International Conference on Machine Learning for Astrophysics - ML4Astro

Session

SKA and Precursors

1 Jun 2022, 10:20

Catania

Conveners

SKA and Precursors: Session 1

• Andrea Possenti (Istituto Nazionale di Astrofisica (INAF))

37. Deep Learning Processing and Analysis of Mock Astrophysical Observations

Claudio Gheller (Istituto Nazionale di Astrofisica (INAF))

01/06/2022, 10:20

Oral Presentation

The challenge facing astronomers in the upcoming decade is not only scientific, but also technological.
A flurry of complex data will be delivered by new telescopes such as SKA ant its precursors and pathfinders, Athena ecc. This data will be difficult to manage with traditional approaches. Data will have to be stored in dedicated facilities, providing the necessary capacity at the highest...

38. Deep neural networks for source detection in radio astronomical maps

Daniel Magro (University of Malta and INAF), Renato Sortino (Istituto Nazionale di Astrofisica (INAF))

01/06/2022, 10:40

Oral Presentation

Source finding is one of the most challenging tasks in upcoming radio continuum surveys with SKA precursors, such as the Evolutionary Map of the Universe (EMU) survey of the Australian SKA Pathfinder (ASKAP) telescope. The resolution, sensitivity, and sky coverage of such surveys is unprecedented, requiring new features and improvements to be made in existing source finders. Among them,...

115. A machine learning classifier for LOFAR radio galaxy cross-matching techniques

Lara Alegre (Institute for Astronomy, University of Edinburgh)

Oral Presentation

New-generation radio telescopes like LOFAR are conducting extensive sky surveys, detecting tens of millions of radio sources. To maximise the scientific value of these surveys, radio source components must be properly associated into physical sources before being cross-matched with their optical/infrared counterparts. We use machine learning to identify those radio sources for which either...

91. Analysis of Instance Segmentation Networks for Dispersed FRB Searches

Anastasia Seifert (University of Malta)

Oral Presentation

Next generation telescopes such as the SKA will be collecting a substantial amount of radio data in future surveys. The number of Fast Radio Burst (FRB) events are expected to increase as a result of the SKA having a higher sensitivity and resolution; thus increasing the volume of data collected which will consequently need to be analyzed and processed. Therefore, the previous methods of FRB...

79. ASGARD: A deep neural network for the detection of compact sources, extended galaxies, and sidelobes in radio astronomical maps

Daniel Magro (University of Malta and INAF)

Oral Presentation

Source finding is one of the most challenging tasks in upcoming radio continuum surveys with SKA precursors, such as the Evolutionary Map of the Universe (EMU) survey of the Australian SKA Pathfinder (ASKAP) telescope. The resolution, sensitivity, and sky coverage of such surveys is unprecedented, requiring new features and improvements to be made in existing source finders. Among them,...

136. Deep learning 21cm light-cones in 3D

Caroline Heneka (UHH)

Oral Presentation

Interferometric measurements of the 21cm signal with the Square Kilometre Array are a prime example of the data-driven era in astronomy and astrophysics we are entering with current and upcoming experiments. To optimally learn the Universe from low to high redshift I advocate for the use of multiple lines (multi-line intensity mapping) and complementary galaxy survey data, as well as the...

55. Deep Learning Processing and Analysis of Mock Astrophysical Observations

Claudio Gheller (Istituto Nazionale di Astrofisica (INAF))

Oral Presentation

The challenge facing astronomers in the upcoming decade is not only scientific, but also technological.
A flurry of complex data will be delivered by new telescopes such as SKA ant its precursors and pathfinders, Athena ecc. This data will be difficult to manage with traditional approaches. Data will have to be stored in dedicated facilities, providing the necessary capacity at the...