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@article{hubble1926extragalactic,
title={Extragalactic nebulae.},
author={Hubble, Edwin P},
journal={The Astrophysical Journal},
volume={64},
year={1926}
}
@ARTICLE{1959HDP....53..275D,
author = {{de Vaucouleurs}, Gerard},
title = "{Classification and Morphology of External Galaxies.}",
journal = {Handbuch der Physik},
year = 1959,
month = jan,
volume = {53},
pages = {275},
doi = {10.1007/978-3-642-45932-0_7},
adsurl = {https://ui.adsabs.harvard.edu/abs/1959HDP....53..275D},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@article {Edmondson464,
author = {Edmondson, Frank K.},
title = {The Hubble Atlas of Galaxies. Allan Sandage. Carnegie Institution of Washington, Washington, D.C., 1961. viii + 32 pp. Illus. + 50 plates. 10},
volume = {134},
number = {3477},
pages = {464--464},
year = {1961},
doi = {10.1126/science.134.3477.464},
publisher = {American Association for the Advancement of Science},
issn = {0036-8075},
URL = {https://science.sciencemag.org/content/134/3477/464},
eprint = {https://science.sciencemag.org/content/134/3477/464.full.pdf},
journal = {Science}
}
@ARTICLE{1976ApJ...206..883V,
author = {{van den Bergh}, S.},
title = "{A new classification system for galaxies.}",
journal = {\apj},
keywords = {Classifications, Galactic Evolution, Galactic Structure, Spiral Galaxies, Abundance, Elliptical Galaxies, Hubble Diagram, Interstellar Gas, Luminous Intensity, Astrophysics},
year = 1976,
month = jun,
volume = {206},
pages = {883-887},
doi = {10.1086/154452},
adsurl = {https://ui.adsabs.harvard.edu/abs/1976ApJ...206..883V},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@book{1968adga.book.....S,
title={Atlas de galaxias australes},
author={S{\'e}rsic, J.L. and Universidad Nacional de C{\'o}rdoba. Observatorio Astron{\'o}mico},
url={https://books.google.co.in/books?id=4YF9QgAACAAJ},
year={1968},
publisher={Observatorio Astronomico, Universidad Nacional de Cordoba}
}
@article{Cohen_2003,
doi = {10.1086/368367},
url = {https://doi.org/10.1086%2F368367},
year = 2003,
month = {apr},
publisher = {{IOP} Publishing},
volume = {125},
number = {4},
pages = {1762--1783},
author = {Seth H. Cohen and Rogier A. Windhorst and Stephen C. Odewahn and Claudia A. Chiarenza and Simon P. Driver},
title = {The [{ITAL}]Hubble Space Telescope[/{ITAL}] {WFPC}2 [{ITAL}]B[/{ITAL}]-Band Parallel Survey: A Study of Galaxy Morphology for Magnitudes 18{\hspace{0.167em}}$\leq${\hspace{0.167em}}[{ITAL}]B[/{ITAL}]{\hspace{0.167em}}$\leq${\hspace{0.167em}}27},
journal = {The Astronomical Journal},
abstract = {We present the results of the Hubble Space Telescope B-Band Parallel Survey (BBPS). It covers 0.0370 deg2 and consists of 31 shallow (four- to six-orbit), randomly selected high-latitude HST WFPC2 parallel fields with images taken in both the B (F450W) and I (F814W) filters. The goal of this survey is to morphologically classify the galaxies in a homogeneous manner and study galaxy properties as a function of type and B-band magnitude for 18 mag ≲ bJ ≲ 23.5 mag. The full sample contains 1800 galaxies, 370 of which are brighter than the formal statistical completeness limit of bJ ≲ 23.5 mag. The galaxies are selected from the B-band images and classified using an artificial neural network (ANN) galaxy classifier on the higher signal-to-noise ratio I-band images. These provide (more) reliable types for I ≲ 24 mag (or bJ ≲ 26 mag), since these I-band classifications are less subject to the uncertain redshifted rest-frame UV morphology. The ANN classification depends on the shape of the surface brightness profile, but not on color. These results are combined with similar (deeper) studies in the Hubble Deep Field and the deep WFPC2 field surrounding the radio galaxy 53W002, for which galaxies have been classified to bJ ≲ 27 mag. The galaxy counts for the combined B-band–selected samples show adequate statistics for a range 19 mag ≲ bJ ≲ 27 mag and are in good agreement with other studies in the flux range where they overlap, while showing improved statistics at the bright end. The galaxies are subdivided into three morphological classes: early types (E/S0), mid types (Sabc), and late types (Sd/Irr), and the B-band counts are presented for each class, as well as the total counts. The faint end of the counts is dominated by the irregular galaxies, which have a steep count slope of d log N/dm ≈ 0.4. These type-dependent counts are compared with models based on local luminosity functions that include the effects of the cosmological constant, ΩΛ. The whole BBPS sample, along with the two deeper fields, is used to delineate the general trends of effective radius and B-I color as function of both morphological type and apparent magnitude for 18 mag ≲ bJ ≲ 27 mag. These properties are discussed in the context of recent redshift surveys. A possible explanation for the combined results is given in terms of the effects of ΩΛ on the evolution of the merger rate in a hierarchical scenario.}
}
@article{abraham1994morphologies,
title={The morphologies of distant galaxies. 1: an automated classification system},
author={Abraham, Roberto G and Valdes, Francisco and Yee, HKC and van den Bergh, Sidney},
journal={The Astrophysical Journal},
volume={432},
pages={75--90},
year={1994}
}
@ARTICLE{2003ApJS..147....1C,
author = {{Conselice}, Christopher J.},
title = "{The Relationship between Stellar Light Distributions of Galaxies and Their Formation Histories}",
journal = {\apjs},
keywords = {Galaxies: Evolution, Galaxies: Formation, Galaxies: Structure, Astrophysics},
year = 2003,
month = jul,
volume = {147},
number = {1},
pages = {1-28},
doi = {10.1086/375001},
archivePrefix = {arXiv},
eprint = {astro-ph/0303065},
primaryClass = {astro-ph},
adsurl = {https://ui.adsabs.harvard.edu/abs/2003ApJS..147....1C},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@ARTICLE{2004AJ....128..163L,
author = {{Lotz}, Jennifer M. and {Primack}, Joel and {Madau}, Piero},
title = "{A New Nonparametric Approach to Galaxy Morphological Classification}",
journal = {\aj},
keywords = {Galaxies: Fundamental Parameters, Galaxies: High-Redshift, Galaxies: Peculiar, Galaxies: Structure, Astrophysics},
year = 2004,
month = jul,
volume = {128},
number = {1},
pages = {163-182},
doi = {10.1086/421849},
archivePrefix = {arXiv},
eprint = {astro-ph/0311352},
primaryClass = {astro-ph},
adsurl = {https://ui.adsabs.harvard.edu/abs/2004AJ....128..163L},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{Djorgovski_2013,
title={Sky Surveys},
ISBN={9789400756182},
url={http://dx.doi.org/10.1007/978-94-007-5618-2_5},
DOI={10.1007/978-94-007-5618-2_5},
journal={Planets, Stars and Stellar Systems},
publisher={Springer Netherlands},
author={Djorgovski, S. George and Mahabal, Ashish and Drake, Andrew and Graham, Matthew and Donalek, Ciro},
year={2013},
pages={223–281}
}
@ARTICLE{2007AJ....134..579F,
author = {{Fukugita}, Masataka and {Nakamura}, Osamu and {Okamura}, Sadanori and
{Yasuda}, Naoki and {Barentine}, John C. and {Brinkmann}, Jon and
{Gunn}, James E. and {Harvanek}, Mike and {Ichikawa}, Takashi and
{Lupton}, Robert H. and {Schneider}, Donald P. and
{Strauss}, Michael A. and {York}, Donald G.},
title = "{A Catalog of Morphologically Classified Galaxies from the Sloan Digital Sky Survey: North Equatorial Region}",
journal = {\aj},
keywords = {catalogs, galaxies: fundamental parameters, Astrophysics},
year = 2007,
month = aug,
volume = {134},
number = {2},
pages = {579-593},
doi = {10.1086/518962},
archivePrefix = {arXiv},
eprint = {0704.1743},
primaryClass = {astro-ph},
adsurl = {https://ui.adsabs.harvard.edu/abs/2007AJ....134..579F},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{ refId0,
author = {{Baillard, A.} and {Bertin, E.} and {de Lapparent, V.} and {Fouqu\'e, P.} and {Arnouts, S.} and {Mellier, Y.} and {Pell\'o, R.} and {Leborgne, J.-F.} and {Prugniel, P.} and {Makarov, D.} and {Makarova, L.} and {McCracken, H. J.} and {Bijaoui, A.} and {Tasca, L.}},
