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%% This BibTeX bibliography file was created using BibDesk.
%% https://bibdesk.sourceforge.io/
%% Created for Valentin Christiaens at 2022-07-21 21:58:36 +0200
%% Saved with string encoding Unicode (UTF-8)
@inproceedings{Burgasser:2017,
abstract = {I describe our team's development of the SpeX Prism Library Analysis Toolkit (SPLAT), a combined spectral data repository for over 2500 low-resolution spectra of very low-mass dwarfs (late M, L and T dwarfs), and a Python-based analysis toolkit. SPLAT was constructed through a collaborative, student-centred, research-based model with high school, undergraduate and graduate students and regional K-12 science teachers. The toolkit enables spectral index analysis, classification, spectrophotometry, atmosphere model comparisons, population synthesis, and other analyses. I summarise the current components of this code, sample applications, and future development plans.},
adsnote = {Provided by the SAO/NASA Astrophysics Data System},
adsurl = {https://ui.adsabs.harvard.edu/abs/2017ASInC..14....7B},
archiveprefix = {arXiv},
author = {{Burgasser}, A.~J. and {Splat Development Team}},
booktitle = {Astronomical Society of India Conference Series},
date-added = {2022-07-21 21:58:31 +0200},
date-modified = {2022-07-21 21:58:35 +0200},
eprint = {1707.00062},
keywords = {astronomical data bases: miscellaneous, stars: low-mass, brown dwarfs, techniques: spectroscopic, Astrophysics - Solar and Stellar Astrophysics},
month = jan,
pages = {7-12},
primaryclass = {astro-ph.SR},
series = {Astronomical Society of India Conference Series},
title = {{The SpeX Prism Library Analysis Toolkit (SPLAT): A Data Curation Model}},
volume = {14},
year = 2017,
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bdsk-url-1 = {https://ui.adsabs.harvard.edu/abs/2017ASInC..14....7B},
bdsk-url-2 = {https://ui.adsabs.harvard.edu/link_gateway/2017ASInC..14....7B/EPRINT_HTML}}
@article{Delorme:2017,
adsnote = {Provided by the SAO/NASA Astrophysics Data System},
adsurl = {http://cdsads.u-strasbg.fr/abs/2017A%26A...608A..79D},
archiveprefix = {arXiv},
author = {{Delorme}, P. and {Schmidt}, T. and {Bonnefoy}, M. and {Desidera}, S. and {Ginski}, C. and {Charnay}, B. and {Lazzoni}, C. and {Christiaens}, V. and {Messina}, S. and {D'Orazi}, V. and {Milli}, J. and {Schlieder}, J.~E. and {Gratton}, R. and {Rodet}, L. and {Lagrange}, A.-M. and {Absil}, O. and {Vigan}, A. and {Galicher}, R. and {Hagelberg}, J. and {Bonavita}, M. and {Lavie}, B. and {Zurlo}, A. and {Olofsson}, J. and {Boccaletti}, A. and {Cantalloube}, F. and {Mouillet}, D. and {Chauvin}, G. and {Hambsch}, F.-J. and {Langlois}, M. and {Udry}, S. and {Henning}, T. and {Beuzit}, J.-L. and {Mordasini}, C. and {Lucas}, P. and {Marocco}, F. and {Biller}, B. and {Carson}, J. and {Cheetham}, A. and {Covino}, E. and {De Caprio}, V. and {Delboulbe}, A. and {Feldt}, M. and {Girard}, J. and {Hubin}, N. and {Maire}, A.-L. and {Pavlov}, A. and {Petit}, C. and {Rouan}, D. and {Roelfsema}, R. and {Wildi}, F.},
date-added = {2022-05-24 18:24:08 +0200},
date-modified = {2022-05-24 18:24:13 +0200},
doi = {10.1051/0004-6361/201731145},
eid = {A79},
eprint = {1709.00349},
journal = {Astronomy and Astrophysics},
keywords = {brown dwarfs, planets and satellites: atmospheres, techniques: high angular resolution, planet-disk interactions},
month = dec,
note = {mypaper},
pages = {A79},
primaryclass = {astro-ph.SR},
title = {{In-depth study of moderately young but extremely red, very dusty substellar companion HD 206893B}},
volume = {608},
year = {2017},
bdsk-url-1 = {http://dx.doi.org/10.1051/0004-6361/201731145}}
@article{Mukherjee:2006,
