FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3204154871> ?p ?o ?g. }

• W3204154871 endingPage "3" @default.
• W3204154871 startingPage "3" @default.
• W3204154871 abstract "The observed sub-structures, like annular gaps, in dust emissions from protoplanetary disk, are often interpreted as signatures of embedded planets. Fitting a model of planetary gaps to these observed features using customized simulations or empirical relations can reveal the characteristics of the hidden planets. However, customized fitting is often impractical owing to the increasing sample size and the complexity of disk-planet interaction. In this paper we introduce the architecture of DPNNet-2.0, second in the series after DPNNet citep{aud20}, designed using a Convolutional Neural Network ( CNN, here specifically ResNet50) for predicting exoplanet masses directly from simulated images of protoplanetary disks hosting a single planet. DPNNet-2.0 additionally consists of a multi-input framework that uses both a CNN and multi-layer perceptron (a class of artificial neural network) for processing image and disk parameters simultaneously. This enables DPNNet-2.0 to be trained using images directly, with the added option of considering disk parameters (disk viscosities, disk temperatures, disk surface density profiles, dust abundances, and particle Stokes numbers) generated from disk-planet hydrodynamic simulations as inputs. This work provides the required framework and is the first step towards the use of computer vision (implementing CNN) to directly extract mass of an exoplanet from planetary gaps observed in dust-surface density maps by telescopes such as the Atacama Large (sub-)Millimeter Array." @default.
• W3204154871 created "2021-10-11" @default.
• W3204154871 creator A5037911203 @default.
• W3204154871 creator A5040991202 @default.
• W3204154871 creator A5059147793 @default.
• W3204154871 creator A5067655724 @default.
• W3204154871 date "2021-10-01" @default.
• W3204154871 modified "2023-10-14" @default.
• W3204154871 title "DPNNet-2.0. I. Finding Hidden Planets from Simulated Images of Protoplanetary Disk Gaps" @default.
• W3204154871 cites W1817440796 @default.
• W3204154871 cites W1987345881 @default.
• W3204154871 cites W2004511524 @default.
• W3204154871 cites W2026852029 @default.
• W3204154871 cites W2042172519 @default.
• W3204154871 cites W2068433364 @default.
• W3204154871 cites W2092693142 @default.
• W3204154871 cites W2095945864 @default.
• W3204154871 cites W2102605133 @default.
• W3204154871 cites W2112796928 @default.
• W3204154871 cites W2113325037 @default.
• W3204154871 cites W2119087912 @default.
• W3204154871 cites W2130754528 @default.
• W3204154871 cites W2154790602 @default.
• W3204154871 cites W2194775991 @default.
• W3204154871 cites W2274580633 @default.
• W3204154871 cites W2295304173 @default.
• W3204154871 cites W2299265631 @default.
• W3204154871 cites W2314445977 @default.
• W3204154871 cites W2507506703 @default.
• W3204154871 cites W2528169117 @default.
• W3204154871 cites W2561714779 @default.
• W3204154871 cites W2589144420 @default.
• W3204154871 cites W2606686133 @default.
• W3204154871 cites W2618530766 @default.
• W3204154871 cites W2622826443 @default.
• W3204154871 cites W2776295956 @default.
• W3204154871 cites W2784284219 @default.
• W3204154871 cites W2791739271 @default.
• W3204154871 cites W2793745122 @default.
• W3204154871 cites W2798978245 @default.
• W3204154871 cites W2804872128 @default.
• W3204154871 cites W2883744742 @default.
• W3204154871 cites W2890900272 @default.
• W3204154871 cites W2891511481 @default.
• W3204154871 cites W2896419629 @default.
• W3204154871 cites W2896707053 @default.
• W3204154871 cites W2899525527 @default.
• W3204154871 cites W2903992149 @default.
• W3204154871 cites W2904999681 @default.
• W3204154871 cites W2909963127 @default.
• W3204154871 cites W2921041755 @default.
• W3204154871 cites W2947592794 @default.
• W3204154871 cites W2962723377 @default.
• W3204154871 cites W3004740879 @default.
• W3204154871 cites W3083524514 @default.
• W3204154871 cites W3098022595 @default.
• W3204154871 cites W3098306646 @default.
• W3204154871 cites W3098952337 @default.
• W3204154871 cites W3098974664 @default.
• W3204154871 cites W3099303292 @default.
• W3204154871 cites W3099374244 @default.
• W3204154871 cites W3099568156 @default.
• W3204154871 cites W3099714297 @default.
• W3204154871 cites W3100363814 @default.
• W3204154871 cites W3100377396 @default.
• W3204154871 cites W3100403874 @default.
• W3204154871 cites W3100736846 @default.
• W3204154871 cites W3101241441 @default.
• W3204154871 cites W3101599961 @default.
• W3204154871 cites W3102041814 @default.
• W3204154871 cites W3102581452 @default.
• W3204154871 cites W3103119880 @default.
• W3204154871 cites W3103385539 @default.
• W3204154871 cites W3103444044 @default.
• W3204154871 cites W3103506589 @default.
• W3204154871 cites W3103548402 @default.
• W3204154871 cites W3103681827 @default.
• W3204154871 cites W3103824324 @default.
• W3204154871 cites W3104099442 @default.
• W3204154871 cites W3104514191 @default.
• W3204154871 cites W3105162764 @default.
• W3204154871 cites W3105307423 @default.
• W3204154871 cites W3105476864 @default.
• W3204154871 cites W3106354804 @default.
• W3204154871 cites W3106363067 @default.
• W3204154871 cites W3121693586 @default.
• W3204154871 cites W4237151101 @default.
• W3204154871 cites W63802623 @default.
• W3204154871 cites W906783617 @default.
• W3204154871 doi "https://doi.org/10.3847/1538-4357/ac1518" @default.
• W3204154871 hasPublicationYear "2021" @default.
• W3204154871 type Work @default.
• W3204154871 sameAs 3204154871 @default.
• W3204154871 citedByCount "6" @default.
• W3204154871 countsByYear W32041548712021 @default.
• W3204154871 countsByYear W32041548712022 @default.
• W3204154871 countsByYear W32041548712023 @default.
• W3204154871 crossrefType "journal-article" @default.

• next