Companion mass limits for 17 binary systems obtained with binary differential imaging and MagAO/Clio

Pearce, Logan; Males, Jared R.; Weinberger, Alycia J.; Long, Joseph D.; Morzinski, Katie M.; Close, Laird M.; Hinz, Philip M.

doi:10.1093/mnras/stac2056

Cited by 4 publications

(12 citation statements)

References 145 publications

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“…Figure 2 shows the distribution of Gaia EDR3ʼs ruwe parameter as a function of χ 2 derived by Brandt (2021). Gaia sources with a ruwe above 1.3, which nominally delimits binary and single stars (e.g., Stassun & Torres 2021;Kervella et al 2022;Pearce et al 2022), have astrometric parameters that are impacted by orbital motion. Many of these stars also show nonlinear motion in the HGCA.…”

Section: Machine Learning On Known Accelerating Starsmentioning

confidence: 99%

“…Similarly, the median ruwe trends higher for sources with binary companions than for all GNASC stars. The presence of detectable neighbors within the projected separation of 200 au indicates that these stars tend to have higher ruwe values, toward stellar binaries (e.g., Brandt 2018;Belokurov et al 2020;Kervella et al 2022;Pearce et al 2022).…”

Section: Sources With Neighbors In Gaiamentioning

confidence: 99%

“…It includes a table of astrometric acceleration solutions for almost 340,000 sources (nss_acceleration_astro; Halbwachs et al 2022;Holl et al 2022), whose proper motion significantly changed over Gaia's first 34 months of observations. Each star on this list has a gravitating companion, either known or a potential new discovery (e.g., Kervella et al 2022;Pearce et al 2022).…”

Section: Introductionmentioning

confidence: 99%

“…For example, the EDR3 model parameter ruwe-the renormalized unit weight error-is a measure of how well a constant-proper-motion solution fits each source. When a star is measurably accelerating, this indicator is high compared to those of sources that are just drifting in the plane of the sky (Brandt 2018;Belokurov et al 2020;Kervella et al 2022;Pearce et al 2022). With a broad range of features common to nearly all EDR3 sources, we can train a machine-learning algorithm to predict whether stars are likely to be accelerating, even when they are not members of the HGCA.…”

Section: Introductionmentioning

confidence: 99%

“…The risk in applying the machine-learning code to Gaia sources when it has been trained with the HGCA is that there may not be enough information available in the Gaia data to identify sources with longer-period orbits. For example, the Gaia ruwe parameter may be low, even consistent with linear drift, if a source is accelerating slowly on a longer-period orbit (e.g., Kervella et al 2022;Pearce et al 2022). A goal here is to explore whether a combination of Gaia astrometric features, including ruwe but also the detailed errors in parallax and proper motion, along with the changes in astrometry from DR2 to EDR3, might reveal promising candidates with significant acceleration from a nearby stellar or substellar companion.…”

Section: Introductionmentioning

confidence: 99%

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A Catalog of Nearby Accelerating Star Candidates in Gaia DR3

Hill

Bromley

et al. 2023

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We describe a new catalog of accelerating star candidates with Gaia G ≤ 17.5 mag and distances d ≤ 100 pc. Designated as the Gaia Nearby Accelerating Star Catalog (GNASC), it contains 29,684 members identified using a supervised machine-learning algorithm trained on the Hipparcos–Gaia Catalog of Accelerations (HGCA), Gaia Data Release 2, and Gaia Early Data Release 3. We take advantage of the difference in observation timelines between the two Gaia catalogs and information about the quality of the astrometric modeling based on the premise that acceleration will correlate with astrometric uncertainties. Catalog membership is based on whether constant proper motion over three decades can be ruled out at high confidence (greater than 99.9%). Test data suggest that catalog members each have a 68% likelihood of true astrometric acceleration; subsets of the catalog perform even better, with the likelihood exceeding 85%. We compare the GNASC with Gaia Data Release 3 and its table of stars for which acceleration is detected at high confidence based on precise astrometric fits. Our catalog, derived without this information, captures over 96% of the sources in the table that meet our selection criteria. In addition, the GNASC contains bright, nearby candidates that were not in the original Hipparcos survey, including members of known binary systems as well as stars with companions yet to be identified. It thus extends the HGCA and demonstrates the potential of the machine-learning approach for discovering hidden partners of nearby stars in future astrometric surveys.

