruitful in finding galaxy-galaxy strong lens candidates. We identify the major challenge to be the automatic detection of galaxy-galaxy strong lenses while simultaneously maintaining a low false positive rate. One aim of this research is to have a quantified starting point on the achieved purity and completeness with our current version of CNN-based detection pipelines for the VIS images of EWS. We select all sources with VIS IE < 23 mag from the Euclid Early Release Observation imaging of the Perseus field. We apply a range of CNN architectures to detect strong lenses in these cutouts. All our networks perform extremely well on simulated data sets and their respective validation sets. However, when applied to real Euclid imaging, the highest lens purity is just 11%. Among all our networks, the false positives are typically identifiable by human volunteers as, for example, spiral galaxies, multiple sources, and artefacts, implying that improvements are still possible, perhaps via a second, more interpretable lens selection filtering stage. There is currently no alternative to human classification of CNN-selected lens candidates. Given the expected 10^5 lensing systems in Euclid, this implies 10^6 objects for human classification, which while very large is not in principle intractable and not without precedent., Comment: 22 pages, 11 figures, Euclid consortium paper, A&A submitted

Transition-Edge Sensors (TESs) are very effective photon-number-resolving (PNR) detectors that have enabled many photonic quantum technologies. However, their relatively slow thermal recovery time severely limits their operation rate in experimental scenarios compared to leading non-PNR detectors. In this work, we develop an algorithmic approach that enables TESs to detect and accurately classify photon pulses without waiting for a full recovery time between detection events. We propose two machine-learning-based signal processing methods: one supervised learning method and one unsupervised clustering method. By benchmarking against data obtained using coherent states and squeezed states, we show that the methods extend the TES operation rate to 800 kHz, achieving at least a four-fold improvement, whilst maintaining accurate photon-number assignment up to at least five photons. Our algorithms will find utility in applications where high rates of PNR detection are required and in technologies which demand fast active feed-forward of PNR detection outcomes., Comment: 18 pages, 7 figures including supplimental material

Galaxy Zoo is an online project to classify morphological features in extra-galactic imaging surveys with public voting. In this paper, we compare the classifications made for two different surveys, the Dark Energy Spectroscopic Instrument (DESI) imaging survey and a part of the Kilo-Degree Survey (KiDS), in the equatorial fields of the Galaxy And Mass Assembly (GAMA) survey. Our aim is to cross-validate and compare the classifications based on different imaging quality and depth. We find that generally the voting agrees globally but with substantial scatter i.e. substantial differences for individual galaxies. There is a notable higher voting fraction in favor of ``smooth'' galaxies in the DESI+\rev{{\sc zoobot}} classifications, most likely due to the difference between imaging depth. DESI imaging is shallower and slightly lower resolution than KiDS and the Galaxy Zoo images do not reveal details such as disk features \rev{and thus are missed in the {\sc zoobot} training sample}. \rev{We check against expert visual classifications and find good agreement with KiDS-based Galaxy Zoo voting.} We reproduce the results from Porter-Temple+ (2022), on the dependence of stellar mass, star-formation, and specific star-formation on the number of spiral arms. This shows that once corrected for redshift, the DESI Galaxy Zoo and KiDS Galaxy Zoo classifications agree well on population properties. The zoobot cross-validation increases confidence in its ability to compliment Galaxy Zoo classifications and its ability for transfer learning across surveys., Comment: 20 pages, 22 figures, 8 tables, accepted for publication in PASA

At the dynamic nexus of artificial intelligence and quantum technology, quantum neural networks (QNNs) play an important role as an emerging technology in the rapidly developing field of quantum machine learning. This development is set to revolutionize the applications of quantum computing. This article reviews the concept of QNNs and their physical realizations, particularly implementations based on quantum optics . We first examine the integration of quantum principles with classical neural network architectures to create QNNs. Some specific examples, such as the quantum perceptron, quantum convolutional neural networks, and quantum Boltzmann machines are discussed. Subsequently, we analyze the feasibility of implementing QNNs through photonics. The key challenge here lies in achieving the required non-linear gates, and measurement-induced approaches, among others, seem promising. To unlock the computational potential of QNNs, addressing the challenge of scaling their complexity through quantum optics is crucial. Progress in controlling quantum states of light is continuously advancing the field. Additionally, we have discovered that different QNN architectures can be unified through non-Gaussian operations. This insight will aid in better understanding and developing more complex QNN circuits.