abstract = {The abundance of cosmological data becoming available means that a wider range of cosmological models are testable than ever before. However, an important distinction must be made between parameter fitting and model selection. While parameter fitting simply determines how well a model fits the data, model selection statistics, such as the Bayesian evidence, are now necessary to choose between these different models, and in particular to assess the need for new parameters. We implement a new evidence algorithm known as nested sampling, which combines accuracy, generality of application, and computational feasibility, and we apply it to some cosmological data sets and models. We find that a five-parameter model with a Harrison-Zel'dovich initial spectrum is currently preferred.},
adsnote = {Provided by the SAO/NASA Astrophysics Data System},
adsurl = {https://ui.adsabs.harvard.edu/abs/2006ApJ...638L..51M},
archiveprefix = {arXiv},
author = {{Mukherjee}, Pia and {Parkinson}, David and {Liddle}, Andrew R.},
date-added = {2022-05-19 16:30:24 +0200},
date-modified = {2022-05-19 16:30:29 +0200},
doi = {10.1086/501068},
eprint = {astro-ph/0508461},
journal = {Astrophysical Journal Letters},
keywords = {Cosmology: Theory, Astrophysics},
month = feb,
number = {2},
pages = {L51-L54},
primaryclass = {astro-ph},
title = {{A Nested Sampling Algorithm for Cosmological Model Selection}},
volume = {638},
year = 2006,
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bdsk-url-1 = {https://doi.org/10.1086/501068},
bdsk-url-2 = {https://ui.adsabs.harvard.edu/abs/2006ApJ...638L..51M},
bdsk-url-3 = {https://ui.adsabs.harvard.edu/link_gateway/2006ApJ...638L..51M/EPRINT_HTML}}
@article{Feroz:2009,
abstract = {We present further development and the first public release of our multimodal nested sampling algorithm, called MULTINEST. This Bayesian inference tool calculates the evidence, with an associated error estimate, and produces posterior samples from distributions that may contain multiple modes and pronounced (curving) degeneracies in high dimensions. The developments presented here lead to further substantial improvements in sampling efficiency and robustness, as compared to the original algorithm presented in Feroz & Hobson, which itself significantly outperformed existing Markov chain Monte Carlo techniques in a wide range of astrophysical inference problems. The accuracy and economy of the MULTINEST algorithm are demonstrated by application to two toy problems and to a cosmological inference problem focusing on the extension of the vanilla Λ cold dark matter model to include spatial curvature and a varying equation of state for dark energy. The MULTINEST software, which is fully parallelized using MPI and includes an interface to COSMOMC, is available at http://www.mrao.cam.ac.uk/software/multinest/. It will also be released as part of the SUPERBAYES package, for the analysis of supersymmetric theories of particle physics, at http://www.superbayes.org.},
adsnote = {Provided by the SAO/NASA Astrophysics Data System},
adsurl = {https://ui.adsabs.harvard.edu/abs/2009MNRAS.398.1601F},
archiveprefix = {arXiv},
author = {{Feroz}, F. and {Hobson}, M.~P. and {Bridges}, M.},
date-added = {2022-05-19 16:09:02 +0200},
date-modified = {2022-05-19 16:09:06 +0200},
doi = {10.1111/j.1365-2966.2009.14548.x},
eprint = {0809.3437},
journal = {Monthly Notices of the RAS},
keywords = {methods: data analysis, methods: statistical, Astrophysics},
month = oct,
number = {4},
pages = {1601-1614},
primaryclass = {astro-ph},
title = {{MULTINEST: an efficient and robust Bayesian inference tool for cosmology and particle physics}},
volume = {398},
year = 2009,
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bdsk-url-1 = {https://doi.org/10.1111/j.1365-2966.2009.14548.x},
bdsk-url-2 = {https://ui.adsabs.harvard.edu/abs/2009MNRAS.398.1601F},
bdsk-url-3 = {https://ui.adsabs.harvard.edu/link_gateway/2009MNRAS.398.1601F/EPRINT_HTML}}
@misc{nestle,
author = {K. Barbary},
date-modified = {2022-05-24 18:24:35 +0200},
journal = {GitHub repository},
publisher = {GitHub},
title = {{\texttt{nestle}}},
url = {https://github.com/kbarbary/nestle},
year = {2013},
bdsk-url-1 = {https://github.com/kbarbary/nestle}}
@article{Buchner:2021b,
