activity
20242026
most citedexoALMA XXIII. Estimating Disk and Planet Properties from Dust Morphologies with DBNets2.0

1 citations · 2 across the 4 of their papers we have counts for

collaborators

6 papers

astro-ph.IM2026

Denoising Interferometric Observations Using Visibilities-Informed Neural Networks

Jason P. Terry, Cassandra Hall, Sergei Gleyzer

The upcoming observations from the Square Kilometer Array Observatory will provide the astronomical community with a wealth of observations of important objects at long wavelengths…

astro-ph.EP20261 cited

exoALMA XXIII. Estimating Disk and Planet Properties from Dust Morphologies with DBNets2.0

Alessandro Ruzza, Giuseppe Lodato, Giovanni Rosotti +21

The exoALMA large program provided an unprecedented view of the morphology and kinematics of 15 circumstellar disks, offering a biased but homogenous and well-characterized sample…

astro-ph.EP20261 cited

A Collective Trigger for Widespread Planetesimal Formation Revealed by Accretion Ages

James Bryson, Hannah Sanderson, Francis Nimmo +4

The formation of planetesimals was an integral part of the cascading series of processes that built the terrestrial planets. To illuminate planetesimal formation, here we develop a…

astro-ph.EP2026

Dust Morphology Under Changing Dust Mass Ratios in Protoplanetary Discs

Matthew Murray, Cassandra Hall, Hans Baehr +1

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 d…

cond-mat.mtrl-sci2025

Excitonic Landscapes in Monolayer Lateral Heterostructures Revealed by Unsupervised Machine Learning

Maninder Kaur, Nicolas T. Sandino, Jason P. Terry +2

Two-dimensional (2D) in-plane heterostructures including compositionally graded alloys and lateral heterostructures with defined interfaces display rich optoelectronic properties a…

astro-ph.EP2024

A Machine Learning Approach to Detecting Albedo Anomalies on the Lunar Surface

Sofia Strukova, Sergei Gleyzer, Patrick Peplowski +1

This study introduces a data-driven approach using machine learning (ML) techniques to explore and predict albedo anomalies on the Moon's surface. The research leverages diverse pl…