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PlanetEvidence: Planet or Noise?

Golomb, Jacob and Rocha, Graça and Meshkat, Tiffany and Bottom, Michael and Mawet, Dimitri and Mennesson, Bertrand and Vasisht, Gautam and Wang, Jason (2021) PlanetEvidence: Planet or Noise? Astronomical Journal, 162 (6). Art. No. 304. ISSN 0004-6256. doi:10.3847/1538-3881/ac174e. https://resolver.caltech.edu/CaltechAUTHORS:20200309-112437574

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Abstract

The work presented here attempts at answering the following question: how do we decide when a given detection is a planet or just residual noise in exoplanet direct imaging data? To this end we implement a metric meant to replace the empirical frequentist-based thresholds for detection. Our method, implemented within a Bayesian framework, introduces an “evidence-based” approach to help decide whether a given detection is a true planet or just noise. We apply this metric jointly with a postprocessing technique and Karhunen–Loeve Image Processing (KLIP), which models and subtracts the stellar PSF from the image. As a proof of concept we implemented a new routine named PlanetEvidence that integrates the nested sampling technique (Multinest) with the KLIP algorithm. This is a first step to recast such a postprocessing method into a fully Bayesian perspective. We test our approach on real direct imaging data, specifically using GPI data of β Pictoris b, and on synthetic data. We find that for the former the method strongly favors the presence of a planet (as expected) and recovers the true parameter posterior distributions. For the latter case our approach allows us to detect (true) dim sources invisible to the naked eye as real planets, rather than background noise, and set a new lower threshold for detection at ∼2.5σ level. Further it allows us to quantify our confidence that a given detection is a real planet and not just residual noise.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.3847/1538-3881/ac174eDOIArticle
https://arxiv.org/abs/1912.01232arXivDiscussion Paper
ORCID:
AuthorORCID
Golomb, Jacob0000-0002-6977-670X
Rocha, Graça0000-0002-4150-8076
Meshkat, Tiffany0000-0001-6126-2467
Bottom, Michael0000-0003-1341-5531
Mawet, Dimitri0000-0002-8895-4735
Mennesson, Bertrand0000-0003-4205-4800
Vasisht, Gautam0000-0002-1871-6264
Wang, Jason0000-0003-0774-6502
Additional Information:© 2021. The American Astronomical Society. Received 2019 November 27; revised 2021 June 1; accepted 2021 June 21; published 2021 December 9. PlanetEvidence is implemented in pyKLIP and is run in conjunction with KLIP-FM. G.R. would like to acknowledge useful discussions with Jeff Jewell. The research presented here was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.
Group:Astronomy Department
Funders:
Funding AgencyGrant Number
NASA/JPL/CaltechUNSPECIFIED
Subject Keywords:Exoplanet detection methods; Direct imaging; Bayesian statistics; Exoplanets
Issue or Number:6
Classification Code:Unified Astronomy Thesaurus concepts: Exoplanet detection methods (489); Direct imaging (387); Bayesian statistics (1900); Exoplanets (498)
DOI:10.3847/1538-3881/ac174e
Record Number:CaltechAUTHORS:20200309-112437574
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200309-112437574
Official Citation:Jacob Golomb et al 2021 AJ 162 304
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:101780
Collection:CaltechAUTHORS
Deposited By: Joy Painter
Deposited On:09 Mar 2020 22:53
Last Modified:16 Dec 2021 19:00

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