title = {The EFIGI catalogue of 4458 nearby galaxies with detailed morphology},
DOI= "10.1051/0004-6361/201016423",
url= "https://doi.org/10.1051/0004-6361/201016423",
journal = {A\&A},
year = 2011,
volume = 532,
pages = "A74",
month = "",
}
@ARTICLE{2010ApJS..186..427N,
author = {{Nair}, Preethi B. and {Abraham}, Roberto G.},
title = "{A Catalog of Detailed Visual Morphological Classifications for 14,034 Galaxies in the Sloan Digital Sky Survey}",
journal = {\apjs},
keywords = {catalogs, galaxies: fundamental parameters, galaxies: photometry, galaxies: structure, Astrophysics - Cosmology and Nongalactic Astrophysics},
year = 2010,
month = feb,
volume = {186},
number = {2},
pages = {427-456},
doi = {10.1088/0067-0049/186/2/427},
archivePrefix = {arXiv},
eprint = {1001.2401},
primaryClass = {astro-ph.CO},
adsurl = {https://ui.adsabs.harvard.edu/abs/2010ApJS..186..427N},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{Willett_2013,
title={Galaxy Zoo 2: detailed morphological classifications for 304 122 galaxies from the Sloan Digital Sky Survey},
volume={435},
ISSN={0035-8711},
url={http://dx.doi.org/10.1093/mnras/stt1458},
DOI={10.1093/mnras/stt1458},
number={4},
journal={Monthly Notices of the Royal Astronomical Society},
publisher={Oxford University Press (OUP)},
author={Willett, Kyle W. and Lintott, Chris J. and Bamford, Steven P. and Masters, Karen L. and Simmons, Brooke D. and Casteels, Kevin R. V. and Edmondson, Edward M. and Fortson, Lucy F. and Kaviraj, Sugata and Keel, William C. and et al.},
year={2013},
month={Sep},
pages={2835–2860}
}
@ARTICLE{2008MNRAS.389.1179L,
author = {{Lintott}, Chris J. and {Schawinski}, Kevin and {Slosar}, An{\v{z}}e and
{Land}, Kate and {Bamford}, Steven and {Thomas}, Daniel and
{Raddick}, M. Jordan and {Nichol}, Robert C. and {Szalay}, Alex and
{Andreescu}, Dan and {Murray}, Phil and {Vandenberg}, Jan},
title = "{Galaxy Zoo: morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey}",
journal = {\mnras},
keywords = {methods: data analysis, galaxies: elliptical and lenticular, cD, galaxies: general, galaxies: spiral, Astrophysics},
year = 2008,
month = sep,
volume = {389},
number = {3},
pages = {1179-1189},
doi = {10.1111/j.1365-2966.2008.13689.x},
archivePrefix = {arXiv},
eprint = {0804.4483},
primaryClass = {astro-ph},
adsurl = {https://ui.adsabs.harvard.edu/abs/2008MNRAS.389.1179L},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{Lintott_2010,
title={Galaxy Zoo 1: data release of morphological classifications for nearly 900 000 galaxies★},
volume={410},
ISSN={0035-8711},
url={http://dx.doi.org/10.1111/j.1365-2966.2010.17432.x},
DOI={10.1111/j.1365-2966.2010.17432.x},
number={1},
journal={Monthly Notices of the Royal Astronomical Society},
publisher={Oxford University Press (OUP)},
author={Lintott, Chris and Schawinski, Kevin and Bamford, Steven and Slosar, Anže and Land, Kate and Thomas, Daniel and Edmondson, Edd and Masters, Karen and Nichol, Robert C. and Raddick, M. Jordan and et al.},
year={2010},
month={Nov},
pages={166–178}
}
@ARTICLE{2010MNRAS.401.1552D,
author = {{Darg}, D.~W. and {Kaviraj}, S. and {Lintott}, C.~J. and
{Schawinski}, K. and {Sarzi}, M. and {Bamford}, S. and {Silk}, J. and
{Andreescu}, D. and {Murray}, P. and {Nichol}, R.~C. and
{Raddick}, M.~J. and {Slosar}, A. and {Szalay}, A.~S. and {Thomas}, D. and
{Vandenberg}, J.},
title = "{Galaxy Zoo: the properties of merging galaxies in the nearby Universe - local environments, colours, masses, star formation rates and AGN activity}",
journal = {\mnras},
keywords = {catalogues, galaxies: elliptical and lenticular, cD, galaxies: evolution, galaxies: general, galaxies: interactions, galaxies: spiral, Astrophysics - Astrophysics of Galaxies},
year = 2010,
month = jan,
volume = {401},
number = {3},
pages = {1552-1563},
doi = {10.1111/j.1365-2966.2009.15786.x},
archivePrefix = {arXiv},
eprint = {0903.5057},
primaryClass = {astro-ph.GA},
adsurl = {https://ui.adsabs.harvard.edu/abs/2010MNRAS.401.1552D},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{Land_et_all_2008,
author = {Land, Kate and Slosar, Anže and Lintott, Chris and Andreescu, Dan and Bamford, Steven and Murray, Phil and Nichol, Robert and Raddick, M. Jordan and Schawinski, Kevin and Szalay, Alex and Thomas, Daniel and Vandenberg, Jan},
title = "{Galaxy Zoo: the large-scale spin statistics of spiral galaxies in the Sloan Digital Sky Survey*}",
journal = {Monthly Notices of the Royal Astronomical Society},
volume = {388},
number = {4},
pages = {1686-1692},
year = {2008},
month = {08},
abstract = "{We re-examine the evidence for a violation of large-scale statistical isotropy in the distribution of projected spin vectors of spiral galaxies. We have a sample of ∼37 000 spiral galaxies from the Sloan Digital Sky Survey, with their line of sight spin direction confidently classified by members of the public through the online project Galaxy Zoo. After establishing and correcting for a certain level of bias in our handedness results we find the winding sense of the galaxies to be consistent with statistical isotropy. In particular, we find no significant dipole signal, and thus no evidence for overall preferred handedness of the Universe. We compare this result to those of other authors and conclude that these may also be affected and explained by a bias effect.}",
issn = {0035-8711},
doi = {10.1111/j.1365-2966.2008.13490.x},
url = {https://doi.org/10.1111/j.1365-2966.2008.13490.x},
eprint = {https://academic.oup.com/mnras/article-pdf/388/4/1686/3040762/mnras0388-1686.pdf},
}
@article{Schawinski,
author = {Schawinski, Kevin and Lintott, Chris and Thomas, Daniel and Sarzi, Marc and Andreescu, Dan and Bamford, Steven P. and Kaviraj, Sugata and Khochfar, Sadegh and Land, Kate and Murray, Phil and Nichol, Robert C. and Raddick, M. Jordan and Slosar, Anže and Szalay, Alex and VandenBerg, Jan and Yi, Sukyoung K.},
title = "{Galaxy Zoo: a sample of blue early-type galaxies at low redshift*}",
journal = {Monthly Notices of the Royal Astronomical Society},
volume = {396},
number = {2},
pages = {818-829},
year = {2009},
month = {06},
abstract = "{We report the discovery of a population of nearby, blue early-type galaxies with high star formation rates (0.5 \\< SFR \\< 50 M⊙ yr−1). They are identified by their visual morphology as provided by Galaxy Zoo for Sloan Digital Sky Survey Data Release 6 and their u−r colour. We select a volume-limited sample in the redshift range 0.02 \\< z \\< 0.05, corresponding to luminosities of approximately L* and above and with u−r colours significantly bluer than the red sequence. We confirm the early-type morphology of the objects in this sample and investigate their environmental dependence and star formation properties. Blue early-type galaxies tend to live in lower density environments than ‘normal’ red sequence early-types and make up 5.7 ± 0.4 per cent of the low-redshift early-type galaxy population. We find that such blue early-type galaxies are virtually absent at high velocity dispersions above 200 km s−1. Our analysis uses emission line diagnostic diagrams and we find that ∼25 per cent of them are actively star forming, while another ∼25 per cent host both star formation and an active galactic nucleus (AGN). Another ∼12 per cent are AGN. The remaining 38 per cent show no strong emission lines. When present and uncontaminated by an AGN contribution, the star formation is generally intense. We consider star formation rates derived from Hα, u band and infrared luminosities, and radial colour profiles, and conclude that the star formation is spatially extended. Of those objects that are not currently undergoing star formation must have ceased doing so recently in order to account for their blue optical colours. The gas-phase metallicity of the actively star-forming blue early-types galaxies is supersolar in all cases. We discuss the place of these objects in the context of galaxy formation. A catalogue of all 204 blue early-type galaxies in our sample, including star formation rates, emission line classification is provided.}",
