Detection of bars in galaxies using a deep convolutional neural network Article Swipe
Sheelu Abraham
,
A. K. Aniyan
,
Ajit Kembhavi
,
Ninan Sajeeth Philip
,
Kaustubh Vaghmare
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1093/mnras/sty627
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1093/mnras/sty627
We present an automated method for the detection of bar structure in optical\nimages of galaxies using a deep convolutional neural network which is easy to\nuse and provides good accuracy. In our study we use a sample of 9346 galaxies\nin the redshift range 0.009-0.2 from the Sloan Digital Sky Survey, which has\n3864 barred galaxies, the rest being unbarred. We reach a top precision of ~94\nper cent in identifying bars in galaxies using the trained network. This\naccuracy matches the accuracy reached by human experts on the same data without\nadditional information about the images. Since Deep Convolutional Neural\nNetworks can be scaled to handle large volumes of data, the method is expected\nto have great relevance in an era where astronomy data is rapidly increasing in\nterms of volume, variety, volatility and velocity along with other V's that\ncharacterize big data. With the trained model we have constructed a catalogue\nof barred galaxies from SDSS and made it available online.\n
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/mnras/sty627
- OA Status
- green
- Cited By
- 55
- References
- 58
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W2770657511Canonical identifier for this work in OpenAlex
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https://doi.org/10.1093/mnras/sty627Digital Object Identifier
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Detection of bars in galaxies using a deep convolutional neural networkWork title
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articleOpenAlex work type
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enPrimary language
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2018Year of publication
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2018-03-07Full publication date if available
- Authors
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Sheelu Abraham, A. K. Aniyan, Ajit Kembhavi, Ninan Sajeeth Philip, Kaustubh VaghmareList of authors in order
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https://doi.org/10.1093/mnras/sty627Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/1711.04573Direct OA link when available
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Physics, Convolutional neural network, Galaxy, Redshift, Astrophysics, Sky, Bar (unit), Astronomy, Artificial intelligence, Computer science, MeteorologyTop concepts (fields/topics) attached by OpenAlex
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55Total citation count in OpenAlex
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2025: 7, 2024: 9, 2023: 7, 2022: 12, 2021: 5Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W4292310804, https://openalex.org/W1982728648, https://openalex.org/W1920842539, https://openalex.org/W2098142369, https://openalex.org/W4231109964, https://openalex.org/W2581074048, https://openalex.org/W2496631665, https://openalex.org/W2044738244, https://openalex.org/W2116690804, https://openalex.org/W1979742375, https://openalex.org/W2100495367, https://openalex.org/W2136922672, https://openalex.org/W2963076183, https://openalex.org/W1569323461, https://openalex.org/W2155893237, https://openalex.org/W1970270064, https://openalex.org/W2515239931, https://openalex.org/W2110940902, https://openalex.org/W2144354855, https://openalex.org/W2919115771, https://openalex.org/W1981452559, https://openalex.org/W2150989398, https://openalex.org/W2156899877, https://openalex.org/W2063823337, https://openalex.org/W1498436455, https://openalex.org/W2021256493, https://openalex.org/W1519418465, https://openalex.org/W2029912483, https://openalex.org/W2109510044, https://openalex.org/W2034040738, https://openalex.org/W2023037133, https://openalex.org/W2157917411, https://openalex.org/W1849277567, https://openalex.org/W2289729435, https://openalex.org/W1536929369, https://openalex.org/W3144756387, https://openalex.org/W3102981450, https://openalex.org/W2799061466, https://openalex.org/W2095705004, https://openalex.org/W3100220540, https://openalex.org/W3100192982, https://openalex.org/W3106487884, https://openalex.org/W3103502977, https://openalex.org/W1911431810, https://openalex.org/W3100633974, https://openalex.org/W1853514424, https://openalex.org/W2952020226, https://openalex.org/W2413458746, https://openalex.org/W3101285323, https://openalex.org/W2142050939, https://openalex.org/W3122914799, https://openalex.org/W3121912651, https://openalex.org/W3099950419, https://openalex.org/W2949300694, https://openalex.org/W4248880341, https://openalex.org/W2914695977, https://openalex.org/W2747816630, https://openalex.org/W2755241981 |
| referenced_works_count | 58 |
| abstract_inverted_index.a | 16, 34, 59, 141 |
| abstract_inverted_index.In | 29 |
| abstract_inverted_index.We | 0, 57 |
