What is Important? Morphological Asymmetries are Useful Predictors of Star Formation Rates of Star-forming Galaxies in SDSS Stripe 82

Yesuf, Hassen M.; Ho, Luis C.; Faber, S. M.

doi:10.48550/arxiv.2109.08882

Cited by 3 publications

(3 citation statements)

References 172 publications

(275 reference statements)

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“…Although not the main focus of this study, we briefly mention, for completeness, the relation between morphological asymmetry and star formation, as it pertains to the distribution of SFGs on the galaxy main sequence (e.g., Daddi et al 2007;Elbaz et al 2007;Noeske et al 2007). It is evident from Figure 12 that the average level of asymmetry (A outer ) increases mildly but systematically above the main sequence, echoing the recent findings of Yesuf et al (2021). Consistent with previous work (e.g., Cibinel et al 2019), major merger candidates (Section 4.2) stand out the most, but it is notable that a significant fraction of major merger candidates also lie on and below the main sequence (Figure 12b).…”

Section: Relation With Star Formationsupporting

confidence: 84%

The Relation between Morphological Asymmetry and Nuclear Activity in Low-redshift Galaxies

Zhao,

Li,

Shangguan

et al. 2021

Preprint

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The morphology of galaxies reflects their assembly history and ongoing dynamical perturbations from the environment. Analyzing stacked i-band images from the Pan-STARRS1 3π Steradian Survey, we study the optical morphological asymmetry of the host galaxies of a large, well-defined sample of nearby active galactic nuclei (AGNs) to investigate the role of mergers and interactions in triggering nuclear activity. The AGNs, comprising 245 type 1 and 4514 type 2 objects, are compared with 4537 star-forming galaxies matched in redshift (0.04 < z < 0.15) and stellar mass (M * > 10 10 M ). We develop a comprehensive masking strategy to isolate the emission of the target from foreground stars and other contaminating nearby sources, all the while retaining projected companions of comparable brightness that may be major mergers. Among three variants of nonparametric indices, both the popular CAS asymmetry parameter (A CAS ) and the outer asymmetry parameter (A outer ) yield robust measures of morphological distortion for star-forming galaxies and type 2 AGNs, while only A outer is effective for type 1 AGNs. The shape asymmetry (A shape ), by comparison, is affected more adversely by background noise. Asymmetry indices 0.4 effectively trace systems that are candidate ongoing mergers. Contrary to theoretical expectations, galaxy interactions and mergers are not the main drivers of nuclear activity, at least not in our sample of low-redshift, relatively low-luminosity AGNs, whose host galaxies are actually significantly less asymmetric than the control sample of star-forming galaxies. Moreover, type 2 AGNs are morphologically indistinguishable from their type 1 counterparts. The level of AGN activity does not correlate with asymmetry, not even among the major merger candidates. As a by-product, we find, consistent with previous studies, that the average asymmetry of star-forming galaxies increases above the main sequence, although not all major mergers exhibit enhanced star formation.

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Section: Relation With Star Formationsupporting

confidence: 84%

The Relation between Morphological Asymmetry and Nuclear Activity in Low-redshift Galaxies

Zhao,

Li,

Shangguan

et al. 2021

Preprint

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“…Thus, the only variable for this test is the amount of resolved morphology. This makes us conclude that galaxy morphology carries much information about galaxy properties, not only in a global sense for total stellar mass but also as a resolved map-like feature (see also Yesuf et al 2021, for the importance of morphology in predicting galaxy SFR).…”

Section: Evaluating the ML Prediction As A Function Of Image Sizementioning

confidence: 64%

Predicting resolved galaxy properties from photometric images using convolutional neural networks