We present morphological classifications based on Galaxy Zoo analysis of 71,052 galaxies with imaging from the United Kingdom Infrared Telescope Infrared Deep Sky Survey (UKIDSS). Galaxies were selected out of the Galaxy Zoo 2 (GZ2) sample, so also have gri imaging from the Sloan Digital Sky Survey. An identical classification tree, and vote weighting/aggregation was applied to both UKIDSS and GZ2 classifications enabling direct comparisons. With this Research Note we provide a public release of the GZ:UKIDSS morphologies and discuss some initial comparisons with GZ2., Comment: 3 pages, 1 figure

We investigate the ability of the Euclid telescope to detect galaxy-scale gravitational lenses. To do so, we perform a systematic visual inspection of the $0.7\,\rm{deg}^2$ Euclid ERO data towards the Perseus cluster using both the high-resolution VIS $I_{\scriptscriptstyle\rm E}$ band, and the lower resolution NISP bands. We inspect every extended source brighter than magnitude $23$ in $I_{\scriptscriptstyle\rm E}$ with $41$ expert human classifiers. This amounts to $12\,086$ stamps of $10^{\prime\prime}\,\times\,10^{\prime\prime}$. We find $3$ grade A and $13$ grade B candidates. We assess the validity of these $16$ candidates by modelling them and checking that they are consistent with a single source lensed by a plausible mass distribution. Five of the candidates pass this check, five others are rejected by the modelling and six are inconclusive. Extrapolating from the five successfully modelled candidates, we infer that the full $14\,000\,{\rm deg}^2$ of the Euclid Wide Survey should contain $100\,000^{+70\,000}_{-30\,000}$ galaxy-galaxy lenses that are both discoverable through visual inspection and have valid lens models. This is consistent with theoretical forecasts of $170\,000$ discoverable galaxy-galaxy lenses in Euclid. Our five modelled lenses have Einstein radii in the range $0.\!\!^{\prime\prime}68\,<\,\theta_\mathrm{E}\,<1.\!\!^{\prime\prime}24$, but their Einstein radius distribution is on the higher side when compared to theoretical forecasts. This suggests that our methodology is likely missing small Einstein radius systems. Whilst it is implausible to visually inspect the full Euclid data set, our results corroborate the promise that Euclid will ultimately deliver a sample of around $10^5$ galaxy-scale lenses., Comment: 21 pages, 20 figures, submitted to A&A

The exponential growth of astronomical literature poses significant challenges for researchers navigating and synthesizing general insights or even domain-specific knowledge. We present Pathfinder, a machine learning framework designed to enable literature review and knowledge discovery in astronomy, focusing on semantic searching with natural language instead of syntactic searches with keywords. Utilizing state-of-the-art large language models (LLMs) and a corpus of 350,000 peer-reviewed papers from the Astrophysics Data System (ADS), Pathfinder offers an innovative approach to scientific inquiry and literature exploration. Our framework couples advanced retrieval techniques with LLM-based synthesis to search astronomical literature by semantic context as a complement to currently existing methods that use keywords or citation graphs. It addresses complexities of jargon, named entities, and temporal aspects through time-based and citation-based weighting schemes. We demonstrate the tool's versatility through case studies, showcasing its application in various research scenarios. The system's performance is evaluated using custom benchmarks, including single-paper and multi-paper tasks. Beyond literature review, Pathfinder offers unique capabilities for reformatting answers in ways that are accessible to various audiences (e.g. in a different language or as simplified text), visualizing research landscapes, and tracking the impact of observatories and methodologies. This tool represents a significant advancement in applying AI to astronomical research, aiding researchers at all career stages in navigating modern astronomy literature., Comment: 25 pages, 9 figures, submitted to AAS jorunals. Comments are welcome, and the tools mentioned are available online at https://pfdr.app

