Pushing the technical frontier: from overwhelming large datasets to machine learning
Kentaro Nagamine
› Integration of Semi Analytic Model with SED Modelling Platforms - Dian Triani, Swinburne University of Technology
09:25-09:45 (20min)
› Variations of the stellar initial mass function in semi-analytical models: implications for the mass assembly of galaxies in the GAEA model. - Fabio Fontanot, Astronomical Observatory of Trieste
09:45-10:05 (20min)
› Dust extinction and emission properties in a cosmological simulation - Shohei Aoyama, ASIAA
10:05-10:25 (20min)
› Simulating Galaxy Spectra with the FIRE Simulations - Tova Yoast-Hull, Canadian Institute for Theoretical Astrophysics
10:25-10:45 (20min)
› Modeling the panchromatic emission of galaxies with CIGALE - Médéric Boquien, Universidad de Antofagasta
10:45-11:05 (20min)
Pushing the technical frontier: from overwhelming large datasets to machine learning
Fangting Yuan
› Advanced panchromatic spectral modelling and fitting with BAGPIPES - Adam Carnall, Royal Observatory Edinburgh
11:35-11:55 (20min)
› FortesFit: Flexible SED modelling with a Bayesian backbone - David Rosario, Department of Physics, Durham University
11:55-12:15 (20min)
› Going beyond Galaxy Ages with Dense Basis Star Formation History Reconstruction - Kartheik Iyer, Rutgers, The State University of New Jersey
12:15-12:35 (20min)
› A Dust Spectral Energy Distribution Model with Hierarchical Bayesian Inference and Its Application to the Nearby Universe - Frédéric Galliano, UMR Astrophysique, Instrumentation-Modelisation, à Paris-Saclay
12:35-12:55 (20min)
› Bayesian discrimination of the panchromatic spectral energy distribution modelings of galaxies - Yunkun Han, Yunnan Observatories, CAS
12:55-13:15 (20min)