abstract = {UltraNest is a general-purpose Bayesian inference package for parameter estimation and model comparison. It allows fitting arbitrary models specified as likelihood functions written in Python, C, C++, Fortran, Julia or R. With a focus on correctness and speed (in that order), UltraNest is especially useful for multi-modal or non-Gaussian parameter spaces, computational expensive models, in robust pipelines. Parallelisation to computing clusters and resuming incomplete runs is available.},
adsnote = {Provided by the SAO/NASA Astrophysics Data System},
adsurl = {https://ui.adsabs.harvard.edu/abs/2021JOSS....6.3001B},
archiveprefix = {arXiv},
author = {{Buchner}, Johannes},
date-added = {2022-05-19 15:59:32 +0200},
date-modified = {2022-05-19 15:59:40 +0200},
doi = {10.21105/joss.03001},
eid = {3001},
eprint = {2101.09604},
journal = {The Journal of Open Source Software},
keywords = {C, Monte Carlo, Python, Nested Sampling, C++, Bayesian inference, Fortran, Bayes factors, Statistics - Computation, Astrophysics - Instrumentation and Methods for Astrophysics},
month = apr,
number = {60},
pages = {3001},
primaryclass = {stat.CO},
title = {{UltraNest - a robust, general purpose Bayesian inference engine}},
volume = {6},
year = 2021,
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bdsk-url-1 = {https://doi.org/10.21105/joss.03001},
bdsk-url-2 = {https://ui.adsabs.harvard.edu/abs/2021JOSS....6.3001B},
bdsk-url-3 = {https://ui.adsabs.harvard.edu/link_gateway/2021JOSS....6.3001B/EPRINT_HTML}}
@article{Buchner:2021a,
abstract = {Nested sampling (NS) computes parameter posterior distributions and makes Bayesian model comparison computationally feasible. Its strengths are the unsupervised navigation of complex, potentially multi-modal posteriors until a well-defined termination point. A systematic literature review of nested sampling algorithms and variants is presented. We focus on complete algorithms, including solutions to likelihood-restricted prior sampling, parallelisation, termination and diagnostics. The relation between number of live points, dimensionality and computational cost is studied for two complete algorithms. A new formulation of NS is presented, which casts the parameter space exploration as a search on a tree. Previously published ways of obtaining robust error estimates and dynamic variations of the number of live points are presented as special cases of this formulation. A new on-line diagnostic test is presented based on previous insertion rank order work. The survey of nested sampling methods concludes with outlooks for future research.},
adsnote = {Provided by the SAO/NASA Astrophysics Data System},
adsurl = {https://ui.adsabs.harvard.edu/abs/2021arXiv210109675B},
archiveprefix = {arXiv},
author = {{Buchner}, Johannes},
date-added = {2022-05-19 15:59:02 +0200},
date-modified = {2022-05-19 15:59:28 +0200},
eid = {arXiv:2101.09675},
eprint = {2101.09675},
journal = {arXiv e-prints},
keywords = {Statistics - Computation, Astrophysics - Instrumentation and Methods for Astrophysics},
month = jan,
pages = {arXiv:2101.09675},
primaryclass = {stat.CO},
title = {{Nested Sampling Methods}},
year = 2021,
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bdsk-url-1 = {https://ui.adsabs.harvard.edu/abs/2021arXiv210109675B},
bdsk-url-2 = {https://ui.adsabs.harvard.edu/link_gateway/2021arXiv210109675B/EPRINT_HTML}}
@article{Goodman:2010,
abstract = {We propose a family of Markov chain Monte Carlo methods whose performance is unaffected by affine tranformations of space. These algorithms are easy to construct and require little or no additional computational overhead. They should be particularly useful for sampling badly scaled distributions. Computational tests show that the affine invariant methods can be significantly faster than standard MCMC methods on highly skewed distributions. <P />},
adsnote = {Provided by the SAO/NASA Astrophysics Data System},
adsurl = {https://ui.adsabs.harvard.edu/abs/2010CAMCS...5...65G},
author = {{Goodman}, Jonathan and {Weare}, Jonathan},
date-added = {2022-05-19 15:57:24 +0200},
date-modified = {2022-05-19 15:57:29 +0200},
doi = {10.2140/camcos.2010.5.65},
journal = {Communications in Applied Mathematics and Computational Science},
keywords = {Markov chain Monte Carlo, affine invariance, ensemble samplers},
month = jan,
number = {1},
pages = {65-80},
title = {{Ensemble samplers with affine invariance}},
volume = {5},
year = 2010,
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bdsk-url-1 = {https://doi.org/10.2140/camcos.2010.5.65},
bdsk-url-2 = {https://ui.adsabs.harvard.edu/abs/2010CAMCS...5...65G}}
@inproceedings{Skilling:2004,
abstract = {"The evidence Z is often the single most important number in the [Bayesian] problem and I think every effort should be devoted to calculating it" (MacKay 2003). Nested sampling does this by giving a direct estimate of the density of states. Posterior samples are an optional by-product.},
adsnote = {Provided by the SAO/NASA Astrophysics Data System},
adsurl = {https://ui.adsabs.harvard.edu/abs/2004AIPC..735..395S},
author = {{Skilling}, John},
booktitle = {Bayesian Inference and Maximum Entropy Methods in Science and Engineering: 24th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering},
date-added = {2022-05-19 14:08:04 +0200},
date-modified = {2022-05-19 14:08:09 +0200},
doi = {10.1063/1.1835238},
editor = {{Fischer}, Rainer and {Preuss}, Roland and {Toussaint}, Udo Von},
keywords = {02.50.Tt, Inference methods},
month = nov,
pages = {395-405},
series = {American Institute of Physics Conference Series},
title = {{Nested Sampling}},
volume = {735},
year = 2004,
bdsk-url-1 = {https://doi.org/10.1063/1.1835238},
bdsk-url-2 = {https://ui.adsabs.harvard.edu/abs/2004AIPC..735..395S}}
@article{Greco:2016,
adsnote = {Provided by the SAO/NASA Astrophysics Data System},
adsurl = {http://cdsads.u-strasbg.fr/abs/2016ApJ...833..134G},
archiveprefix = {arXiv},
author = {{Greco}, J.~P. and {Brandt}, T.~D.},
date-added = {2022-05-17 14:40:00 +0200},
date-modified = {2022-05-17 14:40:06 +0200},
doi = {10.3847/1538-4357/833/2/134},
eid = {134},
eprint = {1602.00691},
journal = {Astrophysical Journal},
keywords = {methods: data analysis, planetary systems, techniques: imaging spectroscopy},
month = dec,
pages = {134},
primaryclass = {astro-ph.EP},
title = {{The Measurement, Treatment, and Impact of Spectral Covariance and Bayesian Priors in Integral-field Spectroscopy of Exoplanets}},
volume = {833},
year = {2016},
bdsk-url-1 = {http://dx.doi.org/10.3847/1538-4357/833/2/134}}
@article{Foreman-Mackey:2019,
abstract = {emcee is a Python library implementing a class of affine-invariant ensemble samplers for Markov chain Monte Carlo (MCMC). This package has been widely applied to probabilistic modeling problems in astrophysics where it was originally published, with some applications in other fields. When it was first released in 2012, the interface implemented in emcee was fundamentally different from the MCMC libraries that were popular at the time, such as PyMC, because it was specifically designed to work with "black box" models instead of structured graphical models. This has been a popular interface for applications in astrophysics because it is often non-trivial to implement realistic physics within the modeling frameworks required by other libraries. Since emcee's release, other libraries have been developed with similar interfaces, such as dynesty (Speagle 2019). The version 3.0 release of emcee is the first major release of the library in about 6 years and it includes a full re-write of the computational backend, several commonly requested features, and a set of new "move" implementations.},
adsnote = {Provided by the SAO/NASA Astrophysics Data System},
adsurl = {https://ui.adsabs.harvard.edu/abs/2019JOSS....4.1864F},
archiveprefix = {arXiv},
author = {{Foreman-Mackey}, Daniel and {Farr}, Will and {Sinha}, Manodeep and {Archibald}, Anne and {Hogg}, David and {Sanders}, Jeremy and {Zuntz}, Joe and {Williams}, Peter and {Nelson}, Andrew and {de Val-Borro}, Miguel and {Erhardt}, Tobias and {Pashchenko}, Ilya and {Pla}, Oriol},
date-added = {2022-05-17 14:37:31 +0200},
date-modified = {2022-05-17 14:37:48 +0200},
doi = {10.21105/joss.01864},
eid = {1864},
eprint = {1911.07688},
journal = {The Journal of Open Source Software},
keywords = {Python, astronomy, Astrophysics - Instrumentation and Methods for Astrophysics, Statistics - Computation},
month = nov,
number = {43},
pages = {1864},
primaryclass = {astro-ph.IM},
title = {{emcee v3: A Python ensemble sampling toolkit for affine-invariant MCMC}},
volume = {4},
year = 2019,
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bdsk-url-1 = {https://doi.org/10.21105/joss.01864},
bdsk-url-2 = {https://ui.adsabs.harvard.edu/abs/2019JOSS....4.1864F}}
@article{Foreman-Mackey:2013,
abstract = {We introduce a stable, well tested Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010). The code is open source and has already been used in several published projects in the astrophysics literature. The algorithm behind emcee has several advantages over traditional MCMC sampling methods and it has excellent performance as measured by the autocorrelation time (or function calls per independent sample). One major advantage of the algorithm is that it requires hand-tuning of only 1 or 2 parameters compared to ∼N<SUP>2</SUP> for a traditional algorithm in an N-dimensional parameter space. In this document, we describe the algorithm and the details of our implementation. Exploiting the parallelism of the ensemble method, emcee permits any user to take advantage of multiple CPU cores without extra effort. The code is available online at <A href="http://dan.iel.fm/emcee">http://dan.iel.fm/emcee</A> under the GNU General Public License v2.},
adsnote = {Provided by the SAO/NASA Astrophysics Data System},
adsurl = {https://ui.adsabs.harvard.edu/abs/2013PASP..125..306F},
archiveprefix = {arXiv},
author = {{Foreman-Mackey}, Daniel and {Hogg}, David W. and {Lang}, Dustin and {Goodman}, Jonathan},
date-added = {2022-05-17 14:37:26 +0200},
date-modified = {2022-05-17 14:37:43 +0200},
doi = {10.1086/670067},
eprint = {1202.3665},
journal = {Publications of the ASP},
keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Physics - Computational Physics, Statistics - Computation},
month = mar,
number = {925},
pages = {306},
primaryclass = {astro-ph.IM},
title = {{emcee: The MCMC Hammer}},
volume = {125},
year = 2013,
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bdsk-url-1 = {https://doi.org/10.1086/670067},
bdsk-url-2 = {https://ui.adsabs.harvard.edu/abs/2013PASP..125..306F}}
@article{Slesnick:2004,
adsnote = {Provided by the SAO/NASA Astrophysics Data System},
adsurl = {http://cdsads.u-strasbg.fr/abs/2004ApJ...610.1045S},
author = {{Slesnick}, C.~L. and {Hillenbrand}, L.~A. and {Carpenter}, J.~M.},
date-added = {2022-05-17 14:37:15 +0200},
date-modified = {2022-05-17 14:38:13 +0200},
doi = {10.1086/421898},
eprint = {astro-ph/0404292},
journal = {Astrophysical Journal},
keywords = {Infrared: Stars, open clusters and associations: individual (Orion Nebula cluster), Stars: Low-Mass, Brown Dwarfs, Stars: Luminosity Function, Mass Function, Stars: Pre-Main-Sequence},
month = aug,
pages = {1045-1063},
title = {{The Spectroscopically Determined Substellar Mass Function of the Orion Nebula Cluster}},
volume = {610},
year = {2004},
bdsk-url-1 = {http://dx.doi.org/10.1086/421898}}
@article{Gorlova:2003,
adsnote = {Provided by the SAO/NASA Astrophysics Data System},
adsurl = {http://cdsads.u-strasbg.fr/abs/2003ApJ...593.1074G},
author = {{Gorlova}, N.~I. and {Meyer}, M.~R. and {Rieke}, G.~H. and {Liebert}, J.},
date-added = {2022-05-17 14:37:10 +0200},
date-modified = {2022-05-17 14:38:06 +0200},
doi = {10.1086/376730},
eprint = {astro-ph/0305147},
journal = {Astrophysical Journal},
keywords = {Infrared: Stars, Stars: Atmospheres, Stars: Fundamental Parameters, Stars: Low-Mass, Brown Dwarfs},
month = aug,
pages = {1074-1092},
title = {{Gravity Indicators in the Near-Infrared Spectra of Brown Dwarfs}},
volume = {593},
year = {2003},
bdsk-url-1 = {http://dx.doi.org/10.1086/376730}}
@article{Allers:2007,
abstract = {We present near-infrared (1.0-2.4 μm) spectra confirming the youth and cool temperatures of six brown dwarfs and low-mass stars with circumstellar disks toward the Chamaeleon II and Ophiuchus star-forming regions. The spectrum of one of our objects indicates a spectral type of ~L1, making it one of the latest spectral type young brown dwarfs identified to date. Comparing spectra of young brown dwarfs, field dwarfs, and giant stars, we define a 1.49-1.56 μm H<SUB>2</SUB>O index capable of determining spectral type to +/-1 subtype, independent of gravity. We have also defined an index based on the 1.14 μm sodium feature that is sensitive to gravity, but only weakly dependent on spectral type. Our 1.14 μm Na index can be used to distinguish young cluster members (τ<~5 Myr) from young field dwarfs, both of which may have the triangular H-band continuum shape that persists for at least tens of Myr. Using T<SUB>eff</SUB> values determined from the spectral types of our objects along with luminosities derived from near and mid-infrared photometry, we place our objects on the H-R diagram and overlay evolutionary models to estimate the masses and ages of our young sources. Three of our sources have inferred ages (τ~=10-30 Myr) that are significantly older than the median stellar age of their parent clouds (1-3 Myr). For these three objects, we derive masses ~3 times greater than expected for 1-3 Myr old brown dwarfs with the bolometric luminosities of our sources. The large discrepancies in the inferred masses and ages determined using two separate, yet reasonable, methods emphasize the need for caution when deriving or exploiting brown dwarf mass and age estimates.},
adsnote = {Provided by the SAO/NASA Astrophysics Data System},
adsurl = {https://ui.adsabs.harvard.edu/abs/2007ApJ...657..511A},
archiveprefix = {arXiv},
author = {{Allers}, K.~N. and {Jaffe}, D.~T. and {Luhman}, K.~L. and {Liu}, Michael C. and {Wilson}, J.~C. and {Skrutskie}, M.~F. and {Nelson}, M. and {Peterson}, D.~E. and {Smith}, J.~D. and {Cushing}, M.~C.},
date-added = {2022-05-17 14:37:06 +0200},
date-modified = {2022-05-17 14:37:38 +0200},
doi = {10.1086/510845},
eprint = {astro-ph/0611408},
journal = {Astrophysical Journal},
keywords = {Infrared: Stars, Stars: Formation, Stars: Low-Mass, Brown Dwarfs, Astrophysics},
month = mar,
number = {1},
pages = {511-520},
primaryclass = {astro-ph},
title = {{Characterizing Young Brown Dwarfs Using Low-Resolution Near-Infrared Spectra}},
volume = {657},
year = 2007,
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bdsk-url-1 = {https://doi.org/10.1086/510845},
bdsk-url-2 = {https://ui.adsabs.harvard.edu/abs/2007ApJ...657..511A}}
@article{Christiaens:2021,
archiveprefix = {arXiv},
author = {{Christiaens}, V. and {Ubeira-Gabellini}, M. -G. and {C{\'a}novas}, H. and {Delorme}, P. and {Pairet}, B. and {Absil}, O. and {Casassus}, S. and {Girard}, J.~H. and {Zurlo}, A. and {Aoyama}, Y. and {Marleau}, G. -D. and {Spina}, L. and {van der Marel}, N. and {Cieza}, L. and {Lodato}, G. and {P{\'e}rez}, S. and {Pinte}, C. and {Price}, D.~J. and {Reggiani}, M.},
doi = {10.1093/mnras/stab480},
eprint = {2102.10288},
journal = {Monthly Notices of the RAS},
keywords = {techniques: image processing, planets and satellites: formation, planet-disc interactions, protoplanetary discs, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Solar and Stellar Astrophysics},
month = apr,
number = {4},
pages = {6117-6139},
primaryclass = {astro-ph.EP},
title = {{A faint companion around CrA-9: protoplanet or obscured binary?}},
url = {https://ui.adsabs.harvard.edu/abs/2021MNRAS.502.6117C},
volume = {502},
year = 2021,
bdsk-url-1 = {https://ui.adsabs.harvard.edu/abs/2021MNRAS.502.6117C},
bdsk-url-2 = {https://doi.org/10.1093/mnras/stab480}}