The CAMELS Project - Paco Villaescusa Navarro
Exploring the CAMELS project: a large-scale simulation suite for galaxy formation and cosmology, combining hydrodynamics and machine learning to advance our understanding of the Universe.
Towards Astronomical Search by Image - The Hubble Image Similarity Project - Josh Peek
Exploring astronomical image similarity using machine learning, focusing on the Hubble Image Similarity Project to enhance search capabilities and facilitate new discoveries in astrophysics.
Generating High-Fidelity HI Maps Using Score-Based Diffusion Models - Sultan Hassan
Exploring score-based diffusion models to generate high-fidelity HI maps, discussing applications in astrostatistics and machine learning for galaxy formation and evolution studies.
Learning Representations of Galaxies from Simulations and Observations - Suchetha Cooray
Explore advanced data science techniques for analyzing galaxy formation and evolution, bridging simulations with observations to uncover new insights in astrophysics.
Finding Simple Structures in Complex Datasets - Dalya Baron
Exploring data-driven techniques to uncover simple structures in complex astronomical datasets, focusing on galaxy formation and evolution using advanced statistical and machine learning tools.
FLORAH - Planting Better Merger Trees with Deep Generative Models - Tri Nguyen (MIT)
Explore deep generative models for improved merger tree creation in galaxy formation studies, enhancing our understanding of cosmic structure evolution and linking observations with theoretical models.
Constraining Ωm with Phase-Space Information of Galaxies - Natali Soler Matubaro De Santi
Explore innovative techniques for constraining the matter density parameter using galaxy phase-space data, combining astrostatistics and machine learning to advance our understanding of cosmic structure formation.
Too Hot to Handle - The Role of Supermassive Black Holes in Heating the Low Redshift Intergalactic Medium
Explore the impact of supermassive black holes on intergalactic medium heating at low redshift, focusing on AGN feedback mechanisms and their role in galaxy formation and evolution.
How Supermassive Black Holes Ignite the Intergalactic Medium - Tales from the Low Redshift Lyman-alpha Forest
Explore how supermassive black holes impact the intergalactic medium through the lens of the low redshift Lyman-alpha forest, offering insights into galaxy formation and evolution.
Prospects for Detecting Gaps in GC Streams with Roman - Christian Aganze
Exploring detection of gaps in globular cluster streams using Roman Space Telescope, applying advanced statistical and machine learning techniques to enhance our understanding of galaxy formation and evolution.
Optimizing Target Selection for Low-Redshift Galaxy Surveys - Elise Darragh Ford
Explores data-driven techniques for optimizing galaxy survey target selection, enhancing our understanding of galaxy formation and evolution through advanced statistical and machine learning methods.
Dust Properties of Galaxies from a Data-Driven Hierarchical Model - John Forbes
Explore data-driven hierarchical modeling to uncover dust properties in galaxies, revealing insights into galaxy formation and evolution through advanced statistical techniques.
Computational and AI Funding Opportunities - Andreas Berlind (NSF)
Explore NSF funding opportunities for computational and AI research in astrophysics. Learn about available grants and how to apply for support in astrostatistics and machine learning projects.
Galaxy Zoo in the Deep Learning Era - Mike Walmsley
Exploring Galaxy Zoo's evolution with deep learning, focusing on self-supervised techniques and new tools for galaxy morphology analysis in the era of big astronomical data.
Galaxy Merger Reconstruction with Generative Graph Neural Networks - Yuan Sen Ting
Exploring galaxy merger reconstruction using generative graph neural networks, advancing our understanding of galaxy formation and evolution through innovative machine learning techniques.
Finding Environmental Measures Sensitive to Halo Properties Using Neural Networks
Exploring neural networks to identify environmental measures that reveal halo properties in galaxy formation studies, enhancing our understanding of cosmic structure evolution.
The Brightest Cluster Galaxy and Intracluster Light - Louise Edwards
Explore the Brightest Cluster Galaxy and Intracluster Light in this talk by Louise Edwards. Gain insights into galaxy clusters, their central galaxies, and the diffuse light between them.
Learning to Simulate the Universe with Deep Learning - Elena Giusarma
Explore deep learning techniques for simulating the universe, focusing on innovative approaches to model cosmic structures and enhance our understanding of galaxy formation and evolution.
Star Formation Histories of Galaxies Near and Far
Explore galaxy formation and evolution through star formation histories, using advanced statistical and machine learning techniques to analyze vast datasets from current and future astronomical surveys.
Outliers - How I Learned to Love Them, and Why You Should Too
Discover the value of statistical outliers in galaxy formation studies. Learn how anomalous galaxies challenge current paradigms and drive new insights in astrophysics and cosmology.
Tracing Dark Matter with Stars - Lina Necib
Explore innovative techniques for detecting dark matter using stellar dynamics. Learn how advanced statistical methods and machine learning are revolutionizing our understanding of galactic structure and evolution.