issn = {0035-8711},
doi = {10.1111/j.1365-2966.2009.14793.x},
url = {https://doi.org/10.1111/j.1365-2966.2009.14793.x},
eprint = {https://academic.oup.com/mnras/article-pdf/396/2/818/17321994/mnras0396-0818.pdf},
}
@article{Bamford,
author = {Bamford, Steven P. and Nichol, Robert C. and Baldry, Ivan K. and Land, Kate and Lintott, Chris J. and Schawinski, Kevin and Slosar, Anže and Szalay, Alexander S. and Thomas, Daniel and Torki, Mehri and Andreescu, Dan and Edmondson, Edward M. and Miller, Christopher J. and Murray, Phil and Raddick, M. Jordan and Vandenberg, Jan},
title = "{Galaxy Zoo: the dependence of morphology and colour on environment*}",
journal = {Monthly Notices of the Royal Astronomical Society},
volume = {393},
number = {4},
pages = {1324-1352},
year = {2009},
month = {02},
abstract = "{We analyse the relationships between galaxy morphology, colour, environment and stellar mass using data for over 105 objects from Galaxy Zoo, the largest sample of visually classified morphologies yet compiled. We conclusively show that colour and morphology fractions are very different functions of environment. Both colour and morphology are sensitive to stellar mass. However, at fixed stellar mass, while colour is also highly sensitive to environment, morphology displays much weaker environmental trends. Only a small part of both the morphology–density and colour–density relations can be attributed to the variation in the stellar-mass function with environment.Galaxies with high stellar masses are mostly red in all environments and irrespective of their morphology. Low stellar-mass galaxies are mostly blue in low-density environments, but mostly red in high-density environments, again irrespective of their morphology. While galaxies with early-type morphology do always have higher red fractions, this is subdominant compared to the dependence of red fraction on stellar mass and environment. The colour–density relation is primarily driven by variations in colour fractions at fixed morphology, in particular the fraction of spiral galaxies that have red colours, and especially at low stellar masses. We demonstrate that our red spirals primarily include galaxies with true spiral morphology, and that they constitute an additional population to the S0 galaxies considered by previous studies. We clearly show there is an environmental dependence for colour beyond that for morphology. The environmental transformation of galaxies from blue to red must occur on significantly shorter time-scales than the transformation from spiral to early-type.We also present many of our results as functions of the distance to the nearest galaxy group. This confirms that the environmental trends we present are not specific to the manner in which environment is quantified, but nevertheless provides plain evidence for an environmental process at work in groups. However, the properties of group members show little dependence on the total mass of the group they inhabit, at least for group masses .Before using the Galaxy Zoo morphologies to produce the above results, we first quantify a luminosity-, size- and redshift-dependent classification bias that affects this data set, and probably most other studies of galaxy population morphology. A correction for this bias is derived and applied to produce a sample of galaxies with reliable morphological-type likelihoods, on which we base our analysis.}",
issn = {0035-8711},
doi = {10.1111/j.1365-2966.2008.14252.x},
url = {https://doi.org/10.1111/j.1365-2966.2008.14252.x},
eprint = {https://academic.oup.com/mnras/article-pdf/393/4/1324/17320867/mnras0393-1324.pdf},
}
@article{Willett_2015,
author = {Willett, Kyle W. and Schawinski, Kevin and Simmons, Brooke D. and Masters, Karen L. and Skibba, Ramin A. and Kaviraj, Sugata and Melvin, Thomas and Wong, O. Ivy and Nichol, Robert C. and Cheung, Edmond and Lintott, Chris J. and Fortson, Lucy},
title = "{Galaxy Zoo: the dependence of the star formation–stellar mass relation on spiral disc morphology}",
journal = {Monthly Notices of the Royal Astronomical Society},
volume = {449},
number = {1},
pages = {820-827},
year = {2015},
month = {03},
abstract = "{We measure the stellar mass–star formation rate (SFR) relation in star-forming disc galaxies at z ≤ 0.085, using Galaxy Zoo morphologies to examine different populations of spirals as classified by their kiloparsec-scale structure. We examine the number of spiral arms, their relative pitch angle, and the presence of a galactic bar in the disc, and show that both the slope and dispersion of the M⋆–SFR relation is constant when varying all the above parameters. We also show that mergers (both major and minor), which represent the strongest conditions for increases in star formation at a constant mass, only boost the SFR above the main relation by ∼0.3 dex; this is significantly smaller than the increase seen in merging systems at z \\> 1. Of the galaxies lying significantly above the M⋆–SFR relation in the local Universe, more than 50 per cent are mergers. We interpret this as evidence that the spiral arms, which are imperfect reflections of the galaxy's current gravitational potential, are either fully independent of the various quenching mechanisms or are completely overwhelmed by the combination of outflows and feedback. The arrangement of the star formation can be changed, but the system as a whole regulates itself even in the presence of strong dynamical forcing.}",
issn = {0035-8711},
doi = {10.1093/mnras/stv307},
url = {https://doi.org/10.1093/mnras/stv307},
eprint = {https://academic.oup.com/mnras/article-pdf/449/1/820/4144144/stv307.pdf},
}
@article{Naim/mnras/275.3.567,
author = {Naim, A. and Lahav, O. and Sodré, L., Jr. and Storrie-Lombardi, M. C.},
title = "{Automated morphological classification of APM galaxies by supervised artificial neural networks}",
journal = {Monthly Notices of the Royal Astronomical Society},
volume = {275},
number = {3},
pages = {567-590},
year = {1995},
month = {08},
abstract = "{We train artificial neural networks to classify galaxies based solely on the morphology of the galaxy images as they appear on blue survey plates. The images are reduced and morphological features such as bulge size and the number of arms are extracted, all in a fully automated manner. The galaxy sample was first classified by six independent experts. We use several definitions for the mean type of each galaxy, based on those classifications. We then train and test the network on these features. We find that the rms error of the network classifications, as compared with the mean types of the expert classifications, is 1.8 Revised Hubble Types. This is comparable to the overall rms dispersion between the experts. This result is robust and almost completely independent of the network architecture used.}",
issn = {0035-8711},
doi = {10.1093/mnras/275.3.567},
url = {https://doi.org/10.1093/mnras/275.3.567},
eprint = {https://academic.oup.com/mnras/article-pdf/275/3/567/2804452/mnras275-0567.pdf},
}
@article{Owens/mnras/281.1.153,
author = {Owens, E. A. and Griffiths, R. E. and Ratnatunga, K. U.},
title = "{Using oblique decision trees for the morphological classification of galaxies}",
journal = {Monthly Notices of the Royal Astronomical Society},
volume = {281},
number = {1},
pages = {153-157},
year = {1996},
month = {07},
abstract = "{We discuss the application of a class of machine learning algorithms known as decision trees to the process of galactic classification. In particular, we explore the application of oblique decision trees induced with different impurity measures to the problem of classifying galactic morphology data provided by Storrie-Lombardi et al. Our results are compared with those obtained by a neural network classifier created by Storrie-Lombardi et al., and we show that the two methodologies are comparable. We conclude with a demonstration that the original data can be easily classified into less well-defined categories.}",