| abstract_inverted_index.an | 2, 112 |
| abstract_inverted_index.be | 96 |
| abstract_inverted_index.by | 79 |
| abstract_inverted_index.in | 11, 65, 68, 111 |
| abstract_inverted_index.is | 22, 106, 117 |
| abstract_inverted_index.it | 149 |
| abstract_inverted_index.of | 8, 13, 36, 62, 102, 121 |
| abstract_inverted_index.on | 82 |
| abstract_inverted_index.to | 98 |
| abstract_inverted_index.we | 32, 138 |
| abstract_inverted_index.Sky | 47 |
| abstract_inverted_index.V's | 130 |
| abstract_inverted_index.and | 25, 125, 147 |
| abstract_inverted_index.bar | 9 |
| abstract_inverted_index.big | 132 |
| abstract_inverted_index.can | 95 |
| abstract_inverted_index.era | 113 |
| abstract_inverted_index.for | 5 |
| abstract_inverted_index.our | 30 |
| abstract_inverted_index.the | 6, 39, 44, 53, 71, 76, 83, 89, 104, 135 |
| abstract_inverted_index.top | 60 |
| abstract_inverted_index.use | 33 |
| abstract_inverted_index.9346 | 37 |
| abstract_inverted_index.Deep | 92 |
| abstract_inverted_index.SDSS | 146 |
| abstract_inverted_index.With | 134 |
| abstract_inverted_index.bars | 67 |
| abstract_inverted_index.cent | 64 |
| abstract_inverted_index.data | 85, 116 |
| abstract_inverted_index.deep | 17 |
| abstract_inverted_index.easy | 23 |
| abstract_inverted_index.from | 43, 145 |
| abstract_inverted_index.good | 27 |
| abstract_inverted_index.have | 108, 139 |
| abstract_inverted_index.made | 148 |
| abstract_inverted_index.rest | 54 |
| abstract_inverted_index.same | 84 |
| abstract_inverted_index.with | 128 |
| abstract_inverted_index.Since | 91 |
| abstract_inverted_index.Sloan | 45 |
| abstract_inverted_index.about | 88 |
| abstract_inverted_index.along | 127 |
| abstract_inverted_index.being | 55 |
| abstract_inverted_index.data, | 103 |
| abstract_inverted_index.data. | 133 |
| abstract_inverted_index.great | 109 |
| abstract_inverted_index.human | 80 |
| abstract_inverted_index.large | 100 |
| abstract_inverted_index.model | 137 |
| abstract_inverted_index.other | 129 |
| abstract_inverted_index.range | 41 |
| abstract_inverted_index.reach | 58 |
| abstract_inverted_index.study | 31 |
| abstract_inverted_index.using | 15, 70 |
| abstract_inverted_index.where | 114 |
| abstract_inverted_index.which | 21, 49 |
| abstract_inverted_index.barred | 51, 143 |
| abstract_inverted_index.handle | 99 |
| abstract_inverted_index.method | 4, 105 |
| abstract_inverted_index.neural | 19 |
| abstract_inverted_index.sample | 35 |
| abstract_inverted_index.scaled | 97 |
| abstract_inverted_index.Digital | 46 |
| abstract_inverted_index.Survey, | 48 |
| abstract_inverted_index.experts | 81 |
| abstract_inverted_index.images. | 90 |
| abstract_inverted_index.matches | 75 |
| abstract_inverted_index.network | 20 |
| abstract_inverted_index.present | 1 |
| abstract_inverted_index.rapidly | 118 |
| abstract_inverted_index.reached | 78 |
| abstract_inverted_index.to\nuse | 24 |
| abstract_inverted_index.trained | 72, 136 |
| abstract_inverted_index.volume, | 122 |
| abstract_inverted_index.volumes | 101 |
| abstract_inverted_index.accuracy | 77 |
| abstract_inverted_index.galaxies | 14, 69, 144 |
| abstract_inverted_index.network. | 73 |
| abstract_inverted_index.provides | 26 |
| abstract_inverted_index.redshift | 40 |
| abstract_inverted_index.variety, | 123 |
| abstract_inverted_index.velocity | 126 |
| abstract_inverted_index.~94\nper | 63 |
| abstract_inverted_index.0.009-0.2 | 42 |
| abstract_inverted_index.accuracy. | 28 |
| abstract_inverted_index.astronomy | 115 |
| abstract_inverted_index.automated | 3 |
| abstract_inverted_index.available | 150 |
| abstract_inverted_index.detection | 7 |
| abstract_inverted_index.galaxies, | 52 |
| abstract_inverted_index.has\n3864 | 50 |
| abstract_inverted_index.in\nterms | 120 |
| abstract_inverted_index.online.\n | 151 |
| abstract_inverted_index.precision | 61 |
| abstract_inverted_index.relevance | 110 |
| abstract_inverted_index.structure | 10 |
| abstract_inverted_index.unbarred. | 56 |
| abstract_inverted_index.increasing | 119 |
| abstract_inverted_index.volatility | 124 |
| abstract_inverted_index.constructed | 140 |
| abstract_inverted_index.identifying | 66 |
| abstract_inverted_index.information | 87 |
| abstract_inverted_index.expected\nto | 107 |
| abstract_inverted_index.galaxies\nin | 38 |
| abstract_inverted_index.Convolutional | 93 |
| abstract_inverted_index.catalogue\nof | 142 |
| abstract_inverted_index.convolutional | 18 |
| abstract_inverted_index.This\naccuracy | 74 |
| abstract_inverted_index.optical\nimages | 12 |
| abstract_inverted_index.Neural\nNetworks | 94 |
| abstract_inverted_index.that\ncharacterize | 131 |
| abstract_inverted_index.without\nadditional | 86 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5014711156, https://openalex.org/A5032332032, https://openalex.org/A5019444007 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I203238179, https://openalex.org/I4210107273, https://openalex.org/I59781447 |
| citation_normalized_percentile.value | 0.91779083 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |