Quantifying the poor purity and completeness of morphological samples selected by galaxy colour

Smethurst, Rebecca; Masters, Karen L.; Simmons, Brooke; Garland, Izzy L.; Géron, Tobias; Häußler, Boris; Kruk, Sandor; Lintott, Chris; O’Ryan, David; Walmsley, Mike

doi:10.1093/mnras/stab3607

Cited by 9 publications

(3 citation statements)

References 89 publications

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“…The Hubble classification correlates with galaxy color, with lenticulars and ellipticals being redder, whereas spirals are increasingly blue toward the later types. Galaxies are bimodal in both morphology and color, but care should be taken with equating red with early-type and blue with spiral, as interesting subpopulations of red spirals and blue early types do exist (e.g., Smethurst et al 2022).…”

Section: Modern Demographics Of Spiral Arms In Galaxiesmentioning

confidence: 99%

Spirals in Galaxies

Sellwood

Masters

2022

Annu. Rev. Astron. Astrophys.

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Spirals in galaxies have long been thought to be caused by gravitational instability in the stellar component of the disk, but discerning the precise mechanism had proved elusive. Tidal interactions, and perhaps bars, may provoke some spiral responses, but spirals in many galaxies must be self-excited. We survey the relevant observational data and aspects of disk dynamical theory. The origin of the recurring spiral patterns in simulations of isolated disk galaxies has recently become clear, and it is likely that the mechanism is the same in real galaxies, although evidence to confirm this supposition is hard to obtain. As transient spiral activity increases random motion, the patterns must fade over time unless the disk also contains a dissipative gas component. Continuing spiral activity alters the structure of the disks in other ways: reducing metallicity gradients and flattening rotation curves are two of the most significant. The overwhelming majority of spirals in galaxies have two- or three-fold rotational symmetry, indicating that the cool, thin disk component is massive. Spirals in simulations of halo-dominated disks instead manifest many arms and, consequently, do not capture the expected full spiral-driven evolution. We conclude by identifying areas where further work is needed. Expected final online publication date for the Annual Review of Astronomy Volume 60 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Section: Modern Demographics Of Spiral Arms In Galaxiesmentioning

confidence: 99%

Spirals in Galaxies

Sellwood

Masters

2022

Annu. Rev. Astron. Astrophys.

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“…Among these misclassified types are the bluer HIrich galaxies of early type and the redder HI-poor spiral galaxies; edge-on and galaxies seen face-on, especially with a pronounced bulge; the bulge-less (ultra-flat) galaxies with inclination 87°...90° for seen edge-on and 10°...0° for seen face-on. The face-on bulge-less galaxies can be considered counterparts to the edge-on disk galaxies giving additional information on their physical parameters, including photometry [54,123]. So, their correct classification is very useful when compiling catalogs with a bulge to superthin galaxies [124,125] or studying the influence of the environment on the morphology and quenching of galaxies in dense environments (for example, [126] for the Hydra cluster).…”

Section: Notes On Problem Points Of Cnn Image-based Galaxy Classifica...mentioning

confidence: 99%

Machine learning technique for morphological classification of galaxies from the SDSS. III. The CNN image-based inference of detailed features

Khramtsov¹,

Vavilova²,

Dobrycheva³

et al. 2022

Kosm. nauka tehnol.

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This paper follows a series of our works on the applicability of various machine learning methods to morphological galaxy classification (Vavilova et al., 2021, 2022). We exploited the sample of ~315800 low-redshift SDSS DR9 galaxies with absolute stellar magnitudes of −24m < Mr < −19.4m at 0.003 < z < 0.1 redshifts as a target data set for the CNN classifier. Because it is tightly overlapped with the Galaxy Zoo 2 (GZ2) sample, we use these annotated data as the training data set to classify galaxies into 34 detailed features. In the presence of a pronounced difference in visual parameters between galaxies from the GZ2 training data set and galaxies without known morphological parameters, we applied novel procedures, which allowed us for the first time to get rid of this difference for smaller and fainter SDSS galaxies with mr < 17.7. We describe in detail the adversarial validation technique as well as how we managed the optimal train-test split of galaxies from the training data set to verify our CNN model based on the DenseNet-201 realistically. We have also found optimal galaxy image transformations, which help increase the classifier’s generalization ability. We demonstrate for the first time that implication of the CNN model with a train-test split of data sets and size-changing function simulating a decrease in magnitude and size (data augmentation) significantly improves the classification of smaller and fainter SDSS galaxies. It can be considered as another way to improve the human bias for those galaxy images that had a poor vote classification in the GZ project. Such an approach, like autoimmunization, when the CNN classifier, trained on very good galaxy images, is able to retrain bad images from the same homogeneous sample, can be considered co-planar to other methods of combating such a human bias. The most promising result is related to the CNN prediction probability in the classification of detailed features. The accuracy of the CNN classifier is in the range of 83.3—99.4 % depending on 32 features (exception is for “disturbed” (68.55 %) and “arms winding medium” (77.39 %) features). As a result, for the first time, we assigned the detailed morphological classification for more than 140000 low-redshift galaxies, especially at the fainter end. A visual inspection of the samples of galaxies with certain morphological features allowed us to reveal typical problem points of galaxy image classification by shape and features from the astronomical point of view. The morphological catalogs of low-redshift SDSS galaxies with the most interesting features are available through the UkrVO website (http://ukr-vo.org/galaxies/) and VizieR.

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“…Limitations also inevitably arise from the data themselves. For example, there is often a trade off between purity and completeness (Smethurst et al 2021); simulated data may not be representative of reality; and the quality of the data could be influenced by observing conditions, signal-to-noise ratio (SNR), etc. It is becoming more crucial to understand these effects with increasingly big data from large sky surveys, such as Vera Rubin Observatory Legacy Survey of Space and Time (LSST), where the utility of the data for a particular investigation are also influenced by the chosen filter and cadence strategies.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Recurrent Neural Networks for Modelling Astronomical Time Series: Advantages and Limitations

Sheng¹,

Nicholl²,

Ross³

2023

Preprint

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This paper reviews the Stochastic Recurrent Neural Network (SRNN) as applied to the light curves of Active Galactic Nuclei by Sheng et al. (2022). Astronomical data have inherent limitations arising from telescope capabilities, cadence strategies, inevitable observing weather conditions, and current understanding of celestial objects. When applying machine learning methods, it is vital to understand the effects of data limitations on our analysis and ability to make inferences. We take Sheng et al. ( 2022) as a case study, and illustrate the problems and limitations encountered in implementing the SRNN for simulating AGN variability as seen by the Rubin Observatory.

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Quantifying the poor purity and completeness of morphological samples selected by galaxy colour

Cited by 9 publications

References 89 publications

Spirals in Galaxies

Spirals in Galaxies

Machine learning technique for morphological classification of galaxies from the SDSS. III. The CNN image-based inference of detailed features

Stochastic Recurrent Neural Networks for Modelling Astronomical Time Series: Advantages and Limitations

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