A Catalog of Automatically Detected Ring Galaxy Candidates in PanSTARSS

Timmis, Ian; Shamir, Lior

doi:10.3847/1538-4365/aa78a3

Cited by 22 publications

(21 citation statements)

References 27 publications

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“…Efforts at automatic identification are sparse. Our literature search revealed only two papers (Timmis & Shamir 2017;Shamir 2020) automatically identifying 185 and 443 ring candidates in PanSTARRS and SDSS, respectively. The largest ring catalogue, Buta 2017, was created using crowdsourcing.…”

Section: Methodsmentioning

confidence: 99%

Practical galaxy morphology tools from deep supervised representation learning

Walmsley

Scaife

Lintott

et al. 2022

Monthly Notices of the Royal Astronomical Society

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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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Section: Methodsmentioning

confidence: 99%

Practical galaxy morphology tools from deep supervised representation learning

Walmsley

Scaife

Lintott

et al. 2022

Monthly Notices of the Royal Astronomical Society

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“…Pan-STARRS data includes 33,028 galaxies from Pan-STARRS DR1 (Shamir, 2020c). The initial set included 2,394,452 Pan-STARRS objects identified as extended sources by all color bands, and g magnitude brighter than 19 (Timmis and Shamir, 2017). These galaxies were annotated automatically as described in Section 2.6.…”

Section: Pan-starrs Datamentioning

confidence: 99%

New evidence and analysis of cosmological-scale asymmetry in galaxy spin directions

Shamir

2022

Preprint

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In the past several decades, multiple cosmological theories that are based on the contention that the Universe has a major axis have been proposed. Such theories can be based on the geometry of the Universe, or multiverse theories such as black hole cosmology. The contention of a cosmological-scale axis is supported by certain evidence such as the dipole axis formed by the CMB distribution. Here I study another form of cosmological-scale axis, based on the distribution of the spin direction of spiral galaxies. Data from four different telescopes is analyzed, showing nearly identical axis profiles when the distribution of the redshifts of the galaxies is similar.

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“…The use of machine learning provided more effective methods for the purpose of galaxy image classification (Shamir 2009;Huertas-Company et al 2009;Banerji et al 2010;Shamir et al 2013;Schutter and Shamir 2015;Kuminski et al 2014;Dieleman et al 2015;Hocking et al 2017;Kuminski and Shamir 2018;Silva et al 2018), and the use of such methods also provided computer-generated catalogs of galaxy morphology (Huertas-Company et al 2010;Simard et al 2011;Shamir and Wallin 2014;Kuminski and Shamir 2016;Huertas-Company et al 2015a,b;Timmis and Shamir 2017;Paul et al 2018;Shamir 2019). Automatic annotation methods were also tested on Pan-STARRS data by using the photometric measurements of colors and moments, classified by a Random Forest classifier to achieve a considerable accuracy of ∼ 89% (Baldeschi et al 2020).…”

Section: Lshamir@mtuedumentioning

confidence: 99%

“…To remove stars, objects that their PSF i magnitude subtracted by their Kron i magnitude was greater than 0.05 were also removed. That led to a dataset of 2,394,452 objects (Timmis and Shamir 2017). Objects that were flagged by Pan-STARRS photometric pipeline as artifacts, had a brighter neighbor, defect, double PSF, or a blend in any of the bands were excluded from the dataset.…”

Section: Datamentioning

confidence: 99%

A catalog of broad morphology of Pan-STARRS galaxies based on deep learning

Goddard,

Shamir

2020

Preprint

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Autonomous digital sky surveys such as Pan-STARRS have the ability to image a very large number of galactic and extra-galactic objects, and the large and complex nature of the image data reinforces the use of automation. Here we describe the design and implementation of a data analysis process for automatic broad morphology annotation of galaxies, and applied it to the data of Pan-STARRS DR1. The process is based on filters followed by a two-step convolutional neural network (CNN) classification. Training samples are generated by using an augmented and balanced set of manually classified galaxies. Results are evaluated for accuracy by comparison to the annotation of Pan-STARRS included in a previous broad morphology catalog of SDSS galaxies. Our analysis shows that a CNN combined with several filters is an effective approach for annotating the galaxies and removing unclean images. The catalog contains morphology labels for 1,662,190 galaxies with ∼95% accuracy. The accuracy can be further improved by selecting labels above certain confidence thresholds. The catalog is publicly available.

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A Catalog of Automatically Detected Ring Galaxy Candidates in PanSTARSS

Cited by 22 publications

References 27 publications

Practical galaxy morphology tools from deep supervised representation learning

Practical galaxy morphology tools from deep supervised representation learning

New evidence and analysis of cosmological-scale asymmetry in galaxy spin directions

A catalog of broad morphology of Pan-STARRS galaxies based on deep learning

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