Constraints on the production of phosphine by Venusian volcanoes

Kavli Affiliate: Sara Seager | First 5 Authors: William Bains, Oliver Shorttle, Sukrit Ranjan, Paul B. Rimmer, Janusz J. Petkowski | Summary: The initial reports of the presence of phosphine in the cloud decks of Venus has led to the suggestion that volcanism was the source of phosphine, through volcanic phosphides ejected into the clouds. […]


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H-alpha emission in local galaxies: star formation, time variability and the diffuse ionized gas

Kavli Affiliate: Mark Vogelsberger | First 5 Authors: Sandro Tacchella, Aaron Smith, Rahul Kannan, Federico Marinacci, Lars Hernquist | Summary: The nebular recombination line H$alpha$ is widely used as a star-formation rate (SFR) indicator in the local and high-redshift Universe. We present a detailed H$alpha$ radiative transfer study of high-resolution isolated Milky-Way and Large Magellanic […]


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Priors on red galaxy stochasticity from hybrid effective field theory

Kavli Affiliate: Risa H. Wechsler | First 5 Authors: Nickolas Kokron, Joseph DeRose, Shi-Fan Chen, Martin White, Risa H. Wechsler | Summary: We investigate the stochastic properties of typical red galaxy samples in a controlled numerical environment. We use Halo Occupation Distribution (HOD) modelling to create mock realizations of three separate bright red galaxy samples […]


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Legacy of the First Workshop on Gravitational Wave Astrophysics for Early Career Scientists

Kavli Affiliate: Lijing Shao | First 5 Authors: Jean-Baptiste Bayle, BĂ©atrice Bonga, Daniela Doneva, Tanja Hinderer, Archisman Ghosh | Summary: Gravitational wave science is a dynamical, fast-expanding research field founded on results, tools and methodologies drawn from different research areas and communities. Early career scientists entering this field must learn and combine knowledge and techniques […]


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AutoDrop: Training Deep Learning Models with Automatic Learning Rate Drop

Kavli Affiliate: Jing Wang | First 5 Authors: Yunfei Teng, Jing Wang, Anna Choromanska, , | Summary: Modern deep learning (DL) architectures are trained using variants of the SGD algorithm that is run with a $textit{manually}$ defined learning rate schedule, i.e., the learning rate is dropped at the pre-defined epochs, typically when the training loss […]


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