English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Detection of exomoons in simulated light curves with a regularized convolutional neural network

Alshehhi, R., Rodenbeck, K., Gizon, L., & Sreenivasan, K. R. (2020). Detection of exomoons in simulated light curves with a regularized convolutional neural network. Astronomy and Astrophysics, 640: A41. doi:10.1051/0004-6361/201937059.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/21.11116/0000-0006-F962-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0006-F963-2
Genre: Journal Article

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Alshehhi, Rasha, Author
Rodenbeck, Kai1, Author              
Gizon, Laurent1, Author              
Sreenivasan, Katepalli R., Author
Affiliations:
1Department Solar and Stellar Interiors, Max Planck Institute for Solar System Research, Max Planck Society, ou_1832287              

Content

show
hide
Free keywords: -
 Abstract: Context. Many moons have been detected around planets in our Solar System, but none has been detected unambiguously around any of the confirmed extrasolar planets. Aims. We test the feasibility of a supervised convolutional neural network to classify photometric transit light curves of planet-host stars and identify exomoon transits, while avoiding false positives caused by stellar variability or instrumental noise. Methods. Convolutional neural networks are known to have contributed to improving the accuracy of classification tasks. The network optimization is typically performed without studying the effect of noise on the training process. Here we design and optimize a 1D convolutional neural network to classify photometric transit light curves. We regularize the network by the total variation loss in order to remove unwanted variations in the data features. Results. Using numerical experiments, we demonstrate the benefits of our network, which produces results comparable to or better than the standard network solutions. Most importantly, our network clearly outperforms a classical method used in exoplanet science to identify moon-like signals. Thus the proposed network is a promising approach for analyzing real transit light curves in the future.

Details

show
hide
Language(s): eng - English
 Dates: 2020
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1051/0004-6361/201937059
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Astronomy and Astrophysics
  Other : Astron. Astrophys.
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Les Ulis Cedex A France : EDP Sciences
Pages: 9 Volume / Issue: 640 Sequence Number: A41 Start / End Page: - Identifier: ISSN: 1432-0746
ISSN: 0004-6361
CoNE: https://pure.mpg.de/cone/journals/resource/954922828219_1