Title: Learning the Universe: Examples from 3D (line intensity mapping), 2D (galaxy deblending), to 1D (classification of spectra)
Speaker: Caroline Heneka
Affiliation: University of Hamburg
Time: Thursday 10 February 2022, 1400 to 1500
Location: online at https://uu-se.zoom.us/j/62091586806 (Zoom meeting ID: 620 9158 6806)
Abstract:
With ongoing and future experiments, we are set to enter a more data-driven era in astronomy and astrophysics, for example with interferometric measurements of the 21-cm signal but also with observations in the far-infrared, optical, UV, and beyond. Both larger-scale techniques such as multi-line intensity mapping and higher sensitivity surveys warrant the need for efficient data reduction and automation as well as the ability to extract more and less biased information. To optimally learn the Universe from low to high redshift I advocate for new observational techniques such as multi-line intensity mapping as well as the application of modern machine learning techniques. In 3D, tomography of line intensity maps such as the 21-cm line of hydrogen can teach us about properties of sources, gaseous media between and cosmological structure formation. The use of multiple lines as well as complementary galaxy survey data will increase information content inferred and its robustness. For example 21-cm radio tomography targeted by the Square Kilometre Array (SKA) can teach about source properties, IGM state and cosmology during the epoch of reionisation, i.e. at high redshifts beyond 5 to 6, as well as HI galaxy properties at lower redshifts. As new analysis layers for astronomy and astrophysics I furthermore showcase in 2D the deblending of galaxy imaging for accurate flux measurements. For 1D, an object classification layer is proposed to efficiently group the around 40 million spectra the 4-metre Multi-Object Spectroscopic Telescope (4MOST) will collect.