issn = {0035-8711},
doi = {10.1093/mnras/281.1.153},
url = {https://doi.org/10.1093/mnras/281.1.153},
eprint = {https://academic.oup.com/mnras/article-pdf/281/1/153/18539987/281-1-153.pdf},
}
@article{Bazell_2001,
doi = {10.1086/318696},
url = {https://doi.org/10.1086%2F318696},
year = 2001,
month = {feb},
publisher = {{IOP} Publishing},
volume = {548},
number = {1},
pages = {219--223},
author = {D. Bazell and David~W. Aha},
title = {Ensembles of Classifiers for Morphological Galaxy Classification},
journal = {The Astrophysical Journal},
abstract = {We compare the use of three algorithms for performing automated morphological galaxy classification using a sample of 800 galaxies. Classifiers are created using a single training set as well as bootstrap replicates of the training set, producing an ensemble of classifiers. We use a Naive Bayes classifier, a neural network trained with backpropagation, and a decision-tree induction algorithm with pruning. Previous work in the field has emphasized backpropagation networks and decision trees. The Naive Bayes classifier is easy to understand and implement and often works remarkably well on real-world data. For each of these algorithms, we examine the classification accuracy of individual classifiers using 10-fold cross validation and of ensembles of classifiers trained using 25 bootstrap data sets and tested on the same cross-validation test sets. Our results show that (1) the neural network produced the best individual classifiers (lowest classification error) for the majority of cases, (2) the ensemble approach significantly reduced the classification error for the neural network and the decision-tree classifiers but not for the Naive Bayes classifier, (3) the ensemble approach worked better for decision trees (typical error reduction of 12%-23%) than for the neural network (typical error reduction of 7%-12%), and (4) the relative improvement when using ensembles decreases as the number of output classes increases. While more extensive comparisons are needed (e.g., a variety of data and classifiers), our work is the first demonstration that the ensemble approach can significantly increase the performance of certain automated classification methods when applied to the domain of morphological galaxy classification.}
}
@article{De_La_Calleja2004,
author = {De La Calleja, Jorge and Fuentes, Olac},
title = "{Machine learning and image analysis for morphological galaxy classification}",
journal = {Monthly Notices of the Royal Astronomical Society},
volume = {349},
number = {1},
pages = {87-93},
year = {2004},
month = {03},
abstract = "{In this paper we present an experimental study of machine learning and image analysis for performing automated morphological galaxy classification. We used a neural network, and a locally weighted regression method, and implemented homogeneous ensembles of classifiers. The ensemble of neural networks was created using the bagging ensemble method, and manipulation of input features was used to create the ensemble of locally weighed regression. The galaxies used were rotated, centred, and cropped, all in a fully automatic manner. In addition, we used principal component analysis to reduce the dimensionality of the data, and to extract relevant information in the images. Preliminary experimental results using 10-fold cross-validation show that the homogeneous ensemble of locally weighted regression produces the best results, with over 91 per cent accuracy when considering three galaxy types (E, S and Irr), and over 95 per cent accuracy for two types (E and S).}",
issn = {0035-8711},
doi = {10.1111/j.1365-2966.2004.07442.x},
url = {https://doi.org/10.1111/j.1365-2966.2004.07442.x},
eprint = {https://academic.oup.com/mnras/article-pdf/349/1/87/11183170/349-1-87.pdf},
}
@article{Banerji_2010,
author = {Banerji, Manda and Lahav, Ofer and Lintott, Chris J. and Abdalla, Filipe B. and Schawinski, Kevin and Bamford, Steven P. and Andreescu, Dan and Murray, Phil and Raddick, M. Jordan and Slosar, Anze and Szalay, Alex and Thomas, Daniel and Vandenberg, Jan},
title = "{Galaxy Zoo: reproducing galaxy morphologies via machine learning*}",
journal = {Monthly Notices of the Royal Astronomical Society},
volume = {406},
number = {1},
pages = {342-353},
year = {2010},
month = {07},
abstract = "{We present morphological classifications obtained using machine learning for objects in the Sloan Digital Sky Survey DR6 that have been classified by Galaxy Zoo into three classes, namely early types, spirals and point sources/artefacts. An artificial neural network is trained on a subset of objects classified by the human eye, and we test whether the machine-learning algorithm can reproduce the human classifications for the rest of the sample. We find that the success of the neural network in matching the human classifications depends crucially on the set of input parameters chosen for the machine-learning algorithm. The colours and parameters associated with profile fitting are reasonable in separating the objects into three classes. However, these results are considerably improved when adding adaptive shape parameters as well as concentration and texture. The adaptive moments, concentration and texture parameters alone cannot distinguish between early type galaxies and the point sources/artefacts. Using a set of 12 parameters, the neural network is able to reproduce the human classifications to better than 90 per cent for all three morphological classes. We find that using a training set that is incomplete in magnitude does not degrade our results given our particular choice of the input parameters to the network. We conclude that it is promising to use machine-learning algorithms to perform morphological classification for the next generation of wide-field imaging surveys and that the Galaxy Zoo catalogue provides an invaluable training set for such purposes.}",
issn = {0035-8711},
doi = {10.1111/j.1365-2966.2010.16713.x},
url = {https://doi.org/10.1111/j.1365-2966.2010.16713.x},
eprint = {https://academic.oup.com/mnras/article-pdf/406/1/342/3714678/mnras0406-0342.pdf},
}
@article{Gauci2010,
author = "Gauci, Adam and Adami, Kristian Zarb and Abela, John and Magro, Alessio",
title = "{Machine Learning for Galaxy Morphology Classification}",
eprint = "1005.0390",
archivePrefix = "arXiv",
primaryClass = "astro-ph.GA",
journal = {Monthly Notices of the Royal Astronomical Society},
month = "5",
year = "2010"
}
@article{Simmons2016CANDLES,
author = {Simmons, B. D. and Lintott, Chris and Willett, Kyle W. and Masters, Karen L. and Kartaltepe, Jeyhan S. and Häußler, Boris and Kaviraj, Sugata and Krawczyk, Coleman and Kruk, S. J. and McIntosh, Daniel H. and Smethurst, R. J. and Nichol, Robert C. and Scarlata, Claudia and Schawinski, Kevin and Conselice, Christopher J. and Almaini, Omar and Ferguson, Henry C. and Fortson, Lucy and Hartley, William and Kocevski, Dale and Koekemoer, Anton M. and Mortlock, Alice and Newman, Jeffrey A. and Bamford, Steven P. and Grogin, N. A. and Lucas, Ray A. and Hathi, Nimish P. and McGrath, Elizabeth and Peth, Michael and Pforr, Janine and Rizer, Zachary and Wuyts, Stijn and Barro, Guillermo and Bell, Eric F. and Castellano, Marco and Dahlen, Tomas and Dekel, Avishai and Ownsworth, Jamie and Faber, Sandra M. and Finkelstein, Steven L. and Fontana, Adriano and Galametz, Audrey and Grützbauch, Ruth and Koo, David and Lotz, Jennifer and Mobasher, Bahram and Mozena, Mark and Salvato, Mara and Wiklind, Tommy},
title = "{Galaxy Zoo: quantitative visual morphological classifications for 48 000 galaxies from CANDELS★}",
journal = {Monthly Notices of the Royal Astronomical Society},
volume = {464},
