The Spatial Distribution of Globular Cluster Systems in Early Type Galaxies: Estimation Procedure and Catalog of Properties for Globular Cluster Systems Observed with Deep Imaging Surveys Article Swipe
YOU?
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· 2024
· Open Access
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· DOI: https://doi.org/10.48550/arxiv.2411.17049
We present an analysis of the spatial distribution of globular cluster (GC) systems of 118 nearby early-type galaxies in the Next Generation Virgo Cluster Survey (NGVS) and Mass Assembly of early-Type GaLAxies with their fine Structures (MATLAS) survey programs, which both used MegaCam on the Canada-France-Hawaii Telescope. We describe the procedure used to select GC candidates and fit the spatial distributions of GCs to a two-dimensional Sérsic function, which provides effective radii (half number radii) and Sérsic indices, and estimate background contamination by adding a constant term to the S'ersic function. In cases where a neighboring galaxy affects the estimation of the GC spatial distribution in the target galaxy, we fit two 2D Sérsic functions, simultaneously. We also investigate the color distributions of GCs in our sample by using Gaussian Mixture Modeling. For GC systems with bimodal color distributions, we divide the GCs into blue and red subgroups and fit their respective spatial distributions with Sérsic functions. Finally, we measure the total number of GCs based on our fitted Sérsic function, and calculate the GC specific frequency.
Related Topics
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2411.17049
- https://arxiv.org/pdf/2411.17049
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404988444
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4404988444Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2411.17049Digital Object Identifier
- Title
-
The Spatial Distribution of Globular Cluster Systems in Early Type Galaxies: Estimation Procedure and Catalog of Properties for Globular Cluster Systems Observed with Deep Imaging SurveysWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-11-26Full publication date if available
- Authors
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Sungsoon Lim, Eric W. Peng, Patrick Côté, Laura Ferrarese, Joel Roediger, Chengze Liu, Chelsea Spengler, Elisabeth Sola, Pierre–Alain Duc, Laura V. Sales, John P. Blakeslee, Jean‐Charles Cuillandre, Patrick R. Durrell, Éric Emsellem, Stephen Gwyn, A. Lançon, F. Marleau, J. Christopher Mihos, Oliver Müller, Thomas H. Puzia, Rubén Sánchez-JanssenList of authors in order
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https://arxiv.org/abs/2411.17049Publisher landing page
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https://arxiv.org/pdf/2411.17049Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2411.17049Direct OA link when available
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Globular cluster, Cluster (spacecraft), Galaxy, Astrophysics, Spatial distribution, Physics, Computer science, Geology, Remote sensing, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.GCs | 62, 123, 142, 164 |
| abstract_inverted_index.and | 26, 56, 75, 78, 145, 148, 171 |
| abstract_inverted_index.fit | 57, 110, 149 |
| abstract_inverted_index.our | 125, 167 |
| abstract_inverted_index.red | 146 |
| abstract_inverted_index.the | 5, 19, 44, 49, 58, 88, 98, 101, 106, 119, 141, 160, 173 |
| abstract_inverted_index.two | 111 |
| abstract_inverted_index.(GC) | 11 |
| abstract_inverted_index.Mass | 27 |
| abstract_inverted_index.Next | 20 |
| abstract_inverted_index.also | 117 |
| abstract_inverted_index.blue | 144 |
| abstract_inverted_index.both | 40 |
| abstract_inverted_index.fine | 34 |
