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Abstract We study the evolution of the bar fraction in disk galaxies between 0.5 < z < 4.0 using multiband colored images from JWST Cosmic Evolution Early Release Science Survey (CEERS). These images were classified by citizen scientists in a new phase of the Galaxy Zoo (GZ) project called GZ CEERS. Citizen scientists were asked whether a strong or weak bar was visible in the host galaxy. After considering multiple corrections for observational biases, we find that the bar fraction decreases with redshift in our volume-limited sample (n= 398); from $25_{-4}^{+6}$% at 0.5 <z< 1.0 to $3_{-1}^{+6}$% at 3.0 < z < 4.0. However, we argue it is appropriate to interpret these fractions as lower limits. Disentangling real changes in the bar fraction from detection biases remains challenging. Nevertheless, we find a significant number of bars up toz= 2.5. This implies that disks are dynamically cool or baryon dominated, enabling them to host bars. This also suggests that bar-driven secular evolution likely plays an important role at higher redshifts. When we distinguish between strong and weak bars, we find that the weak bar fraction decreases with increasing redshift. In contrast, the strong bar fraction is constant between 0.5 <z< 2.5. This implies that the strong bars found in this work are robust long-lived structures, unless the rate of bar destruction is similar to the rate of bar formation. Finally, our results are consistent with disk instabilities being the dominant mode of bar formation at lower redshifts, while bar formation through interactions and mergers is more common at higher redshifts. 

ABSTRACT We have not yet observed the epoch at which disc galaxies emerge in the Universe. While high-z measurements of large-scale features such as bars and spiral arms trace the evolution of disc galaxies, such methods cannot directly quantify featureless discs in the early Universe. Here, we identify a substantial population of apparently featureless disc galaxies in the Cosmic Evolution Early Release Science (CEERS) survey by combining quantitative visual morphologies of $${\sim} 7000$$ galaxies from the Galaxy Zoo JWST CEERS project with a public catalogue of expert visual and parametric morphologies. While the highest redshift featured disc we identify is at $$z_{\rm {phot}}=5.5$$, the highest redshift featureless disc we identify is at $$z_{\rm {phot}}=7.4$$. The distribution of Sérsic indices for these featureless systems suggests that they truly are dynamically cold: disc-dominated systems have existed since at least $$z\sim 7.4$$. We place upper limits on the featureless disc fraction as a function of redshift, and show that up to 75 per cent of discs are featureless at $3.0< z< 7.4$. This is a conservative limit assuming all galaxies in the sample truly lack features. With further consideration of redshift effects and observational constraints, we find the featureless disc fraction in CEERS imaging at these redshifts is more likely $${\sim} 29{\!-\!}38~{{\ \rm per\ cent}}$$. We hypothesize that the apparent lack of features in a third of high-redshift discs is due to a higher gas fraction in the early Universe, which allows the discs to be resistant to buckling and instabilities. 

Abstract 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. Observations of low-redshift clumpy galaxy analogues are rare but the availability of wide-field galaxy survey data makes the detection of large clumpy galaxy samples much more feasible. Deep Learning (DL), and in particular Convolutional Neural Networks (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 localizing specific objects or features in astrophysical imaging data. In this paper, we demonstrate the use of DL-based object detection models to localize GSFCs in astrophysical imaging data. We apply the Faster Region-based Convolutional Neural Network object detection framework (FRCNN) to identify GSFCs in low-redshift (z ≲ 0.3) galaxies. Unlike other studies, we train different FRCNN models on observational data that was collected by the Sloan Digital Sky 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. Our final model is capable of producing GSFC detections with a completeness and purity of ≥0.8 while only being trained on ∼5000 galaxy images. 

ABSTRACT 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 per cent 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 (5000–19 000 deg2) and allows for full overlap with complementary surveys including ALFALFA and MaNGA. 

ABSTRACT Galaxy Zoo: Clump Scout  is a web-based citizen science project designed to identify and spatially locate giant star forming clumps in galaxies that were imaged by the Sloan Digital Sky Survey Legacy Survey. We present a statistically driven software framework that is designed to aggregate two-dimensional annotations of clump locations provided by multiple independent Galaxy Zoo: Clump Scout volunteers and generate a consensus label that identifies the locations of probable clumps within each galaxy. The statistical model our framework is based on allows us to assign false-positive probabilities to each of the clumps we identify, to estimate the skill levels of each of the volunteers who contribute to Galaxy Zoo: Clump Scout and also to quantitatively assess the reliability of the consensus labels that are derived for each subject. We apply our framework to a data set containing 3561 454 two-dimensional points, which constitute 1739 259 annotations of 85 286 distinct subjects provided by 20 999 volunteers. Using this data set, we identify 128 100 potential clumps distributed among 44 126 galaxies. This data set can be used to study the prevalence and demographics of giant star forming clumps in low-redshift galaxies. The code for our aggregation software framework is publicly available at: https://github.com/ou-astrophysics/BoxAggregator 

In the era of rapidly growing astronomical data, the gap between data collection and analysis is a significant barrier, especially for teams searching for rare scientific objects. Although machine learning (ML) can quickly parse large data sets, it struggles to robustly identify scientifically interesting objects, a task at which humans excel. Human-in-the-loop (HITL) strategies that combine the strengths of citizen science (CS) and ML offer a promising solution, but first, we need to better understand the relationship between human- and machine-identified samples. In this work, we present a case study from the Galaxy Zoo: Weird & Wonderful project, where volunteers inspected ~200,000 astronomical images—processed by an ML-based anomaly detection model—to identify those with unusual or interesting characteristics. Volunteer-selected images with common astrophysical characteristics had higher consensus, while rarer or more complex ones had lower consensus. This suggests low-consensus choices shouldn’t be dismissed in further explorations. Additionally, volunteers were better at filtering out uninteresting anomalies, such as image artifacts, which the machine struggled with. We also found that a higher ML-generated anomaly score that indicates images’ low-level feature anomalousness was a better predictor of the volunteers’ consensus choice. Combining a locus of high volunteer-consensus images within the ML learnt feature space and anomaly score, we demonstrated a decision boundary that can effectively isolate images with unusual and potentially scientifically interesting characteristics. Using this case study, we lay important guidelines for future research studies looking to adapt and operationalize human-machine collaborative frameworks for efficient anomaly detection in big data. 

