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Matthew Murray

Publications and source records attributed to Matthew Murray.

2 recordsLinked to original sources

Dust Morphology Under Changing Dust Mass Ratios in Protoplanetary Discs

Protoplanetary disc mass is one of the most fundamental properties of a planet-forming system, as it sets the total mass budget available for planet formation. However, obtaining disc mass measurements remain challenging, since it is not possible to directly detect H$_2$, and CO abundance ratios are poorly constrained. Dynamical measurements of the disc mass are now possible, but they are not suited to all discs since the measurements typically require well-behaved emission surfaces. A long-standing method is to obtain continuum flux measurements from the dust emission, and convert to a total disc mass by assumption of the dust-to-gas mass ratio, $\epsilon$. This quantity is poorly constrained in protoplanetary discs. % We investigate the impact of $\epsilon$ on the morphology of planet-containing hydrodynamical simulations of dusty protoplanetary accretion discs, and suggest that if a planet mass estimate can be obtained, then disc morphology could be used to constrain $\epsilon$ in observed systems relative to each other, improving the total disc mass estimates of protoplanetary discs.

astro-ph.EP

Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling

Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models. Lingvo models are composed of modular building blocks that are flexible and easily extensible, and experiment configurations are centralized and highly customizable. Distributed training and quantized inference are supported directly within the framework, and it contains existing implementations of a large number of utilities, helper functions, and the newest research ideas. Lingvo has been used in collaboration by dozens of researchers in more than 20 papers over the last two years. This document outlines the underlying design of Lingvo and serves as an introduction to the various pieces of the framework, while also offering examples of advanced features that showcase the capabilities of the framework.

cs.LG