Uniform Forward-modeling Analysis of Ultracool Dwarfs. II. Atmospheric Properties of 55 Late-T Dwarfs

Zhang, Zhoujian; Liu, Mengmeng; Marley, Mark S.; Line, Michael R.; Best, William M. J.

doi:10.3847/1538-4357/ac0af7

Cited by 25 publications

(52 citation statements)

References 203 publications

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“…Independent of the controversy surrounding the mechanism behind the L-T transition, the spectra of brown dwarfs as measured by ground-based telescopes are an excellent training ground for atmospheric retrieval, as they are of a comparable quality to future spectra of exoplanets obtained using the James Webb Space Telescope (JWST). Atmospheric retrieval provides a complementary approach to the traditional one of analyzing brown dwarf spectra using precomputed grids of atmospheric models (e.g., Marley et al 1996;Burrows et al 1997;Chabrier et al 2000;Ackerman & Marley 2001;Allard et al 2001;Baraffe et al 2002;Burrows et al 2003Burrows et al , 2011Morley et al 2014;Zhang et al 2021aZhang et al , 2021b.…”

Section: Introductionmentioning

confidence: 99%

Retrieval Study of Brown Dwarfs across the L-T Sequence

Lueber

Kitzmann

Bowler

et al. 2022

ApJ

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A large suite of 228 atmospheric retrievals is performed on a curated sample of 19 brown dwarfs spanning the L0–T8 spectral types using the open-source Helios-r2 retrieval code, which implements the method of short characteristics for radiative transfer and a finite-element description of the temperature–pressure profile. Surprisingly, we find that cloud-free and cloudy (both gray and nongray) models are equally consistent with the archival SpeX data from the perspective of Bayesian model comparison. Only upper limits for cloud properties are inferred if log-uniform priors are assumed, but the cloud optical depth becomes constrained if a uniform prior is used. Water is detected in all 19 objects, and methane is detected in all of the T dwarfs, but no obvious trend exists across effective temperature. As carbon monoxide is only detected in a handful of objects, the inferred carbon-to-oxygen ratios are unreliable. The retrieved radius generally decreases with effective temperature, but the values inferred for some T dwarfs are implausibly low and may indicate missing physics or chemistry in the models. For the early L dwarfs, the retrieved surface gravity depends on whether the gray-cloud or non-gray-cloud model is preferred. Future data are necessary for constraining cloud properties and the vertical variation of chemical abundances, the latter of which is needed for distinguishing between the chemical instability and traditional cloud interpretation of the L-T transition.

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Section: Introductionmentioning

confidence: 99%

Retrieval Study of Brown Dwarfs across the L-T Sequence

Lueber

Kitzmann

Bowler

et al. 2022

ApJ

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“…is classified as a T7.0 brown dwarf(Kirkpatrick et al 2011). Using a forward modeling analysis,Zhang et al (2021) measured an effective temperature of =…”

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confidence: 99%

Informed Systematic Method to Identify Variable Mid- and Late-T Dwarfs

Natalia

Manjavacas

Ashraf

et al. 2022

ApJ

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The majority of brown dwarfs show some level of photometric or spectrophotometric variability in different wavelength ranges. This variability allow us to trace the 3D atmospheric structures of variable brown dwarfs and directly-imaged exoplanets with radiative-transfer models and mapping codes. Nevertheless, to date, we do not have an informed method to preselect the brown dwarfs that might show a higher variability amplitude for a thorough variability study. In this work, we designed and tested near-infrared spectral indices to preselect the most likely variable mid- and late-T dwarfs, which overlap in effective temperatures with directly-imaged exoplanets. We used time-resolved near-infrared Hubble Space Telescope Wide Field Camera 3 spectra of a T6.5 dwarf, 2MASS J22282889–431026, to design our novel spectral indices. We tested these spectral indices on 26 T5.5–T7.5 near-infrared SpeX/IRTF spectra, and we provided eight new mid- and late-T variable candidates. We estimated the variability fraction of our sample as 38 − 30 + 4 %, which agrees with the variability fractions provided by Metchev for mid- to late-T dwarfs. In addition, two of the three previously known variables in our sample of SpeX spectra are flagged as variable candidates by our indices. Similarly, all seven known nonvariables in our sample are flagged as nonvariable objects by our indices. These results suggest that our spectral indices might be used to find variable mid- and late-T brown dwarf variables. These indices may be crucial in the future to select cool directly-imaged exoplanets for variability studies.

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“…Starfish pioneered the use of whole-spectrum fitting with resilience to model imperfections by addressing the problem of what to do when the underlying atomic and molecular data were wrong or approximate or missing. It has been extended to inferring starspot physical properties (Gully-Santiago et al 2017), measuring veiling in Class 0 protostars (Greene et al 2018), and quantifying imperfections in brown dwarf models (Zhang et al 2021). The Spectral Inference Crank (sick; Casey 2016) shares similar aims to starfish and provides additional useful grid search capabilities.…”

Section: Discussionmentioning

confidence: 99%

An Interpretable Machine-learning Framework for Modeling High-resolution Spectroscopic Data*

Gully-Santiago

Morley

2022

ApJ

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Comparison of échelle spectra to synthetic models has become a computational statistics challenge, with over 10,000 individual spectral lines affecting a typical cool star échelle spectrum. Telluric artifacts, imperfect line lists, inexact continuum placement, and inflexible models frustrate the scientific promise of these information-rich data sets. Here we debut an interpretable machine-learning framework blasé that addresses these and other challenges. The semiempirical approach can be viewed as “transfer learning”—first pretraining models on noise-free precomputed synthetic spectral models, then learning the corrections to line depths and widths from whole-spectrum fitting to an observed spectrum. The auto-differentiable model employs back-propagation, the fundamental algorithm empowering modern deep learning and neural networks. Here, however, the 40,000+ parameters symbolize physically interpretable line profile properties such as amplitude, width, location, and shape, plus radial velocity and rotational broadening. This hybrid data-/model-driven framework allows joint modeling of stellar and telluric lines simultaneously, a potentially transformative step forward for mitigating the deleterious telluric contamination in the near-infrared. The blasé approach acts as both a deconvolution tool and semiempirical model. The general-purpose scaffolding may be extensible to many scientific applications, including precision radial velocities, Doppler imaging, chemical abundances for Galactic archeology, line veiling, magnetic fields, and remote sensing. Its sparse-matrix architecture and GPU acceleration make blasé fast. The open-source PyTorch-based code blase includes tutorials, Application Programming Interface documentation, and more. We show how the tool fits into the existing Python spectroscopy ecosystem, demonstrate a range of astrophysical applications, and discuss limitations and future extensions.

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Uniform Forward-modeling Analysis of Ultracool Dwarfs. II. Atmospheric Properties of 55 Late-T Dwarfs

Cited by 25 publications

References 203 publications

Retrieval Study of Brown Dwarfs across the L-T Sequence

Retrieval Study of Brown Dwarfs across the L-T Sequence

Informed Systematic Method to Identify Variable Mid- and Late-T Dwarfs

An Interpretable Machine-learning Framework for Modeling High-resolution Spectroscopic Data*

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