number = {4},
pages = {4420-4447},
year = {2016},
month = {10},
abstract = "{We present quantified visual morphologies of approximately 48 000 galaxies observed in three Hubble Space Telescope legacy fields by the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) and classified by participants in the Galaxy Zoo project. 90 per cent of galaxies have z ≤ 3 and are observed in rest-frame optical wavelengths by CANDELS. Each galaxy received an average of 40 independent classifications, which we combine into detailed morphological information on galaxy features such as clumpiness, bar instabilities, spiral structure, and merger and tidal signatures. We apply a consensus-based classifier weighting method that preserves classifier independence while effectively down-weighting significantly outlying classifications. After analysing the effect of varying image depth on reported classifications, we also provide depth-corrected classifications which both preserve the information in the deepest observations and also enable the use of classifications at comparable depths across the full survey. Comparing the Galaxy Zoo classifications to previous classifications of the same galaxies shows very good agreement; for some applications, the high number of independent classifications provided by Galaxy Zoo provides an advantage in selecting galaxies with a particular morphological profile, while in others the combination of Galaxy Zoo with other classifications is a more promising approach than using any one method alone. We combine the Galaxy Zoo classifications of ‘smooth’ galaxies with parametric morphologies to select a sample of featureless discs at 1 ≤ z ≤ 3, which may represent a dynamically warmer progenitor population to the settled disc galaxies seen at later epochs.}",
issn = {0035-8711},
doi = {10.1093/mnras/stw2587},
url = {https://doi.org/10.1093/mnras/stw2587},
eprint = {https://academic.oup.com/mnras/article-pdf/464/4/4420/8313511/stw2587.pdf},
}
@article{Willet2016HST,
author = {Willett, Kyle W. and Galloway, Melanie A. and Bamford, Steven P. and Lintott, Chris J. and Masters, Karen L. and Scarlata, Claudia and Simmons, B. D. and Beck, Melanie and Cardamone, Carolin N. and Cheung, Edmond and Edmondson, Edward M. and Fortson, Lucy F. and Griffith, Roger L. and Häußler, Boris and Han, Anna and Hart, Ross and Melvin, Thomas and Parrish, Michael and Schawinski, Kevin and Smethurst, R. J. and Smith, Arfon M.},
title = "{Galaxy Zoo: morphological classifications for 120 000 galaxies in HST legacy imaging★}",
journal = {Monthly Notices of the Royal Astronomical Society},
volume = {464},
number = {4},
pages = {4176-4203},
year = {2016},
month = {10},
abstract = "{We present the data release paper for the Galaxy Zoo: Hubble (GZH) project. This is the third phase in a large effort to measure reliable, detailed morphologies of galaxies by using crowdsourced visual classifications of colour-composite images. Images in GZH were selected from various publicly released Hubble Space Telescope legacy programmes conducted with the Advanced Camera for Surveys, with filters that probe the rest-frame optical emission from galaxies out to z ∼ 1. The bulk of the sample is selected to have mI814W \\< 23.5, but goes as faint as mI814W \\< 26.8 for deep images combined over five epochs. The median redshift of the combined samples is 〈z〉 = 0.9 ± 0.6, with a tail extending out to z ≃ 4. The GZH morphological data include measurements of both bulge- and disc-dominated galaxies, details on spiral disc structure that relate to the Hubble type, bar identification, and numerous measurements of clump identification and geometry. This paper also describes a new method for calibrating morphologies for galaxies of different luminosities and at different redshifts by using artificially redshifted galaxy images as a baseline. The GZH catalogue contains both raw and calibrated morphological vote fractions for 119 849 galaxies, providing the largest data set to date suitable for large-scale studies of galaxy evolution out to z ∼ 1.}",
issn = {0035-8711},
doi = {10.1093/mnras/stw2568},
url = {https://doi.org/10.1093/mnras/stw2568},
eprint = {https://academic.oup.com/mnras/article-pdf/464/4/4176/8312093/stw2568.pdf},
}
@article{Storrie1992,
author = {Storrie-Lombardi, Michael and Lahav, O. and Jr, Sodre},
year = {1992},
month = {10},
pages = {8P},
title = {Morphological Classification of Galaxies by Artificial Neural Networks},
volume = {259},
journal = {Monthly Notices of the Royal Astronomical Society},
doi = {10.1093/mnras/259.1.8P},
}
@article{Ferrari_2015,
doi = {10.1088/0004-637x/814/1/55},
url = {https://doi.org/10.1088\%2F0004-637x%2F814%2F1%2F55},
year = 2015,
month = {nov},
publisher = {{IOP} Publishing},
volume = {814},
number = {1},
pages = {55},
author = {F. Ferrari and R. R. de Carvalho and M. Trevisan},
title = {{MORFOMETRYKA}{\textemdash}A {NEW} {WAY} {OF} {ESTABLISHING} {MORPHOLOGICAL} {CLASSIFICATION} {OF} {GALAXIES}},
journal = {The Astrophysical Journal},
abstract = {We present an extended morphometric system to automatically classify galaxies from astronomical images. The new system includes the original and modified versions of the CASGM coefficients (Concentration C1, Asymmetry A3, and Smoothness S3), and the new parameters entropy, H, and spirality σψ. The new parameters A3, S3, and H are better to discriminate galaxy classes than A1, S1, and G, respectively. The new parameter σψ captures the amount of non-radial pattern on the image and is almost linearly dependent on T-type. Using a sample of spiral and elliptical galaxies from the Galaxy Zoo project as a training set, we employed the Linear Discriminant Analysis (LDA) technique to classify EFIGI (Baillard et al. 4458 galaxies), Nair & Abraham (14,123 galaxies), and SDSS Legacy (779,235 galaxies) samples. The cross-validation test shows that we can achieve an accuracy of more than 90\% with our classification scheme. Therefore, we are able to define a plane in the morphometric parameter space that separates the elliptical and spiral classes with a mismatch between classes smaller than 10\%. We use the distance to this plane as a morphometric index (Mi) and we show that it follows the human based T-type index very closely. We calculate morphometric index Mi for ∼780k galaxies from SDSS Legacy Survey–DR7. We discuss how Mi correlates with stellar population parameters obtained using the spectra available from SDSS–DR7.}
}
@article{Lecun2015,
title={Deep learning},
author={LeCun, Yann and Bengio, Yoshua and Hinton, Geoffrey},
journal={nature},
volume={521},
number={7553},
pages={436--444},
year={2015},
publisher={Nature Publishing Group}
}
@ARTICLE{Bengio2013,
author={Y. {Bengio} and A. {Courville} and P. {Vincent}},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, title={Representation Learning: A Review and New Perspectives},
year={2013},
volume={35},
number={8},
pages={1798-1828},
}
@ARTICLE{Dieleman2015,
author = {{Dieleman}, Sander and {Willett}, Kyle W. and {Dambre}, Joni},
title = "{Rotation-invariant convolutional neural networks for galaxy morphology prediction}",
journal = {\mnras},
keywords = {methods: data analysis, techniques: image processing, catalogues, galaxies: general, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Astrophysics of Galaxies, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Statistics - Machine Learning},
year = 2015,
month = jun,
volume = {450},
number = {2},
pages = {1441-1459},
doi = {10.1093/mnras/stv632},
archivePrefix = {arXiv},
eprint = {1503.07077},
primaryClass = {astro-ph.IM},
adsurl = {https://ui.adsabs.harvard.edu/abs/2015MNRAS.450.1441D},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@inproceedings{Mairal2014,
title={Convolutional Kernel Networks},
author={Julien Mairal and Piotr Koniusz and Za{\"i}d Harchaoui and Cordelia Schmid},
booktitle={NIPS},
year={2014}
}
@INBOOK{Polsterer_2012,
