Predicting Galactic OH Masers from Dense Clump Properties with Neural Networks and Generalized Linear Models Article Swipe
Dmitry A. Ladeyschikov
,
Elena A. Filonova
,
Anton I. Vasyunin
,
Dmitry A. Ladeyschikov
,
Elena A. Filonova
,
Anton I. Vasyunin
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/galaxies13060130
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/galaxies13060130
We develop predictive models for OH maser occurrence in Galactic star-forming regions by integrating dense-clump physical properties from the APEX Telescope Large Area Survey of the Galaxy (ATLASGAL) and Herschel Infrared Galactic Plane Survey (Hi-GAL) 360° catalogs with maser detections and non-detections compiled in the MaserDB.net database. We compare two predictive modeling approaches for Galactic OH maser incidence: a Generalized Linear Model (GLM; logistic regression) and a compact Keras-based binary neural network (BNN). For the 1665/1667 MHz lines, both models achieve recall of 90% with a precision of approximately 50%, while for the excited-state 6031/6035 MHz lines, precision reaches roughly 20% at the same recall. We found no statistically significant difference between the BNN and GLM in out-of-sample performance. This implies that maser occurrence may be expressed as a monotonic trend without requiring nonlinear interactions. Across different catalogs and transition lines, luminosity, luminosity-to-mass ratio (L/M), dust temperature, and H2 column, surface, and volume densities are the most influential features for maser prediction. These variables support a physical picture in which radiative pumping favors warm, luminous, and compact clump environments. We provide an accessible online tool that allows users to predict the likelihood of OH maser emission toward ATLASGAL or Hi-GAL sources based on coordinate lists.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/galaxies13060130
- https://www.mdpi.com/2075-4434/13/6/130/pdf?version=1764158879
- OA Status
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- References
- 41
- OpenAlex ID
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Predicting Galactic OH Masers from Dense Clump Properties with Neural Networks and Generalized Linear ModelsWork title
- Type
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-11-26Full publication date if available
- Authors
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Dmitry A. Ladeyschikov, Elena A. Filonova, Anton I. Vasyunin, Dmitry A. Ladeyschikov, Elena A. Filonova, Anton I. VasyuninList of authors in order
- Landing page
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https://doi.org/10.3390/galaxies13060130Publisher landing page
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https://www.mdpi.com/2075-4434/13/6/130/pdf?version=1764158879Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2075-4434/13/6/130/pdf?version=1764158879Direct OA link when available
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Maser, Physics, Astrophysics, Galaxy, Megamaser, Radiative transfer, Galactic plane, Artificial neural network, Astronomy, Binary number, Nonlinear system, Linear regression, Linear model, Parameter space, Redshift, Plane (geometry), Statistical physics, TelescopeTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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| abstract_inverted_index.at | 101 |
| abstract_inverted_index.be | 125 |
| abstract_inverted_index.by | 12 |
| abstract_inverted_index.in | 8, 43, 116, 168 |
| abstract_inverted_index.no | 107 |
| abstract_inverted_index.of | 24, 82, 87, 192 |
| abstract_inverted_index.on | 202 |
| abstract_inverted_index.or | 198 |
| abstract_inverted_index.to | 188 |
| abstract_inverted_index.20% | 100 |
| abstract_inverted_index.90% | 83 |
| abstract_inverted_index.BNN | 113 |
| abstract_inverted_index.For | 73 |
| abstract_inverted_index.GLM | 115 |
| abstract_inverted_index.MHz | 76, 95 |
| abstract_inverted_index.and | 28, 40, 65, 114, 138, 147, 151, 175 |
| abstract_inverted_index.are | 154 |
| abstract_inverted_index.for | 4, 53, 91, 159 |
| abstract_inverted_index.may | 124 |
| abstract_inverted_index.the | 18, 25, 44, 74, 92, 102, 112, 155, 190 |
| abstract_inverted_index.two | 49 |
| abstract_inverted_index.50%, | 89 |
| abstract_inverted_index.APEX | 19 |
| abstract_inverted_index.Area | 22 |
| abstract_inverted_index.This | 119 |
| abstract_inverted_index.both | 78 |
| abstract_inverted_index.dust | 145 |
| abstract_inverted_index.from | 17 |
| abstract_inverted_index.most | 156 |
| abstract_inverted_index.same | 103 |
| abstract_inverted_index.that | 121, 185 |
| abstract_inverted_index.tool | 184 |
| abstract_inverted_index.with | 37, 84 |
| abstract_inverted_index.(GLM; | 62 |
| abstract_inverted_index.360° | 35 |
| abstract_inverted_index.Large | 21 |