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Section: Machine Learning On Known Accelerating Starsmentioning

confidence: 99%

Section: Sources With Neighbors In Gaiamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Catalog of Nearby Accelerating Star Candidates in Gaia DR3

Hill

Bromley

et al. 2023

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HIP 67506 C: MagAO-X confirmation of a new low-mass stellar companion to HIP 67506 A

Pearce

Males

Haffert

et al. 2023

Monthly Notices of the Royal Astronomical Society

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We report the confirmation of HIP 67506 C, a new stellar companion to HIP 67506 A. We previously reported a candidate signal at 2λ/D (240 mas) in L′ in MagAO/Clio imaging using the binary differential imaging technique. Several additional indirect signals showed that the candidate signal merited follow-up: significant astrometric acceleration in Gaia DR3, Hipparcos-Gaia proper motion anomaly, and overluminosity compared to single main sequence stars. We confirmed the companion, HIP 67506 C, at 0.1” with MagAO-X in April, 2022. We characterized HIP 67506 C MagAO-X photometry and astrometry, and estimated spectral type K7-M2; we also re-evaluated HIP 67506 A in light of the close companion. Additionally we show that a previously identified 9” companion, HIP 67506 B, is a much further distant unassociated background star. We also discuss the utility of indirect signposts in identifying small inner working angle candidate companions.

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Efficiently Searching for Close-in Companions Around Young M Dwarfs Using a Multiyear PSF Library

Sanghi,

Xuan,

Wang

et al. 2024

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We present Super-RDI, a unique framework for the application of reference star differential imaging (RDI) to Keck/NIRC2 high-contrast imaging observations with the vortex coronagraph. Super-RDI combines frame selection and signal-to-noise ratio (S/N) optimization techniques with a large multiyear reference point-spread function (PSF) library to achieve optimal PSF subtraction at small angular separations. We compile an ∼7000 frame reference PSF library based on a set of 288 new Keck/NIRC2 L ′ sequences of 237 unique targets acquired between 2015 and 2019 as part of two planet-search programs designed for RDI, one focusing on nearby young M dwarfs and the other targeting members of the Taurus star-forming region. For our data set, synthetic companion injection-recovery tests reveal that frame selection with the mean-squared error metric combined with Karhunen–Loève Image-Processing-based PSF subtraction using 1000–3000 frames and ≲500 principal components yields the highest average S/N for injected synthetic companions. We uniformly reduce targets in the young M-star survey with both Super-RDI and angular differential imaging (ADI). For the typical parallactic angle rotation of our data set (∼10°), Super-RDI performs better than a widely used implementation of ADI-based PSF subtraction at separations ≲0.″4 (≈5 λ/D), gaining an average of 0.25 mag in contrast at 0.″25 and 0.4 mag in contrast at 0.″15. This represents a performance improvement in separation space over RDI with single-night reference star observations (∼100 frame PSF libraries) applied to a similar Keck/NIRC2 data set in previous work. We recover two known brown dwarf companions and provide detection limits for 155 targets in the young M-star survey. Our results demonstrate that increasing the PSF library size with careful selection of reference frames can improve the performance of RDI with the Keck/NIRC2 vortex coronagraph in L ′ .

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Companion mass limits for 17 binary systems obtained with binary differential imaging and MagAO/Clio

Cited by 4 publications

References 145 publications

A Catalog of Nearby Accelerating Star Candidates in Gaia DR3

A Catalog of Nearby Accelerating Star Candidates in Gaia DR3

HIP 67506 C: MagAO-X confirmation of a new low-mass stellar companion to HIP 67506 A

Efficiently Searching for Close-in Companions Around Young M Dwarfs Using a Multiyear PSF Library

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