author = {{Polsterer}, K.~L. and {Gieseke}, F. and {Kramer}, O.},
title = "{Galaxy Classification without Feature Extraction}",
booktitle = {Astronomical Data Analysis Software and Systems XXI},
year = 2012,
editor = {{Ballester}, P. and {Egret}, D. and {Lorente}, N.~P.~F.},
volume = {461},
series = {Astronomical Society of the Pacific Conference Series},
pages = {561},
adsurl = {https://ui.adsabs.harvard.edu/abs/2012ASPC..461..561P},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@ARTICLE{Huertas-Company_2011,
author = {{Huertas-Company}, M. and {Aguerri}, J.~A.~L. and {Bernardi}, M. and
{Mei}, S. and {S{\'a}nchez Almeida}, J.},
title = "{Revisiting the Hubble sequence in the SDSS DR7 spectroscopic sample: a publicly available Bayesian automated classification}",
journal = {\aap},
keywords = {catalogs, astronomical databases: miscellaneous, Galaxy: fundamental parameters, galaxies: evolution, Galaxy: formation, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},
year = 2011,
month = jan,
volume = {525},
eid = {A157},
pages = {A157},
doi = {10.1051/0004-6361/201015735},
archivePrefix = {arXiv},
eprint = {1010.3018},
primaryClass = {astro-ph.CO},
adsurl = {https://ui.adsabs.harvard.edu/abs/2011A&A...525A.157H},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@ARTICLE{Abazajian2009,
author = {{Abazajian}, Kevork N. and {Adelman-McCarthy}, Jennifer K. and
{Ag{\"u}eros}, Marcel A. and {Allam}, Sahar S. and
{Allende Prieto}, Carlos and {An}, Deokkeun and {Anderson}, Kurt S.~J. and
{Anderson}, Scott F. and {Annis}, James and {Bahcall}, Neta A. and
{Bailer-Jones}, C.~A.~L. and {Barentine}, J.~C. and
{Bassett}, Bruce A. and {Becker}, Andrew C. and {Beers}, Timothy C. and
{Bell}, Eric F. and {Belokurov}, Vasily and {Berlind}, Andreas A. and
{Berman}, Eileen F. and {Bernardi}, Mariangela and
{Bickerton}, Steven J. and {Bizyaev}, Dmitry and {Blakeslee}, John P. and
{Blanton}, Michael R. and {Bochanski}, John J. and
{Boroski}, William N. and {Brewington}, Howard J. and
{Brinchmann}, Jarle and {Brinkmann}, J. and {Brunner}, Robert J. and
{Budav{\'a}ri}, Tam{\'a}s and {Carey}, Larry N. and {Carliles}, Samuel and
{Carr}, Michael A. and {Castander}, Francisco J. and {Cinabro}, David and
{Connolly}, A.~J. and {Csabai}, Istv{\'a}n and {Cunha}, Carlos E. and
{Czarapata}, Paul C. and {Davenport}, James R.~A. and {de Haas}, Ernst and
{Dilday}, Ben and {Doi}, Mamoru and {Eisenstein}, Daniel J. and
{Evans}, Michael L. and {Evans}, N.~W. and {Fan}, Xiaohui and
{Friedman}, Scott D. and {Frieman}, Joshua A. and {Fukugita}, Masataka and
{G{\"a}nsicke}, Boris T. and {Gates}, Evalyn and {Gillespie}, Bruce and
{Gilmore}, G. and {Gonzalez}, Belinda and {Gonzalez}, Carlos F. and
{Grebel}, Eva K. and {Gunn}, James E. and {Gy{\"o}ry}, Zsuzsanna and
{Hall}, Patrick B. and {Harding}, Paul and {Harris}, Frederick H. and
{Harvanek}, Michael and {Hawley}, Suzanne L. and
{Hayes}, Jeffrey J.~E. and {Heckman}, Timothy M. and {Hendry}, John S. and
{Hennessy}, Gregory S. and {Hindsley}, Robert B. and {Hoblitt}, J. and
{Hogan}, Craig J. and {Hogg}, David W. and {Holtzman}, Jon A. and
{Hyde}, Joseph B. and {Ichikawa}, Shin-ichi and {Ichikawa}, Takashi and
{Im}, Myungshin and {Ivezi{\'c}}, {\v{Z}}eljko and {Jester}, Sebastian and
{Jiang}, Linhua and {Johnson}, Jennifer A. and {Jorgensen}, Anders M. and
{Juri{\'c}}, Mario and {Kent}, Stephen M. and {Kessler}, R. and
{Kleinman}, S.~J. and {Knapp}, G.~R. and {Konishi}, Kohki and
{Kron}, Richard G. and {Krzesinski}, Jurek and {Kuropatkin}, Nikolay and
{Lampeitl}, Hubert and {Lebedeva}, Svetlana and {Lee}, Myung Gyoon and
{Lee}, Young Sun and {French Leger}, R. and
{L{\'e}pine}, S{\'e}bastien and {Li}, Nolan and {Lima}, Marcos and
{Lin}, Huan and {Long}, Daniel C. and {Loomis}, Craig P. and
{Loveday}, Jon and {Lupton}, Robert H. and {Magnier}, Eugene and
{Malanushenko}, Olena and {Malanushenko}, Viktor and {Mand
elbaum}, Rachel and {Margon}, Bruce and {Marriner}, John P. and
{Mart{\'\i}nez-Delgado}, David and {Matsubara}, Takahiko and
{McGehee}, Peregrine M. and {McKay}, Timothy A. and {Meiksin}, Avery and
{Morrison}, Heather L. and {Mullally}, Fergal and {Munn}, Jeffrey A. and
{Murphy}, Tara and {Nash}, Thomas and {Nebot}, Ada and
{Neilsen}, Eric H., Jr. and {Newberg}, Heidi Jo and {Newman}, Peter R. and
{Nichol}, Robert C. and {Nicinski}, Tom and {Nieto-Santisteban}, Maria and
{Nitta}, Atsuko and {Okamura}, Sadanori and {Oravetz}, Daniel J. and
{Ostriker}, Jeremiah P. and {Owen}, Russell and {Padmanabhan}, Nikhil and
{Pan}, Kaike and {Park}, Changbom and {Pauls}, George and
{Peoples}, John, Jr. and {Percival}, Will J. and {Pier}, Jeffrey R. and
{Pope}, Adrian C. and {Pourbaix}, Dimitri and {Price}, Paul A. and
{Purger}, Norbert and {Quinn}, Thomas and {Raddick}, M. Jordan and
{Re Fiorentin}, Paola and {Richards}, Gordon T. and
{Richmond}, Michael W. and {Riess}, Adam G. and {Rix}, Hans-Walter and
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{Schneider}, Donald P. and {Scholz}, Ralf-Dieter and
{Schreiber}, Matthias R. and {Schwope}, Axel D. and
{Seljak}, Uro{\v{s}} and {Sesar}, Branimir and {Sheldon}, Erin and
{Shimasaku}, Kazu and {Sibley}, Valena C. and {Simmons}, A.~E. and
{Sivarani}, Thirupathi and {Allyn Smith}, J. and {Smith}, Martin C. and
{Smol{\v{c}}i{\'c}}, Vernesa and {Snedden}, Stephanie A. and
{Stebbins}, Albert and {Steinmetz}, Matthias and {Stoughton}, Chris and
{Strauss}, Michael A. and {SubbaRao}, Mark and {Suto}, Yasushi and
{Szalay}, Alexander S. and {Szapudi}, Istv{\'a}n and {Szkody}, Paula and
{Tanaka}, Masayuki and {Tegmark}, Max and {Teodoro}, Luis F.~A. and
{Thakar}, Aniruddha R. and {Tremonti}, Christy A. and
{Tucker}, Douglas L. and {Uomoto}, Alan and {Vanden Berk}, Daniel E. and
{Vandenberg}, Jan and {Vidrih}, S. and {Vogeley}, Michael S. and
{Voges}, Wolfgang and {Vogt}, Nicole P. and {Wadadekar}, Yogesh and
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{Yanny}, Brian and {Yocum}, D.~R. and {York}, Donald G. and
{Zehavi}, Idit and {Zibetti}, Stefano and {Zucker}, Daniel B.},
title = "{The Seventh Data Release of the Sloan Digital Sky Survey}",
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year = 2009,
month = jun,
volume = {182},
number = {2},
pages = {543-558},
doi = {10.1088/0067-0049/182/2/543},
archivePrefix = {arXiv},
eprint = {0812.0649},
primaryClass = {astro-ph},
adsurl = {https://ui.adsabs.harvard.edu/abs/2009ApJS..182..543A},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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title={Galaxy morphology classification with deep convolutional neural networks},
author={Zhu, Xiao-Pan and Dai, Jia-Ming and Bian, Chun-Jiang and Chen, Yu and Chen, Shi and Hu, Chen},
journal={Astrophysics and Space Science},
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number={4},
pages={55},
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publisher={Springer}
}
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@book{Goodfellow-et-al-2016,
title={Deep Learning},
author={Ian Goodfellow and Yoshua Bengio and Aaron Courville},
publisher={MIT Press},
note={\url{http://www.deeplearningbook.org}},
year={2016}
}
@inproceedings{Nair,
author = {Nair, Vinod and Hinton, Geoffrey E.},
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year = {2010},
isbn = {9781605589077},
publisher = {Omnipress},
address = {Madison, WI, USA},
booktitle = {Proceedings of the 27th International Conference on International Conference on Machine Learning},
pages = {807–814},
numpages = {8},
location = {Haifa, Israel},
series = {ICML’10}
}
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title={Dropout: a simple way to prevent neural networks from overfitting},
author={Srivastava, Nitish and Hinton, Geoffrey and Krizhevsky, Alex and Sutskever, Ilya and Salakhutdinov, Ruslan},
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number={1},
pages={1929--1958},
year={2014},
publisher={JMLR. org}
}
@inproceedings{Krizhevsky,
