Using Bayesian Deep Learning to Infer Planet Mass from Gaps in Protoplanetary Disks Article Swipe
Sayantan Auddy
,
Ramit Dey
,
Min-Kai Lin
,
Daniel Carrera
,
Jacob B. Simon
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3847/1538-4357/ac7a3c
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3847/1538-4357/ac7a3c
Planet-induced substructures, like annular gaps, observed in dust emission from protoplanetary disks, provide a unique probe for characterizing unseen young planets. While deep-learning-based models have an edge in characterizing a planet’s properties over traditional methods, such as customized simulations and empirical relations, they lacks the ability to quantify the uncertainties associated with their predictions. In this paper, we introduce a Bayesian deep-learning network, “DPNNet-Bayesian,” which can predict planet mass from disk gaps and also provides the uncertainties associated with the prediction. A unique feature of our approach is that it is able to distinguish between the uncertainty associated with the deep-learning architecture and the uncertainty inherent in the input data due to measurement noise. The model is trained on a data set generated from disk–planet simulations using the fargo3d hydrodynamics code, with a newly implemented fixed grain size module and improved initial conditions. The Bayesian framework enables the estimation of a gauge/confidence interval over the validity of the prediction, when applied to unknown observations. As a proof of concept, we apply DPNNet-Bayesian to the dust gaps observed in HL Tau. The network predicts masses of 86.0 ± 5.5 M ⊕ , 43.8 ± 3.3 M ⊕ , and 92.2 ± 5.1 M ⊕ , respectively, which are comparable to those from other studies based on specialized simulations.
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- Type
- article
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- en
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Using Bayesian Deep Learning to Infer Planet Mass from Gaps in Protoplanetary DisksWork title
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articleOpenAlex work type
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enPrimary language
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2022Year of publication
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2022-09-01Full publication date if available
- Authors
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Sayantan Auddy, Ramit Dey, Min-Kai Lin, Daniel Carrera, Jacob B. SimonList of authors in order
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https://doi.org/10.3847/1538-4357/ac7a3cPublisher landing page
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goldOpen access status per OpenAlex
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https://doi.org/10.3847/1538-4357/ac7a3cDirect OA link when available
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Planet, Physics, Bayesian probability, Astrophysics, Bayesian network, Artificial intelligence, Deep learning, Algorithm, Machine learning, Computer scienceTop concepts (fields/topics) attached by OpenAlex
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7Total citation count in OpenAlex
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2025: 2, 2024: 2, 2023: 2, 2022: 1Per-year citation counts (last 5 years)
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| referenced_works | https://openalex.org/W3102100346, https://openalex.org/W1920023268, https://openalex.org/W2314445977, https://openalex.org/W3185064977, https://openalex.org/W3082619320, https://openalex.org/W2901969800, https://openalex.org/W2269830133, https://openalex.org/W2225156818, https://openalex.org/W2095945864, https://openalex.org/W1987345881, https://openalex.org/W1609435114, https://openalex.org/W1817440796, https://openalex.org/W2962723377, https://openalex.org/W2026852029, https://openalex.org/W4292406582, https://openalex.org/W2084622335, https://openalex.org/W2947114586, https://openalex.org/W2782884703, https://openalex.org/W2904999681, https://openalex.org/W3014596384, https://openalex.org/W3083524514, https://openalex.org/W63802623, https://openalex.org/W2106556001, https://openalex.org/W2223572600, https://openalex.org/W3100736846, https://openalex.org/W2299265631, https://openalex.org/W2088024896, https://openalex.org/W2810503551, https://openalex.org/W2798978245, https://openalex.org/W2885274016, https://openalex.org/W2921041755, https://openalex.org/W2116952160, https://openalex.org/W4210818292, https://openalex.org/W2091724524, https://openalex.org/W2751448052, https://openalex.org/W2804790504, https://openalex.org/W2955061178, https://openalex.org/W3001752242, https://openalex.org/W2065532082, https://openalex.org/W2942231644, https://openalex.org/W3087681349, https://openalex.org/W2804872128, https://openalex.org/W2035547408, https://openalex.org/W2883744742, https://openalex.org/W2068433364, https://openalex.org/W2962851448, https://openalex.org/W4205823607, https://openalex.org/W2905495549, https://openalex.org/W3101309688, https://openalex.org/W3101670442, https://openalex.org/W3105162764, https://openalex.org/W3204154871, https://openalex.org/W3100377396, https://openalex.org/W3103119880, https://openalex.org/W3099926431, https://openalex.org/W3101599961, https://openalex.org/W3098952337, https://openalex.org/W3105476864, https://openalex.org/W3102714277, https://openalex.org/W3098022595, https://openalex.org/W3098725059, https://openalex.org/W3134774296, https://openalex.org/W3103385539, https://openalex.org/W3105179610, https://openalex.org/W3101790398, https://openalex.org/W3102518771, https://openalex.org/W3164731060, https://openalex.org/W3103506589, https://openalex.org/W3103681827 |
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| abstract_inverted_index.M | 189, 195, 202 |
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| abstract_inverted_index.3.3 | 194 |
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| corresponding_author_ids | https://openalex.org/A5040991202 |
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| corresponding_institution_ids | https://openalex.org/I173911158 |
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