Reliably characterised pulses are the starting point of any application of ultrafast techniques. Unfortunately, experimental constraints do not always allow optimising the characterisation conditions. This dictates the need for refined analysis methods. Here we show that neutral networks can provide a viable characterisation when applied to data from SPIDER. We have adopted a cascade of convolutional networks, addressing the multiparameter structure of the interferogram with a reasonable computing power. In particular, the necessity of precalibration is reduced, thus pointing towards the introduction of neural networks in more generic arrangements.

Despite the evidence that supermassive black holes (SMBHs) co-evolve with their host galaxy, and that most of the growth of these SMBHs occurs via merger-free processes, the underlying mechanisms which drive this secular co-evolution are poorly understood. We investigate the role that both strong and weak large-scale galactic bars play in mediating this relationship. Using 72,940 disc galaxies in a volume-limited sample from Galaxy Zoo DESI, we analyse the active galactic nucleus (AGN) fraction in strongly barred, weakly barred, and unbarred galaxies up to z = 0.1 over a range of stellar masses and colours. After controlling for stellar mass and colour, we find that the optically selected AGN fraction is 31.6 +/- 0.9 per cent in strongly barred galaxies, 23.3 +/- 0.8 per cent in weakly barred galaxies, and 14.2 +/- 0.6 per cent in unbarred disc galaxies. These are highly statistically robust results, strengthening the tantalising results in earlier works. Strongly barred galaxies have a higher fraction of AGNs than weakly barred galaxies, which in turn have a higher fraction than unbarred galaxies. Thus, while bars are not required in order to grow a SMBH in a disc galaxy, large-scale galactic bars appear to facilitate AGN fuelling, and the presence of a strong bar makes a disc galaxy more than twice as likely to host an AGN than an unbarred galaxy at all galaxy stellar masses and colours., Comment: 11 pages, 8 figures, accepted for publication in MNRAS

Knowledge of the nucleosynthetic isotope composition of the outermost protoplanetary disk is critical to understand the formation and early dynamical evolution of the Solar System. We report the discovery of outer disk material preserved in a pristine meteorite based on its chemical composition, organic-rich petrology, and 15N-rich, deuterium-rich, and 16O-poor isotope signatures. We infer that this outer disk material originated in the comet-forming region. The nucleosynthetic Fe, Mg, Si and Cr compositions of this material reveal that, contrary to current belief, the isotope signature of the comet-forming region is ubiquitous amongst outer Solar System bodies, possibly reflecting an important planetary building block in the outer Solar System. This nucleosynthetic component represents fresh material added to the outer disk by late accretion streamers connected to the ambient molecular cloud. Our results show that most Solar System carbonaceous asteroids accreted material from the comet-forming region, a signature lacking in the terrestrial planet region., Comment: Accepted manuscript, pre-print version

The current standard model of cosmology successfully describes a variety of measurements, but the nature of its main ingredients, dark matter and dark energy, remains unknown. Euclid is a medium-class mission in the Cosmic Vision 2015-2025 programme of the European Space Agency (ESA) that will provide high-resolution optical imaging, as well as near-infrared imaging and spectroscopy, over about 14,000 deg^2 of extragalactic sky. In addition to accurate weak lensing and clustering measurements that probe structure formation over half of the age of the Universe, its primary probes for cosmology, these exquisite data will enable a wide range of science. This paper provides a high-level overview of the mission, summarising the survey characteristics, the various data-processing steps, and data products. We also highlight the main science objectives and expected performance., Comment: Accepted for publication in the A&A special issue`Euclid on Sky'