author = {Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E.},
title = {ImageNet Classification with Deep Convolutional Neural Networks},
year = {2012},
publisher = {Curran Associates Inc.},
address = {Red Hook, NY, USA},
booktitle = {Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1},
pages = {1097–1105},
numpages = {9},
location = {Lake Tahoe, Nevada},
series = {NIPS’12}
}
@InProceedings{Ioffe,
title = {Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift},
author = {Sergey Ioffe and Christian Szegedy},
booktitle = {Proceedings of the 32nd International Conference on Machine Learning},
pages = {448--456},
year = {2015},
editor = {Francis Bach and David Blei},
volume = {37},
series = {Proceedings of Machine Learning Research},
address = {Lille, France},
month = {07--09 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v37/ioffe15.pdf},
url = {http://proceedings.mlr.press/v37/ioffe15.html},
abstract = {Training Deep Neural Networks is complicated by the fact that the distribution of each layer’s inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a stateof-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82\% top-5 test error, exceeding the accuracy of human raters.}
}
@incollection{alex,
title = {ImageNet Classification with Deep Convolutional Neural Networks},
author = {Alex Krizhevsky and Sutskever, Ilya and Hinton, Geoffrey E},
booktitle = {Advances in Neural Information Processing Systems 25},
editor = {F. Pereira and C. J. C. Burges and L. Bottou and K. Q. Weinberger},
pages = {1097--1105},
year = {2012},
publisher = {Curran Associates, Inc.}
}
@misc{simonyan,
title={Very Deep Convolutional Networks for Large-Scale Image Recognition},
author={Karen Simonyan and Andrew Zisserman},
year={2014},
eprint={1409.1556},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@INPROCEEDINGS{Szegedy,
author={C. {Szegedy} and {Wei Liu} and {Yangqing Jia} and P. {Sermanet} and S. {Reed} and D. {Anguelov} and D. {Erhan} and V. {Vanhoucke} and A. {Rabinovich}},
booktitle={2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
title={Going deeper with convolutions},
year={2015},
volume={},
number={},
pages={1-9},}
@INPROCEEDINGS{K.He,
author={K. {He} and X. {Zhang} and S. {Ren} and J. {Sun}},
booktitle={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
title={Deep Residual Learning for Image Recognition},
year={2016},
volume={},
number={},
pages={770-778},
abstract={Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57\% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28\% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.},
keywords={image classification;learning (artificial intelligence);neural nets;object detection;COCO segmentation;ImageNet localization;ILSVRC & COCO 2015 competitions;deep residual nets;COCO object detection dataset;visual recognition tasks;CIFAR-10;ILSVRC 2015 classification task;ImageNet test set;VGG nets;residual nets;ImageNet dataset;residual function learning;deeper neural network training;image recognition;deep residual learning;Training;Degradation;Complexity theory;Image recognition;Neural networks;Visualization;Image segmentation},
doi={10.1109/CVPR.2016.90},
ISSN={1063-6919},
month={June},}
@inproceedings{zeiler,
title={Visualizing and understanding convolutional networks},
author={Zeiler, Matthew D and Fergus, Rob},
booktitle={European conference on computer vision},
pages={818--833},
year={2014},
organization={Springer}
}
@inproceedings{he2016,
title={Deep residual learning for image recognition},
author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={770--778},
year={2016}
}
@article{Huang,
author = {Gao Huang and
Zhuang Liu and
Kilian Q. Weinberger},
title = {Densely Connected Convolutional Networks},
journal = {CoRR},
volume = {abs/1608.06993},
year = {2016},
url = {http://arxiv.org/abs/1608.06993},
archivePrefix = {arXiv},
eprint = {1608.06993},
timestamp = {Mon, 10 Sep 2018 15:49:32 +0200},
biburl = {https://dblp.org/rec/journals/corr/HuangLW16a.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{Huertas,
doi = {10.1088/0067-0049/221/1/8},
url = {https://doi.org/10.1088%2F0067-0049%2F221%2F1%2F8},
year = 2015,
month = {oct},
publisher = {{IOP} Publishing},
volume = {221},
number = {1},
pages = {8},
author = {M. Huertas-Company and R. Gravet and G. Cabrera-Vives and P. G. P{\'{e}}rez-Gonz{\'{a}}lez and J. S. Kartaltepe and G. Barro and M. Bernardi and S. Mei and F. Shankar and P. Dimauro and E. F. Bell and D. Kocevski and D. C. Koo and S. M. Faber and D. H. Mcintosh},
title = {A {CATALOG} {OF} {VISUAL}-{LIKE} {MORPHOLOGIES} {IN} {THE} 5 {CANDELS} {FIELDS} {USING} {DEEP} {LEARNING}},
journal = {The Astrophysical Journal Supplement Series},
abstract = {We present a catalog of visual-like H-band morphologies of ∼50.000 galaxies (Hf160w < 24.5) in the 5 CANDELS fields (GOODS-N, GOODS-S, UDS, EGS, and COSMOS). Morphologies are estimated using Convolutional Neural Networks (ConvNets). The median redshift of the sample is The algorithm is trained on GOODS-S, for which visual classifications are publicly available, and then applied to the other 4 fields. Following the CANDELS main morphology classification scheme, our model retrieves for each galaxy the probabilities of having a spheroid or a disk, presenting an irregularity, being compact or a point source, and being unclassifiable. ConvNets are able to predict the fractions of votes given to a galaxy image with zero bias and ∼10% scatter. The fraction of mis-classifications is less than 1%. Our classification scheme represents a major improvement with respect to Concentration-Asymmetry-Smoothness-based methods, which hit a 20%–30% contamination limit at high z. The catalog is released with the present paper via the Rainbow database (http://rainbowx.fis.ucm.es/Rainbow_navigator_public/).}
}
@article{HOYLE, title = "Measuring photometric redshifts using galaxy images and Deep Neural Networks", journal = "Astronomy and Computing", volume = "16", pages = "34 - 40", year = "2016", issn = "2213-1337", doi = "https://doi.org/10.1016/j.ascom.2016.03.006", url = "http://www.sciencedirect.com/science/article/pii/S221313371630021X", author = "B. Hoyle", keywords = "Astronomy, Machine learning, Cosmology", abstract = "We propose a new method to estimate the photometric redshift of galaxies by using the full galaxy image in each measured band. This method draws from the latest techniques and advances in machine learning, in particular Deep Neural Networks. We pass the entire multi-band galaxy image into the machine learning architecture to obtain a redshift estimate that is competitive, in terms of the measured point prediction metrics, with the best existing standard machine learning techniques. The standard techniques estimate redshifts using post-processed features, such as magnitudes and colours, which are extracted from the galaxy images and are deemed to be salient by the user. This new method removes the user from the photometric redshift estimation pipeline. However we do note that Deep Neural Networks require many orders of magnitude more computing resources than standard machine learning architectures, and as such are only tractable for making predictions on datasets of size ≤50k before implementing parallelisation techniques." }
