Overview

Galaxy morphology is a long-standing subfield of astronomy, moving from visual qualifications to quantitative morphometrics.  This book covers the descriptions developed by astronomers to describe the appearance of galaxies, primarily in optical, ultraviolet and near-infrared wavelengths. These descriptions range from galaxy-wide down to clumps of stars and the phenomena on scales in between. It covers qualitative classification using descriptions of the light distributions, as well as some of the near-future techniques that are expected to play a role as astronomy moves to surveys of millions of galaxies and to depths that are dominated by low-surface-brightness. Each chapter is accompanied by an appropriate Jupyter Notebook Python programming assignment. The book is aimed at the graduate student level for researchers in need to a review of galaxy morphology techniques. Key Features: Aimed at graduate students or researchers in need to a review of galaxy morphology techniques Presents qualitative and quantitative galaxy morphology classifications Cover near-future techniques expected to play a role for large galaxy surveys Includes Jupyter Notebook workable examples in each chapter

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"B. W. Holwerda's Galaxy Morphology is an excellent introduction to the quantitative methods that have been used and is geared to the era of large image databases and the sophisticated programs needed to analyse them. These databases cover a wide range of redshifts and morphology, from X-rays to radio waves. To analyse properly such material, it is essential to have effective ways of quantifying characteristics such as angular size, integrated brightness, and other aspects of galaxy structure. Astronomers have long sought ways of replacing visual morphological classes with quantitative representations that can be used to determine scaling relations and to evaluate the accuracy of models of galaxy structure and evolution. Parameters such as the Sersic index and nonparametric approaches such as the CAS system can be effective for quantitative morphology but still have limitations. The interplay between visual and quantitative classifications led to the idea of using machine-learning methods to classify galaxies. Holwerda covers all of these topics and much more. The book is suitable for a course on galaxies and is written for extragalactic astronomy students ""at any level"". Each chapter is accompanied by a 'Jupyter notebook' assignment and has a useful list of articles for further reading. Ron Buta, The Observatory, October 2022"

B. W. Holwerda’s Galaxy Morphology is an excellent introduction to the quantitative methods that have been used and is geared to the era of large image databases and the sophisticated programs needed to analyse them. These databases cover a wide range of redshifts and morphology, from X-rays to radio waves. To analyse properly such material, it is essential to have effective ways of quantifying characteristics such as angular size, integrated brightness, and other aspects of galaxy structure. Astronomers have long sought ways of replacing visual morphological classes with quantitative representations that can be used to determine scaling relations and to evaluate the accuracy of models of galaxy structure and evolution. Parameters such as the Sersic index and nonparametric approaches such as the CAS system can be effective for quantitative morphology but still have limitations. The interplay between visual and quantitative classifications led to the idea of using machine-learning methods to classify galaxies. Holwerda covers all of these topics and much more. The book is suitable for a course on galaxies and is written for extragalactic astronomy students “at any level”. Each chapter is accompanied by a ‘Jupyter notebook’ assignment and has a useful list of articles for further reading. Ron Buta, The Observatory, October 2022 -- Ron Buta * The Observatory *

B. W. Holwerda's Galaxy Morphology is an excellent introduction to the quantitative methods that have been used and is geared to the era of large image databases and the sophisticated programs needed to analyse them. These databases cover a wide range of redshifts and morphology, from X-rays to radio waves. To analyse properly such material, it is essential to have effective ways of quantifying characteristics such as angular size, integrated brightness, and other aspects of galaxy structure. Astronomers have long sought ways of replacing visual morphological classes with quantitative representations that can be used to determine scaling relations and to evaluate the accuracy of models of galaxy structure and evolution. Parameters such as the Sersic index and nonparametric approaches such as the CAS system can be effective for quantitative morphology but still have limitations. The interplay between visual and quantitative classifications led to the idea of using machine-learning methods to classify galaxies. Holwerda covers all of these topics and much more. The book is suitable for a course on galaxies and is written for extragalactic astronomy students at any level . Each chapter is accompanied by a 'Jupyter notebook' assignment and has a useful list of articles for further reading. Ron Buta, The Observatory, October 2022 -- Ron Buta * The Observatory *

Author Information

Benne Holwerda is an associate professor at the University of Louisville, and is a leading expert in astronomical source catalogues and object characterization. He has worked on several projects that involve galaxy morphology classification and characterization; the Spitzer Survey of Spiral Structure in Galaxies, the Galaxy and Mass Assembly survey and the GHOSTS survey which explore extremes of low surface brightness. He authored the application of galaxy morphometrics on HI images.

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