Artificial Intelligence (AI) and citizen science (CS) are two approaches to tackling data challenges related to scale and complexity. CS by its very definition relies on the joint effort of typically a distributed group of non-expert people to solve problems in a manner that relies on human intelligence. As AI capabilities increasingly augment or complement human intelligence, if not replicate it, there is a growing effort to understand the role that AI can play in CS and vice versa. With this growing interest as context, this special collection, The Future of AI and Citizen Science, illustrates the many ways that CS practitioners are integrating AI into their efforts, as well as identifies current limitations. In this spirit, our editorial briefly introduces the special collection papers to demonstrate and assess some uses of AI in CS; then, we contextualize these uses in terms of key challenges; and conclude with future directions that use AI with CS in both innovative and ethical ways. 

Citizen science has become a valuable and reliable method for interpreting and processing big datasets, and is vital in the era of ever-growing data volumes. However, there are inherent difficulties in the generating labels from citizen scientists, due to the inherent variability between the members of the crowd, leading to variability in the results. Sometimes, this is useful — such as with serendipitous discoveries, which corresponds to rare/unknown classes in the data — but it might also be due to ambiguity between classes. The primary issue is then to distinguish between the intrinsic variability in the dataset and the uncertainty in the citizen scientists’ responses, and leveraging that to extract scientifically useful relationships. In this paper, we explore using a neural network to interpret volunteer confusion across the dataset, to increase the purity of the downstream analysis. We focus on the use of learned features from the network to disentangle feature similarity across the classes, and the ability of the machines’ “attention” in identifying features that lead to confusion. We use data from Jovian Vortex Hunter, a citizen science project to study vortices in Jupiter’s atmosphere, and find that the latent space from the model helps effectively identify different sources of image-level features that lead to low volunteer consensus. Furthermore, the machine’s attention highlights features corresponding to specific classes. This provides meaningful image-level feature-class relationships, which is useful in our analysis for identifying vortex-specific features to better understand vortex evolution mechanisms. Finally, we discuss the applicability of this method to other citizen science projects. 

Using public support to extract information from vast datasets has become a popular method for accurately labeling wildlife data in camera trap (CT) images. However, the increasing demand for volunteer effort lengthens the time interval between data collection and our ability to draw ecological inferences or perform data-driven conservation actions. Artificial intelligence (AI) approaches are currently highly effective for species detection (i.e., whether an image contains animals or not) and labeling common species; however, it performs poorly on species rarely captured in images and those that are highly visually similar to one another. To capitalize on the best of human and AI classifying methods, we developed an integrated CT data pipeline in which AI provides an initial pass on labeling images, but is supervised and validated by humans (i.e., a “human-in-the-loop” approach). To assess classification accuracy gains, we compare the precision of species labels produced by AI and HITL protocols to a “gold standard” (GS) dataset annotated by wildlife experts. The accuracy of the AI method was species-dependent and positively correlated with the number of training images. The combined efforts of HITL led to error rates of less than 10% for 73% of the dataset and lowered the error rates for an additional 23%. For two visually similar species, human input resulted in higher error rates than AI. While integrating humans in the loop increases classification times relative to AI alone, the gains in accuracy suggest that this method is highly valuable for high-volume CT surveys. 

Abstract The UltraViolet imaging of the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey Fields (UVCANDELS) program provides Hubble Space Telescope (HST)/UVIS F275W imaging for four CANDELS fields. We combine this UV imaging with existing HST/near-IR grism spectroscopy from 3D-HST+AGHAST to directly compare the resolved rest-frame UV and H α emission for a sample of 979 galaxies at 0.7 < z < 1.5, spanning a range in stellar mass of 10 8−11.5 M ⊙ . Using a stacking analysis, we perform a resolved comparison between homogenized maps of rest-UV and H α to compute the average UV-to-H α luminosity ratio (an indicator of burstiness in star formation) as a function of galactocentric radius. We find that galaxies below stellar mass of ∼10 9.5 M ⊙ , at all radii, have a UV-to-H α ratio higher than the equilibrium value expected from constant star formation, indicating a significant contribution from bursty star formation. Even for galaxies with stellar mass ≳10 9.5 M ⊙ , the UV-to-H α ratio is elevated toward their outskirts ( R / R eff > 1.5), suggesting that bursty star formation is likely prevalent in the outskirts of even the most massive galaxies, but is likely overshadowed by their brighter cores. Furthermore, we present the UV-to-H α ratio as a function of galaxy surface brightness, a proxy for stellar mass surface density, and find that regions below ∼10 7.5 M ⊙ kpc −2 are consistent with bursty star formation, regardless of their galaxy stellar mass, potentially suggesting that local star formation is independent of global galaxy properties at the smallest scales. Last, we find galaxies at z > 1.1 to have bursty star formation, regardless of radius or surface brightness.