40 research outputs found
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
From fat droplets to floating forests: cross-domain transfer learning using a PatchGAN-based segmentation model
Many scientific domains gather sufficient labels to train machine algorithms
through human-in-the-loop techniques provided by the Zooniverse.org citizen
science platform. As the range of projects, task types and data rates increase,
acceleration of model training is of paramount concern to focus volunteer
effort where most needed. The application of Transfer Learning (TL) between
Zooniverse projects holds promise as a solution. However, understanding the
effectiveness of TL approaches that pretrain on large-scale generic image sets
vs. images with similar characteristics possibly from similar tasks is an open
challenge. We apply a generative segmentation model on two Zooniverse
project-based data sets: (1) to identify fat droplets in liver cells
(FatChecker; FC) and (2) the identification of kelp beds in satellite images
(Floating Forests; FF) through transfer learning from the first project. We
compare and contrast its performance with a TL model based on the COCO image
set, and subsequently with baseline counterparts. We find that both the FC and
COCO TL models perform better than the baseline cases when using >75% of the
original training sample size. The COCO-based TL model generally performs
better than the FC-based one, likely due to its generalized features. Our
investigations provide important insights into usage of TL approaches on
multi-domain data hosted across different Zooniverse projects, enabling future
projects to accelerate task completion.Comment: 5 pages, 4 figures, accepted for publication at the Proceedings of
the ACM/CIKM 2022 (Human-in-the-loop Data Curation Workshop
Distinguishing Mergers and Disks in High Redshift Observations of Galaxy Kinematics
The majority of massive star-forming galaxies at have velocity
gradients suggestive of rotation, in addition to large amounts of disordered
motions. In this paper, we demonstrate that it is challenging to distinguish
the regular rotation of a disk galaxy from the orbital motions of merging
galaxies with seeing-limited data. However, the merger fractions at
are likely too low for this to have a large effect on measurements of disk
fractions. To determine how often mergers pass for disks, we look to galaxy
formation simulations. We analyze 24000 synthetic images and kinematic
maps of 31 high-resolution simulations of isolated galaxies and mergers at
. We determine if the synthetic observations pass criteria commonly
used to identify disk galaxies, and whether the results are consistent with
their intrinsic dynamical states. Galaxies that are intrinsically mergers pass
the disk criteria for anywhere from 0 to 100 of sightlines. The exact
percentage depends strongly on the specific disk criteria adopted, and weakly
on the separation of the merging galaxies. Therefore, one cannot tell with
certainty whether observations of an individual galaxy indicate a merger or a
disk. To estimate the fraction of mergers passing as disks in current
kinematics samples, we combine the probability that a merger will pass as a
disk with theoretical merger fractions from a cosmological simulation. Taking
the latter at face-value, the observed disk fractions are overestimated by
small amounts: at most by at high stellar mass ( M)
and at low stellar mass ( M).Comment: 15 pages, 10 figures, accepted for publication in Ap
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Transfer learning for galaxy feature detection: Finding giant star-forming clumps in low-redshift galaxies using Faster Region-based Convolutional Neural Network
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
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Galaxy Zoo: Clump Scout - Design and first application of a two-dimensional aggregation tool for citizen science
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 dataset containing 3,561,454 two-dimensional points, which constitute 1,739,259 annotations of 85,286 distinct subjects provided by 20,999 volunteers. Using this dataset, we identify 128,100 potential clumps distributed among 44,126 galaxies. This dataset 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/BoxAggregato
Galaxy Zoo DESI: Detailed Morphology Measurements for 8.7M Galaxies in the DESI Legacy Imaging Surveys
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) 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 volunteer
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). This challenges the longstanding view that deep supervised methods require new large labelled datasets for practical use in astronomy. To help the community benefit from our pretrained models, we release our fine-tuning code zoobot. Zoobot is accessible to researchers with no prior experience in deep learning
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Galaxy Zoo DESI: Detailed morphology measurements for 8.7M galaxies in the DESI Legacy Imaging Surveys
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 (5000 to 19 000 deg2) and allows for full overlap with complementary surveys including ALFALFA and MaNGA
Galaxy Zoo DESI : Detailed morphology measurements for 8.7M galaxies in the DESI Legacy Imaging Surveys
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 (5000 to 19 000 deg2) and allows for full overlap with complementary surveys including ALFALFA and MaNGA