@article{brunner, author = {Kim, Edward J. and Brunner, Robert J.}, title = "{Star–galaxy classification using deep convolutional neural networks}", journal = {Monthly Notices of the Royal Astronomical Society}, volume = {464}, number = {4}, pages = {4463-4475}, year = {2016}, month = {10}, abstract = "{Most existing star–galaxy classifiers use the reduced summary information from catalogues, requiring careful feature extraction and selection. The latest advances in machine learning that use deep convolutional neural networks (ConvNets) allow a machine to automatically learn the features directly from the data, minimizing the need for input from human experts. We present a star–galaxy classification framework that uses deep ConvNets directly on the reduced, calibrated pixel values. Using data from the Sloan Digital Sky Survey and the Canada–France–Hawaii Telescope Lensing Survey, we demonstrate that ConvNets are able to produce accurate and well-calibrated probabilistic classifications that are competitive with conventional machine learning techniques. Future advances in deep learning may bring more success with current and forthcoming photometric surveys, such as the Dark Energy Survey and the Large Synoptic Survey Telescope, because deep neural networks require very little, manual feature engineering.}", issn = {0035-8711}, doi = {10.1093/mnras/stw2672}, url = {https://doi.org/10.1093/mnras/stw2672}, eprint = {https://academic.oup.com/mnras/article-pdf/464/4/4463/8313746/stw2672.pdf}, }
@inproceedings{hu2018squeeze,
title={Squeeze-and-excitation networks},
author={Hu, Jie and Shen, Li and Sun, Gang},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={7132--7141},
year={2018}
}
@article{tan2019efficientnet,
title={Efficientnet: Rethinking model scaling for convolutional neural networks},
author={Tan, Mingxing and Le, Quoc V},
journal={arXiv preprint arXiv:1905.11946},
year={2019}
}
@article{Zagoruyko_2016,
title={Wide Residual Networks},
ISBN={1901725596},
url={http://dx.doi.org/10.5244/C.30.87},
DOI={10.5244/c.30.87},
journal={Procedings of the British Machine Vision Conference 2016},
publisher={British Machine Vision Association},
author={Zagoruyko, Sergey and Komodakis, Nikos},
year={2016}
}
@article{efficientnet,
author = {Mingxing Tan and
Quoc V. Le},
title = {EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks},
journal = {CoRR},
volume = {abs/1905.11946},
year = {2019},
url = {http://arxiv.org/abs/1905.11946},
archivePrefix = {arXiv},
eprint = {1905.11946},
timestamp = {Mon, 03 Jun 2019 13:42:33 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1905-11946.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@ARTICLE{1963ApJS....8...31D,
author = {{de Vaucouleurs}, G.},
title = "{Revised Classification of 1500 Bright Galaxies.}",
journal = {\apjs},
year = 1963,
month = apr,
volume = {8},
pages = {31},
doi = {10.1086/190084},
adsurl = {https://ui.adsabs.harvard.edu/abs/1963ApJS....8...31D},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@ARTICLE{1996ApJS..107....1A,
author = {{Abraham}, Roberto G. and {van den Bergh}, Sidney and {Glazebrook}, Karl and {Ellis}, Richard S. and {Santiago}, Basilio X. and {Surma}, Peter and {Griffiths}, Richard E.},
title = "{The Morphologies of Distant Galaxies. II. Classifications from the Hubble Space Telescope Medium Deep Survey}",
journal = {\apjs},
keywords = {GALAXIES: EVOLUTION, GALAXIES: FUNDAMENTAL PARAMETERS, GALAXIES: INTERACTIONS, SURVEYS},
year = 1996,
month = nov,
volume = {107},
pages = {1},
doi = {10.1086/192352},
adsurl = {https://ui.adsabs.harvard.edu/abs/1996ApJS..107....1A},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{Conselice_2000,
title={The Asymmetry of Galaxies: Physical Morphology for Nearby and High‐Redshift Galaxies},
volume={529},
ISSN={1538-4357},
url={http://dx.doi.org/10.1086/308300},
DOI={10.1086/308300},
number={2},
journal={The Astrophysical Journal},
publisher={American Astronomical Society},
author={Conselice, Christopher J. and Bershady, Matthew A. and Jangren, Anna},
year={2000},
month={Feb},
pages={886–910}
}
@article{abraham1996b,
author = {Abraham, R. G. and Tanvir, N. R. and Santiago, B. X. and Ellis, R. S. and Glazebrook, K. and Bergh, S. van den},
title = "{Galaxy morphology to I=25 mag in the Hubble Deep Field}",
journal = {Monthly Notices of the Royal Astronomical Society},
volume = {279},
number = {3},
pages = {L47-L52},
year = {1996},
month = {04},
abstract = "{The morphological properties of galaxies in the range 21 \\< I \\< 25 mag in the Hubble Deep Field are investigated using a quantitative classification system based on measurements of the central concentration and asymmetry of galaxian light. The class distribution of objects in the Hubble Deep Field is strongly skewed towards highly asymmetric objects, relative to distributions from both the HST Medium Deep Survey at I\\<22 mag and an artificially redshifted sample of local galaxies. The steeply rising number count-magnitude relation for irregular/peculiar/merging systems at I \\< 22 mag reported by Glazebrook et al. continues to at least I = 25 mag. Although these peculiar systems are predominantly blue at optical wavelengths, a significant fraction also exhibit red U–B colours, which may indicate that they are at high redshift. Beyond Glazebrook et al.'s magnitude limit, the spiral counts appear to rise more steeply than high-normalization no-evolution predictions, whereas those of elliptical/S0 galaxies only slightly exceed such predictions and may turn over beyond I ∼ 24 mag. These results are compared with those from previous investigations of faint galaxy morphology with HST, and the possible implications are briefly discussed. The large fraction of peculiar/irregular/merging systems in the Hubble Deep Field suggests that by I ∼ 25 mag the conventional Hubble system no longer provides an adequate description of the morphological characteristics of a high fraction of field galaxies.}",
issn = {0035-8711},
doi = {10.1093/mnras/279.3.L47},
url = {https://doi.org/10.1093/mnras/279.3.L47},
eprint = {https://academic.oup.com/mnras/article-pdf/279/3/L47/2901913/279-3-L47.pdf},
}
@InProceedings{okamura1994,
author="Okamura, S.
and Doi, M.
and Fukugita, M.
and Kashikawa, N.
and Sekiguchi, M.
and Shimasaku, K.
and Yasuda, N.",
editor="MacGillivray, H. T.
and Thomson, E. B.
and Lasker, B. M.
and Reid, I. N.
and Malin, D. F.
and West, R. M.
and Lorenz, H.",
title="Automatic Morphological Classification of Galaxies",
booktitle="Astronomy from Wide-Field Imaging",
year="1994",
publisher="Springer Netherlands",
address="Dordrecht",
pages="243--247",
abstract="We study the performance and limitations of the morphological classification method based on luminosity concentration and mean surface brightness. In particular, the effects of the different colour bands and of a finite seeing are investigated.",
isbn="978-94-011-1146-1"
}
@article{Conselice_2003,
title={The Relationship between Stellar Light Distributions of Galaxies and Their Formation Histories},
volume={147},
ISSN={1538-4365},
url={http://dx.doi.org/10.1086/375001},
DOI={10.1086/375001},
number={1},
journal={The Astrophysical Journal Supplement Series},
publisher={American Astronomical Society},
author={Conselice, Christopher J.},
year={2003},
month={Jul},
pages={1–28}
}
@InProceedings{zeilar2014,
author="Zeiler, Matthew D.
and Fergus, Rob",
editor="Fleet, David
and Pajdla, Tomas
and Schiele, Bernt
and Tuytelaars, Tinne",
title="Visualizing and Understanding Convolutional Networks",
booktitle="Computer Vision -- ECCV 2014",
year="2014",
publisher="Springer International Publishing",
address="Cham",
pages="818--833",
abstract="Large Convolutional Network models have recently demonstrated impressive classification performance on the ImageNet benchmark Krizhevsky et al. [18]. However there is no clear understanding of why they perform so well, or how they might be improved. In this paper we explore both issues. We introduce a novel visualization technique that gives insight into the function of intermediate feature layers and the operation of the classifier. Used in a diagnostic role, these visualizations allow us to find model architectures that outperform Krizhevsky et al on the ImageNet classification benchmark. We also perform an ablation study to discover the performance contribution from different model layers. We show our ImageNet model generalizes well to other datasets: when the softmax classifier is retrained, it convincingly beats the current state-of-the-art results on Caltech-101 and Caltech-256 datasets.",
isbn="978-3-319-10590-1"
}