We present the first systematic investigation of supervised scaling laws outside of an ImageNet-like context - on images of galaxies. We use 840k galaxy images and over 100M annotations by Galaxy Zoo volunteers, comparable in scale to Imagenet-1K. We find that adding annotated galaxy images provides a power law improvement in performance across all architectures and all tasks, while adding trainable parameters is effective only for some (typically more subjectively challenging) tasks. We then compare the downstream performance of finetuned models pretrained on either ImageNet-12k alone vs. additionally pretrained on our galaxy images. We achieve an average relative error rate reduction of 31% across 5 downstream tasks of scientific interest. Our finetuned models are more label-efficient and, unlike their ImageNet-12k-pretrained equivalents, often achieve linear transfer performance equal to that of end-to-end finetuning. We find relatively modest additional downstream benefits from scaling model size, implying that scaling alone is not sufficient to address our domain gap, and suggest that practitioners with qualitatively different images might benefit more from in-domain adaption followed by targeted downstream labelling., Comment: 10+6 pages, 12 figures. Appendix C2 based on arxiv:2206.11927. Code, demos, documentation at https://github.com/mwalmsley/zoobot

Various galaxy merger detection methods have been applied to diverse datasets. However, it is difficult to understand how they compare. We aim to benchmark the relative performance of machine learning (ML) merger detection methods. We explore six leading ML methods using three main datasets. The first one (the training data) consists of mock observations from the IllustrisTNG simulations and allows us to quantify the performance metrics of the detection methods. The second one consists of mock observations from the Horizon-AGN simulations, introduced to evaluate the performance of classifiers trained on different, but comparable data. The third one consists of real observations from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) survey. For the binary classification task (mergers vs. non-mergers), all methods perform reasonably well in the domain of the training data. At $0.1

The Euclid mission is expected to image millions of galaxies with high resolution, providing an extensive dataset to study galaxy evolution. We investigate the application of deep learning to predict the detailed morphologies of galaxies in Euclid using Zoobot a convolutional neural network pretrained with 450000 galaxies from the Galaxy Zoo project. We adapted Zoobot for emulated Euclid images, generated based on Hubble Space Telescope COSMOS images, and with labels provided by volunteers in the Galaxy Zoo: Hubble project. We demonstrate that the trained Zoobot model successfully measures detailed morphology for emulated Euclid images. It effectively predicts whether a galaxy has features and identifies and characterises various features such as spiral arms, clumps, bars, disks, and central bulges. When compared to volunteer classifications Zoobot achieves mean vote fraction deviations of less than 12% and an accuracy above 91% for the confident volunteer classifications across most morphology types. However, the performance varies depending on the specific morphological class. For the global classes such as disk or smooth galaxies, the mean deviations are less than 10%, with only 1000 training galaxies necessary to reach this performance. For more detailed structures and complex tasks like detecting and counting spiral arms or clumps, the deviations are slightly higher, around 12% with 60000 galaxies used for training. In order to enhance the performance on complex morphologies, we anticipate that a larger pool of labelled galaxies is needed, which could be obtained using crowdsourcing. Finally, our findings imply that the model can be effectively adapted to new morphological labels. We demonstrate this adaptability by applying Zoobot to peculiar galaxies. In summary, our trained Zoobot CNN can readily predict morphological catalogues for Euclid images., Comment: 27 pages, 26 figures, 5 tables, published in A&A

We present a numerical study, using the vortex filament model, of vortex tangles in a flow of pure superfluid $^4$He in the $T = 0$ limit through a channel of width $D = 1$ mm for various applied velocities $V$. The flat channel walls are assumed to be microscopically rough such that vortices terminating at the walls are permanently pinned; vortices are liberated from their pinned ends exclusively through self-reconnection with their images. Sustained tangles were observed, for a period of 80 s, above the critical velocity $V_c \sim 0.20$ cm s$^{-1} = 20 \kappa/D$. The coarse-grained velocity profile was akin to a classical parabolic profile of the laminar Poiseuille flow, albeit with a non-zero slip velocity $\sim$ 0.20 cm s$^{-1}$ at the walls. The friction force was found to be proportional to the applied velocity. The effective kinematic viscosity was $\sim 0.1\kappa$, and effective Reynolds numbers within $\mathrm{Re'} < 15$. The fraction of the polarized vortex length varied between zero in the middle of the channel and $\sim$ 60% within the shear flow regions $\sim D/4$ from the walls. Therefore, we studied a state of polarized ultraquantum (Vinen) turbulence fuelled at short lengthscales by vortex reconnections, including those with vortex images due to the relative motion between the vortex tangle and the pinning rough surface., Comment: 12 pages, 8 figures. Contribution to Journal of Low Temperature Physics QFS2023 Special Edition