Buck¹,

Wolf²

2021

Preprint

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Multi-band images of galaxies reveal a huge amount of information about their morphology and structure. However, inferring properties of the underlying stellar populations such as age, metallicity or kinematics from those images is notoriously difficult. Traditionally such information is best extracted from expensive spectroscopic observations. Here we present the Painting IntrinsiC Attributes onto SDSS Objects (PICASSSO) project and test the information content of photometric multi-band images of galaxies. We train a convolutional neural network on 27,558 galaxy image pairs to establish a connection between broad-band images and the underlying physical stellar and gaseous galaxy property maps. We test our machine learning (ML) algorithm with SDSS ugriz mock images for which uncertainties and systematics are exactly known. We show that multi-band galaxy images contain enough information to reconstruct 2d maps of stellar mass, metallicity, age and gas mass, metallicity as well as star formation rate. We recover the true stellar properties on a pixel by pixel basis with only little scatter, ∼ < 20% compared to ∼ 50% statistical uncertainty from traditional mass-to-light-ratio based methods. We further test for any systematics of our algorithm with image resolution, training sample size or wavelength coverage. We find that galaxy morphology alone constrains stellar properties to better than ∼ 20% thus highlighting the benefits of including morphology into the parameter estimation. The machine learning approach can predict maps of high resolution, only limited by the resolution of the input bands, thus achieve higher resolution than IFU observations. The network architecture and all code is publicly available on GitHub .

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“…Meanwhile, the control sample predominantly exhibits more "normal" morphologies, but also includes irregular and asymmetric morphologies which may be driven by environment, secular evolution, or more minor interactions with 𝜇 < 0.1 (e.g. Richter & Sancisi 1994;Rix & Zaritsky 1995;Swaters et al 1999;Jog & Combes 2009;Zaritsky et al 2013;Martin et al 2018;Yesuf et al matching control was extracted. The mean growth factor was 𝑁 grow = 1.77.…”

Section: Post-merger and Control Selectionmentioning

confidence: 99%

The combined and respective roles of imaging and stellar kinematics in identifying galaxy merger remnants

Bottrell,

Hani,

Teimoorinia

et al. 2022

Preprint

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One of the central challenges to establishing the role of mergers in galaxy evolution is the selection of pure and complete merger samples in observations. In particular, while large and reasonably pure interacting galaxy pair samples can be obtained with relative ease via spectroscopic criteria, automated selection of post-coalescence merger remnants is restricted to the physical characteristics of remnants alone. Furthermore, such selection has predominantly focused on imaging data -whereas kinematic data may offer a complimentary basis for identifying merger remnants. Therefore, we examine the theoretical utility of both the morphological and kinematic features of merger remnants in distinguishing galaxy merger remnants from other galaxies. Deep classification models are calibrated and evaluated using idealized synthetic images and line-of-sight stellar velocity maps of a heterogeneous population of galaxies and merger remnants from the TNG100 cosmological hydrodynamical simulation. We show that even idealized stellar kinematic data has limited utility compared to imaging and under-performs by 2.1% ± 0.5% in completeness and 4.7% ± 0.4% in purity for our fiducial model architecture. Combining imaging and stellar kinematics offers a small boost in completeness (by 1.8% ± 0.4%, compared to 92.7% ± 0.2% from imaging alone) but no change in purity (0.1% ± 0.3% improvement compared to 92.7% ± 0.2%, evaluated with equal numbers of merger remnant and non-remnant control galaxies). Classification accuracy of all models is particularly sensitive to physical companions at separations 40 kpc and to time-since-coalescence. Taken together, our results show that the stellar kinematic data has little to offer in compliment to imaging for merger remnant identification in a heterogeneous galaxy population.

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What is Important? Morphological Asymmetries are Useful Predictors of Star Formation Rates of Star-forming Galaxies in SDSS Stripe 82

Cited by 3 publications

References 172 publications

The Relation between Morphological Asymmetry and Nuclear Activity in Low-redshift Galaxies

The Relation between Morphological Asymmetry and Nuclear Activity in Low-redshift Galaxies

Predicting resolved galaxy properties from photometric images using convolutional neural networks

The combined and respective roles of imaging and stellar kinematics in identifying galaxy merger remnants

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