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Results for “pre-main sequence”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2021

Tidal Circularization of Binaries by Resonance Locking. I. The Importance of the Pre-main Sequence

Although tidal dissipation in binary stars has been studied for over a century, theoretical predictions have yet to match the observed properties of binary populations. This work quantitatively examines the recent proposal of tidal circularization by resonance locking, where tidal dissipation arises from resonances between the star’s natural oscillation frequencies and harmonics of the orbital frequency, and where resonances are “locked” for an extended period of time due to concurrent stellar evolution. We focus on tidal resonances with axi-symmetric gravity modes, and examine binaries with primary masses from one to two solar masses. We find that orbital evolution via resonance locking occurs primarily during the star’s pre-main-sequence phase, with the main-sequence phase contributing negligibly. Resonance locking, ignoring nonlinearity, can circularize binaries with peri-center distances out to ∼10 stellar radii, corresponding to circular periods of ∼4–6 days. However, we find resonantly excited gravity modes will become nonlinear in stellar cores, which prevents them from reaching their full, linear amplitudes. We estimate that such a “saturated resonance lock” reduces the circularization period by about a third, but resonance locking remains much more effective than the cumulative actions of equilibrium tides. In a companion paper, we examine recent binary data to compare against theory.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Subaru Hyper Suprime-Cam Survey of Cygnus OB2 Complex – I. Introduction, photometry, and source catalogue

ABSTRACT Low-mass star formation inside massive clusters is crucial to understand the effect of cluster environment on processes like circumstellar disc evolution, planet, and brown dwarf formation. The young massive association of Cygnus OB2, with a strong feedback from massive stars, is an ideal target to study the effect of extreme environmental conditions on its extensive low-mass population. We aim to perform deep multiwavelength studies to understand the role of stellar feedback on the IMF, brown dwarf fraction and circumstellar disc properties in the region. We introduce here, the deepest and widest optical photometry of 1.5○ diameter region centred at Cygnus OB2 in r2, i2, z, and Y-filters, using Subaru Hyper Suprime-Cam (HSC). This work presents the data reduction, source catalogue generation, data quality checks, and preliminary results about the pre-main sequence sources. We obtain 713 529 sources in total, with detection down to ∼28, 27, 25.5, and 24.5 mag in r2, i2, z, and Y-band, respectively, which is ∼3 – 5 mag deeper than the existing Pan-STARRS and GTC/OSIRIS photometry. We confirm the presence of a distinct pre-main sequence branch by statistical field subtraction of the central 18 arcmin region. We find the median age of the region as ∼5 ± 2 Myr with an average disc fraction of ∼9 per cent. At this age, combined with A $_V\, \sim$ 6 – 8 mag, we detect sources down to a mass range of ∼0.01–0.17 M⊙. The deep HSC catalogue will serve as the groundwork for further studies on this prominent active young cluster.

Gupta, Saumya (ORCID:0000000161843958)↗
DOE OSTI2022

A Machine Learning Framework to Predict Images of Edge-on Protoplanetary Disks

The physical structure and properties of protoplanetary disks are typically derived from spatially resolved disk images. Edge-on disks in particular provide an important view point on the vertical structure and degree of settling of disks. Such analyses rely on radiative transfer (RT) calculations that are generally computationally intensive due to the high optical depth of disks. Here we present a machine learning framework that has the potential to dramatically speed up the forward modeling process by approximating the results of RT calculations. This framework, trained on an initial set of RT calculations, utilizes an autoencoder neural network to enable the generation of synthetic scattered light images of edge-on disks directly from a set of physical parameters. We demonstrate that this framework generates synthetic images 2–3 orders of magnitude faster than using RT calculations. These machine learning-generated images appear to approximate the RT images well, in particular preserving their size and shape. We also find a strong correlation between the latent space representations of the generated disk images and several of their associated physical parameters. Finally, we discuss potential changes to the framework, such as methods to further improve the image quality, extending the framework to multiple wavelengths, and inverting the process to infer physical parameters from observed images. Overall, these new tools have the potential to enable a more efficient and uniform analysis of edge-on disk properties and the initial conditions of planet formation.

79 ASTRONOMY AND ASTROPHYSICS↗