| abstract_inverted_index.into | 143 |
| abstract_inverted_index.term | 86 |
| abstract_inverted_index.used | 41, 51 |
| abstract_inverted_index.with | 32, 135, 154 |
| abstract_inverted_index.(half | 72 |
| abstract_inverted_index.Virgo | 22 |
| abstract_inverted_index.based | 165 |
| abstract_inverted_index.cases | 92 |
| abstract_inverted_index.color | 120, 137 |
| abstract_inverted_index.radii | 71 |
| abstract_inverted_index.their | 33, 150 |
| abstract_inverted_index.total | 161 |
| abstract_inverted_index.using | 128 |
| abstract_inverted_index.where | 93 |
| abstract_inverted_index.which | 39, 68 |
| abstract_inverted_index.(NGVS) | 25 |
| abstract_inverted_index.Survey | 24 |
| abstract_inverted_index.adding | 83 |
| abstract_inverted_index.divide | 140 |
| abstract_inverted_index.fitted | 168 |
| abstract_inverted_index.galaxy | 96 |
| abstract_inverted_index.nearby | 15 |
| abstract_inverted_index.number | 73, 162 |
| abstract_inverted_index.radii) | 74 |
| abstract_inverted_index.sample | 126 |
| abstract_inverted_index.select | 53 |
| abstract_inverted_index.survey | 37 |
| abstract_inverted_index.target | 107 |
| abstract_inverted_index.Cluster | 23 |
| abstract_inverted_index.MegaCam | 42 |
| abstract_inverted_index.Mixture | 130 |
| abstract_inverted_index.S'ersic | 89 |
| abstract_inverted_index.Sérsic | 66, 76, 113, 155, 169 |
| abstract_inverted_index.affects | 97 |
| abstract_inverted_index.bimodal | 136 |
| abstract_inverted_index.cluster | 10 |
| abstract_inverted_index.galaxy, | 108 |
| abstract_inverted_index.measure | 159 |
| abstract_inverted_index.present | 1 |
| abstract_inverted_index.spatial | 6, 59, 103, 152 |
| abstract_inverted_index.systems | 12, 134 |
| abstract_inverted_index.(MATLAS) | 36 |
| abstract_inverted_index.Assembly | 28 |
| abstract_inverted_index.Finally, | 157 |
| abstract_inverted_index.GaLAxies | 31 |
| abstract_inverted_index.Gaussian | 129 |
| abstract_inverted_index.analysis | 3 |
| abstract_inverted_index.constant | 85 |
| abstract_inverted_index.describe | 48 |
| abstract_inverted_index.estimate | 79 |
| abstract_inverted_index.galaxies | 17 |
| abstract_inverted_index.globular | 9 |
| abstract_inverted_index.indices, | 77 |
| abstract_inverted_index.provides | 69 |
| abstract_inverted_index.specific | 175 |
| abstract_inverted_index.Modeling. | 131 |
| abstract_inverted_index.calculate | 172 |
| abstract_inverted_index.effective | 70 |
| abstract_inverted_index.function, | 67, 170 |
| abstract_inverted_index.function. | 90 |
| abstract_inverted_index.procedure | 50 |
| abstract_inverted_index.programs, | 38 |
| abstract_inverted_index.subgroups | 147 |
| abstract_inverted_index.Generation | 21 |
| abstract_inverted_index.Structures | 35 |
| abstract_inverted_index.Telescope. | 46 |
| abstract_inverted_index.background | 80 |
| abstract_inverted_index.candidates | 55 |
| abstract_inverted_index.early-Type | 30 |
| abstract_inverted_index.early-type | 16 |
| abstract_inverted_index.estimation | 99 |
| abstract_inverted_index.frequency. | 176 |
| abstract_inverted_index.functions, | 114 |
| abstract_inverted_index.functions. | 156 |
| abstract_inverted_index.respective | 151 |
| abstract_inverted_index.investigate | 118 |
| abstract_inverted_index.neighboring | 95 |
| abstract_inverted_index.distribution | 7, 104 |
| abstract_inverted_index.contamination | 81 |
| abstract_inverted_index.distributions | 60, 121, 153 |
| abstract_inverted_index.distributions, | 138 |
| abstract_inverted_index.simultaneously. | 115 |
| abstract_inverted_index.two-dimensional | 65 |
| abstract_inverted_index.Canada-France-Hawaii | 45 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 21 |
| citation_normalized_percentile |