Dr Ting-Yun Cheng
(aka Sunny)

A galaxy's ecosystem is composed of various gases. The intergalactic gas flows into the halo of a galaxy, this gas further accretes into the inner part of a galaxy, fuelling star formation which may later eject enriched material back to the halo and/or the intergalactic medium. This ecosystem plays a key role in driving various facets of galaxy evolution, containing an imprint of their chemical evolution, star formation history, and internal kinematics of a galaxy. When radiation from a background quasar passes through and interacts with these gases, the gas imprints an absorption line on the quasar spectrum. These absorption lines — collectively called the Lyman alpha (Lyα) forest — contain precious information about the physical properties of the gas, such as the gas density and temperature.

I am leading a major project to develop an automated line detection and fitting algorithm using machine learning approaches to maximise the number of spectroscopic data that can be analysed within a limited timeframe and the amount of information that is extracted from these spectra. The primary pipeline can identify and characterise Lyα absorbers with low HI column density from the forest (Cheng, Cooke & Rudie 2022). This specific region has been challenging for other machine learning approaches due to its shallow and narrow absorption features.