Giant Star-forming Clumps (GSFCs) are areas of intensive star-formation that are commonly observed in high-redshift (z>1) galaxies but their formation and role in galaxy evolution remain unclear. High-resolution observations of low-redshift clumpy galaxy analogues are rare and restricted to a limited set of galaxies but the increasing availability of wide-field galaxy survey data makes the detection of large clumpy galaxy samples increasingly feasible. Deep Learning, and in particular CNNs, have been successfully applied to image classification tasks in astrophysical data analysis. However, one application of DL that remains relatively unexplored is that of automatically identifying and localising specific objects or features in astrophysical imaging data. In this paper we demonstrate the feasibility of using Deep learning-based object detection models to localise GSFCs in astrophysical imaging data. We apply the Faster R-CNN object detection framework (FRCNN) to identify GSFCs in low redshift (z<0.3) galaxies. Unlike other studies, we train different FRCNN models not on simulated images with known labels but on real observational data that was collected by the Sloan Digital Sky Survey Legacy Survey and labelled by volunteers from the citizen science project `Galaxy Zoo: Clump Scout'. The FRCNN model relies on a CNN component as a `backbone' feature extractor. We show that CNNs, that have been pre-trained for image classification using astrophysical images, outperform those that have been pre-trained on terrestrial images. In particular, we compare a domain-specific CNN -`Zoobot' - with a generic classification backbone and find that Zoobot achieves higher detection performance and also requires smaller training data sets to do so. Our final model is capable of producing GSFC detections with a completeness and purity of >=0.8 while only being trained on ~5,000 galaxy images., Comment: Accepted for publication in RASTI, 22 pages

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 2023

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 2023

Recently, new topological properties that an operator acting on a topological vector space can have were introduced: strong hypercyclicity and hypermixing. We introduce a new property called ultra hypercyclicity, and we compare it to the other common topological properties, including the new ones. For the family of weighted backward shifts on $c_0$ and $\ell^p$, we prove the existence of a strongly hypercyclic weighted backward shift which is not ultra hypercyclic., Comment: arXiv admin note: text overlap with arXiv:2304.02073

Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of transients to neural network emulators of cosmological simulations, and is shifting paradigms about how we generate and report scientific results. At the same time, this class of method comes with its own set of best practices, challenges, and drawbacks, which, at present, are often reported on incompletely in the astrophysical literature. With this paper, we aim to provide a primer to the astronomical community, including authors, reviewers, and editors, on how to implement machine learning models and report their results in a way that ensures the accuracy of the results, reproducibility of the findings, and usefulness of the method., Comment: 14 pages, 3 figures; submitted to the Bulletin of the American Astronomical Society