| abstract_inverted_index.Model | 61 |
| abstract_inverted_index.Plane | 32 |
| abstract_inverted_index.These | 162 |
| abstract_inverted_index.based | 201 |
| abstract_inverted_index.clump | 177 |
| abstract_inverted_index.found | 106 |
| abstract_inverted_index.maser | 6, 38, 56, 122, 160, 194 |
| abstract_inverted_index.ratio | 143 |
| abstract_inverted_index.trend | 130 |
| abstract_inverted_index.users | 187 |
| abstract_inverted_index.warm, | 173 |
| abstract_inverted_index.which | 169 |
| abstract_inverted_index.while | 90 |
| abstract_inverted_index.(BNN). | 72 |
| abstract_inverted_index.(L/M), | 144 |
| abstract_inverted_index.Across | 135 |
| abstract_inverted_index.Galaxy | 26 |
| abstract_inverted_index.Hi-GAL | 199 |
| abstract_inverted_index.Linear | 60 |
| abstract_inverted_index.Survey | 23, 33 |
| abstract_inverted_index.allows | 186 |
| abstract_inverted_index.binary | 69 |
| abstract_inverted_index.favors | 172 |
| abstract_inverted_index.lines, | 77, 96, 140 |
| abstract_inverted_index.lists. | 204 |
| abstract_inverted_index.models | 3, 79 |
| abstract_inverted_index.neural | 70 |
| abstract_inverted_index.online | 183 |
| abstract_inverted_index.recall | 81 |
| abstract_inverted_index.toward | 196 |
| abstract_inverted_index.volume | 152 |
| abstract_inverted_index.achieve | 80 |
| abstract_inverted_index.between | 111 |
| abstract_inverted_index.column, | 149 |
| abstract_inverted_index.compact | 67, 176 |
| abstract_inverted_index.compare | 48 |
| abstract_inverted_index.develop | 1 |
| abstract_inverted_index.implies | 120 |
| abstract_inverted_index.network | 71 |
| abstract_inverted_index.picture | 167 |
| abstract_inverted_index.predict | 189 |
| abstract_inverted_index.provide | 180 |
| abstract_inverted_index.pumping | 171 |
| abstract_inverted_index.reaches | 98 |
| abstract_inverted_index.recall. | 104 |
| abstract_inverted_index.regions | 11 |
| abstract_inverted_index.roughly | 99 |
| abstract_inverted_index.sources | 200 |
| abstract_inverted_index.support | 164 |
| abstract_inverted_index.without | 131 |
| abstract_inverted_index.(Hi-GAL) | 34 |
| abstract_inverted_index.ATLASGAL | 197 |
| abstract_inverted_index.Galactic | 9, 31, 54 |
| abstract_inverted_index.Herschel | 29 |
| abstract_inverted_index.Infrared | 30 |
| abstract_inverted_index.catalogs | 36, 137 |
| abstract_inverted_index.compiled | 42 |
| abstract_inverted_index.emission | 195 |
| abstract_inverted_index.features | 158 |
| abstract_inverted_index.logistic | 63 |
| abstract_inverted_index.modeling | 51 |
| abstract_inverted_index.physical | 15, 166 |
| abstract_inverted_index.surface, | 150 |
| abstract_inverted_index.1665/1667 | 75 |
| abstract_inverted_index.6031/6035 | 94 |
| abstract_inverted_index.Telescope | 20 |
| abstract_inverted_index.database. | 46 |
| abstract_inverted_index.densities | 153 |
| abstract_inverted_index.different | 136 |
| abstract_inverted_index.expressed | 126 |
| abstract_inverted_index.luminous, | 174 |
| abstract_inverted_index.monotonic | 129 |
| abstract_inverted_index.nonlinear | 133 |
| abstract_inverted_index.precision | 86, 97 |
| abstract_inverted_index.radiative | 170 |
| abstract_inverted_index.requiring | 132 |
| abstract_inverted_index.variables | 163 |
| abstract_inverted_index.(ATLASGAL) | 27 |
| abstract_inverted_index.accessible | 182 |
| abstract_inverted_index.approaches | 52 |
| abstract_inverted_index.coordinate | 203 |
| abstract_inverted_index.detections | 39 |
| abstract_inverted_index.difference | 110 |
| abstract_inverted_index.incidence: | 57 |
| abstract_inverted_index.likelihood | 191 |
| abstract_inverted_index.occurrence | 7, 123 |
| abstract_inverted_index.predictive | 2, 50 |
| abstract_inverted_index.properties | 16 |
| abstract_inverted_index.transition | 139 |
| abstract_inverted_index.Generalized | 59 |
| abstract_inverted_index.Keras-based | 68 |
| abstract_inverted_index.MaserDB.net | 45 |
| abstract_inverted_index.dense-clump | 14 |
| abstract_inverted_index.influential | 157 |
| abstract_inverted_index.integrating | 13 |
| abstract_inverted_index.luminosity, | 141 |
| abstract_inverted_index.prediction. | 161 |
| abstract_inverted_index.regression) | 64 |
| abstract_inverted_index.significant | 109 |
| abstract_inverted_index.performance. | 118 |
| abstract_inverted_index.star-forming | 10 |
| abstract_inverted_index.temperature, | 146 |
| abstract_inverted_index.approximately | 88 |
| abstract_inverted_index.environments. | 178 |
| abstract_inverted_index.excited-state | 93 |
| abstract_inverted_index.interactions. | 134 |
| abstract_inverted_index.out-of-sample | 117 |
| abstract_inverted_index.statistically | 108 |
| abstract_inverted_index.non-detections | 41 |
| abstract_inverted_index.luminosity-to-mass | 142 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |