Deep Orbital Search for Additional Planets in the HR 8799 System

Thompson, William; Marois, Christian; Ó, Clarissa R.; Konopacky, Quinn; Ruffio, Jean-Baptiste; Wang, Jason; Skemer, Andrew J.; Rosa, Robert J. De; Macintosh, Bruce

doi:10.3847/1538-3881/aca1af

Cited by 9 publications

(11 citation statements)

References 86 publications

(123 reference statements)

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“…Directly modeling point sources in images is one of the key features of Octofitter. This is accomplished with the same approach described in Ruffio et al (2018) and applied in Mawet et al (2019), Llop-Sayson et al (2021), and Thompson et al (2023. In the case where a planet is robustly detected in each image and the uncertainties are well-approximated by Gaussian uncertainties, it is mathematically equivalent to extracting the relative astrometry and photometry and then modeling those measurements.…”

Section: Imagesmentioning

confidence: 99%

“…These include a search for planets around Sirius B (Skemer & Close 2011), K-Stacker (Nowak et al 2018;Le Coroller et al 2020), PACOME (J. Dallant et al 2023, submitted), the search for planets around ε Eri (Mawet et al 2019;Llop-Sayson et al 2021), and the search for additional HR 8799 planets by Thompson et al (2023). These have now led to promising evidence for α Cen AB b (Le Coroller et al 2022) and HR 8799 f (Thompson et al 2023).…”

Section: Introductionmentioning

confidence: 99%

“…Dallant et al 2023, submitted), the search for planets around ε Eri (Mawet et al 2019;Llop-Sayson et al 2021), and the search for additional HR 8799 planets by Thompson et al (2023). These have now led to promising evidence for α Cen AB b (Le Coroller et al 2022) and HR 8799 f (Thompson et al 2023). Moving the analysis of direct imaging data into the orbital domain enables a Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence.…”

Section: Introductionmentioning

confidence: 99%

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Octofitter: Fast, Flexible, and Accurate Orbit Modeling to Detect Exoplanets

Thompson,

Lawrence,

Blakely

et al. 2023

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As next-generation imaging instruments and interferometers search for planets closer to their stars, they must contend with increasing orbital motion and longer integration times. These compounding effects make it difficult to detect faint planets but also present an opportunity. Increased orbital motion makes it possible to move the search for planets into the orbital domain, where direct images can be freely combined with the radial velocity and proper motion anomaly, even without a confirmed detection in any single epoch. In this paper, we present a fast and differentiable multimethod orbit-modeling and planet detection code called Octofitter. This code is designed to be highly modular and allows users to easily adjust priors, change parameterizations, and specify arbitrary function relations between the parameters of one or more planets. Octofitter further supplies tools for examining model outputs including prior and posterior predictive checks and simulation-based calibration. We demonstrate the capabilities of Octofitter on real and simulated data from different instruments and methods, including HD 91312, simulated JWST/NIRISS aperture masking interferometry observations, radial velocity curves, and grids of images from the Gemini Planet Imager. We show that Octofitter can reliably recover faint planets in long sequences of images with arbitrary orbital motion. This publicly available tool will enable the broad application of multiepoch and multimethod exoplanet detection, which could improve how future targeted ground- and space-based surveys are performed. Finally, its rapid convergence makes it a useful addition to the existing ecosystem of tools for modeling the orbits of directly imaged planets.

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Section: Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Octofitter: Fast, Flexible, and Accurate Orbit Modeling to Detect Exoplanets

Thompson,

Lawrence,

Blakely

et al. 2023

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“…Dots show the experimental data for 3 × 3, 7 × 7, and 15 × 15 pixels, in pink, blue, and red, respectively. (6)] by considering all the potential correlations within the sliding window. In this case, the correction factors are K s ð3 2 ; 1Þ ¼ 0.7073 and K s ð7 2 ; 3Þ ¼ 0.8198, respectively.…”

Section: Experimental Validation Of Proposed Modelmentioning

confidence: 99%

Improved spatial speckle contrast model for tissue blood flow imaging: effects of spatial correlation among neighboring camera pixels

Juarez-Ramirez,

Coyotl-Ocelotl,

Choi

et al. 2023

J. Biomed. Opt.

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Speckle contrast analysis is the basis of laser speckle imaging (LSI), a simple, inexpensive, noninvasive technique used in various fields of medicine and engineering. A common application of LSI is the measurement of tissue blood flow. Accurate measurement of speckle contrast is essential to correctly measure blood flow. Variables, such as speckle grain size and camera pixel size, affect the speckle pattern and thus the speckle contrast.Aim: We studied the effects of spatial correlation among adjacent camera pixels on the resulting speckle contrast values.Approach: We derived a model that accounts for the potential correlation of intensity values in the common experimental situation where the speckle grain size is larger than the camera pixel size. In vitro phantom experiments were performed to test the model. Results: Our spatial correlation model predicts that speckle contrast first increases, then decreases as the speckle grain size increases relative to the pixel size. This decreasing trend opposes what is observed with a standard speckle contrast model that does not consider spatial correlation. Experimental data are in good agreement with the predictions of our spatial correlation model. Conclusions:We present a spatial correlation model that provides a more accurate measurement of speckle contrast, which should lead to improved accuracy in tissue blood flow measurements. The associated correlation factors only need to be calculated once, and open-source software is provided to assist with the calculation.

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“…They generally assume distributions that are uniform (for the eccentricity, argument of periastron, and period of ascending nodes), log-uniform (semimajor axis) and sine (inclination). It is also common practice to narrow down the parameter space with physically motivated priors, such as stability constraints, coplanarity and stellar rotation rates (e.g., Pearce et al 2015;Wang et al 2018;Wang et al 2020;Thompson et al 2023;Yimiao Zhang et al 2023). However, for orbits where less than 40% of the orbital arc is covered, which is the majority of directly imaged companions, this standard method of model-based priors (which herein we will refer to as "uniform" priors, as is done by O'Neil et al 2019) has been shown to introduce biases in the resulting posterior distributions, generating inaccurate parameters and confidence intervals (Lucy 2014;Martinez et al 2017).…”

Section: Introductionmentioning

confidence: 99%

The Orbital Eccentricities of Directly Imaged Companions Using Observable-based Priors: Implications for Population-level Distributions

O'Neil

Konopacky

et al. 2023

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The eccentricity of a substellar companion is an important tracer of its formation history. Directly imaged companions often present poorly constrained eccentricities. A recently developed prior framework for orbit fitting called “observable-based priors” has the advantage of improving biases in derived orbit parameters for objects with minimal phase coverage, which is the case for the majority of directly imaged companions. We use observable-based priors to fit the orbits of 21 exoplanets and brown dwarfs in an effort to obtain the eccentricity distributions with minimized biases. We present the objects’ individual posteriors compared to their previously derived distributions, showing in many cases a shift toward lower eccentricities. We analyze the companions’ eccentricity distribution at a population level, and compare this to the distributions obtained with the traditional uniform priors. We fit a Beta distribution to our posteriors using observable-based priors, obtaining shape parameters α = 1.09 − 0.22 + 0.30 and β = 1.42 − 0.25 + 0.33 . This represents an approximately flat distribution of eccentricities. The derived α and β parameters are consistent with the values obtained using uniform priors, though uniform priors lead to a tail at high eccentricities. We find that separating the population into high- and low-mass companions yields different distributions depending on the classification of intermediate-mass objects. We also determine via simulation that the minimal orbit coverage needed to give meaningful posteriors under the assumptions made for directly imaged planets is ≈15% of the inferred period of the orbit.

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Deep Orbital Search for Additional Planets in the HR 8799 System

Cited by 9 publications

References 86 publications

Octofitter: Fast, Flexible, and Accurate Orbit Modeling to Detect Exoplanets

Octofitter: Fast, Flexible, and Accurate Orbit Modeling to Detect Exoplanets

Improved spatial speckle contrast model for tissue blood flow imaging: effects of spatial correlation among neighboring camera pixels

The Orbital Eccentricities of Directly Imaged Companions Using Observable-based Priors: Implications for Population-level Distributions

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