We take a deep learning-based approach for galaxy merger identification in Subaru HSC-SSP, specifically through the use of deep representation learning and fine-tuning, with the aim of creating a pure and complete merger sample within the HSC-SSP survey. We can use this merger sample to conduct studies on how mergers affect galaxy evolution. We use Zoobot, a deep learning representation learning model pre-trained on citizen science votes on Galaxy Zoo DeCALS images. We fine-tune Zoobot for the purpose of merger classification of images of SDSS and GAMA galaxies in HSC-SSP PDR 3. Fine-tuning is done using 1200 synthetic HSC-SSP images of galaxies from the TNG simulation. We then find merger probabilities on observed HSC images using the fine-tuned model. Using our merger probabilities, we examine the relationship between merger activity and environment. We find that our fine-tuned model returns an accuracy on the synthetic validation data of 76%. This number is comparable to those of previous studies where convolutional neural networks were trained with simulation images, but with our work requiring a far smaller number of training samples. For our synthetic data, our model is able to achieve completeness and precision values of 80%. In addition, our model is able to correctly classify both mergers and non-mergers of diverse morphologies and structures, including those at various stages and mass ratios, while distinguishing between projections and merger pairs. For the relation between galaxy mergers and environment, we find two distinct trends. Using stellar mass overdensity estimates for TNG simulations and observations using SDSS and GAMA, we find that galaxies with higher merger scores favor lower density environments on scales of 0.5 to 8 h^-1 Mpc. However, below these scales in the simulations, we find that galaxies with higher merger scores favor higher density environments., Comment: 36 pages, 15 figures, accepted to Astronomy and Astrophysics

We present detailed morphology measurements for 8.67 million galaxies in the DESI Legacy Imaging Surveys (DECaLS, MzLS, and BASS, plus DES). These are automated measurements made by deep learning models trained on Galaxy Zoo volunteer votes. Our models typically predict the fraction of volunteers selecting each answer to within 5-10\% for every answer to every GZ question. The models are trained on newly-collected votes for DESI-LS DR8 images as well as historical votes from GZ DECaLS. We also release the newly-collected votes. Extending our morphology measurements outside of the previously-released DECaLS/SDSS intersection increases our sky coverage by a factor of 4 (5,000 to 19,000 deg$^2$) and allows for full overlap with complementary surveys including ALFALFA and MaNGA., Comment: 20 pages. Accepted at MNRAS. Catalog available via https://zenodo.org/record/7786416. Pretrained models available via https://github.com/mwalmsley/zoobot. Vizier and Astro Data Lab access not yet available. With thanks to the Galaxy Zoo volunteers

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 eight 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 sources

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.

We present a novel natural language processing (NLP) approach to deriving plain English descriptors for science cases otherwise restricted by obfuscating technical terminology. We address the limitations of common radio galaxy morphology classifications by applying this approach. We experimentally derive a set of semantic tags for the Radio Galaxy Zoo EMU (Evolutionary Map of the Universe) project and the wider astronomical community. We collect 8,486 plain English annotations of radio galaxy morphology, from which we derive a taxonomy of tags. The tags are plain English. The result is an extensible framework which is more flexible, more easily communicated, and more sensitive to rare feature combinations which are indescribable using the current framework of radio astronomy classifications., Comment: 17 pages, 11 Figures, Accepted at MNRAS

Recently, two stronger versions of dynamical properties have been introduced and investigated: strong topological transitivity, which is a stronger version of the topological transitivity property, and hypermixing, which is a stronger version of the mixing property. We continue the investigation of these notions with two main results. First, we show there are dynamical systems which are strongly topologically transitive but not weakly mixing. We then show that on $\ell^p$ or $c_0$, there is a weighted backward shift which is strongly topologically transitive but not mixing.

Understanding phase competition and phase separation in quantum materials requires access to the spatiotemporal dynamics of electronic ordering phenomena on a micro- to nanometer length- and femtosecond timescale. While time- and angle-resolved photoemission (trARPES) experiments provide sensitivity to the femtosecond dynamics of electronic ordering, they typically lack the required spatial resolution. Here, we demonstrate ultrafast dark-field photoemission microscopy (PEEM) using a momentum microscope, providing access to ultrafast electronic order on the microscale. We investigate the prototypical charge-density wave (CDW) compound TbTe3 in the vicinity of a buried crystal defect, demonstrating real- and reciprocal-space configurations combined with a pump-probe approach. We find CDW order to be suppressed in the region covered by the crystal defect, most likely due to locally imposed strain. Comparing the ultrafast dynamics in different areas of the sample reveals a substantially smaller response to optical excitation and faster relaxation of excited carriers in the defect area, which we attribute to enhanced particle-hole scattering and defect-induced relaxation channels., Comment: 8 pages, 4 figures