Posters List

Last Name First Name Home Institute Poster Title
Ahmed Tania AAO-Macquarie University, AU EMU: Radio detected galaxies are more obscured than optically selected galaxies
Arnaudova Marina University of Hertfordshire, GB Probing the radio-loudness dichotomy of optically-selected quasars
Battisti Andrew Australian National University, AU Linking IFS and multi-wavelength facilities to study galaxy evolution: the TYPHOON and MAGPI surveys
Carvajal Rodrigo Instituto de Astrofísica e Ciências do Espaço, Universidade de Lisboa, PT Radio Galaxy prediction with multi-survey data and ensemble Machine Learning
Deg Nathan Queen's University, CA Gas-rich Polar Ring Galaxies in WALLABY PDR1
Duchesne Stefan CSIRO, AU The Rapid ASKAP Continuum Survey: progress, data releases, and highlights
Gendron-Marsolais Marie-Lou Instituto de Astrofísica de Andalucía-CSIC, ES What can bent-jet radio galaxies teach us about clusters of galaxies?
Guimard Yanis CEA Paris-Saclay, FR Deconvolution and removal of foregrounds in 21 cm intensity maps
Heino Lennart IDIA, ZA The Nature of Polarised Sources in Deep Radio Surveysv
Hongming Tang Department of Astronomy, Tsinghua University, CN Radio Galaxy Zoos - how Astro-AI and citizen science facilitate each other
Maina Eric Rhodes University, ZA MALS HI observations of Klemola 31: emission and absorption
O'Beirne Tamsyn International Centre for Radio Astronomy Research, AU Refining the 200 MHz Local Radio Luminosity Function
Perron-Cormier Mathieu Queen's University, CA Measuring Galaxy Structure with 3D Asymmetries
Radley Isaac University of Leeds, GB A multifrequency cm-mm survey of Ophiuchus A: a pilot study for an SKA-MID young cluster deep field
Rozgonyi Kristof Ludwig-Maximilians Universität, DE Asynchronous on-the-fly (OTF) mosaic imaging with MeerKAT
Singal Ashok Physical Research Laboratory, IN Dipole asymmetries seen in large radio surveys - VLASS and RACS
Vardoulaki Eleni Thüringer Landessternwarte Tautenburg (TLS), DE Radio Galaxy Zoo EMU: building a citizen science project using novel methods
Worrell Ellie Instituto de Astrofísica e Ciências do Espaço, Universidade de Lisboa, PT Machine Learning Techniques to Classify Emission Line Galaxies with Missing Spectral Lines