PhD Thesis

PhD Thesis : Unveiling galaxy evolution with the 4MOST-WAVES survey

4MOST-WAVES

The 4MOST Wide Area Vista ExtraGalactic Survey (WAVES) is a next generation, high-density spectroscopic survey designed to probe the evolutionary history of galaxies and cosmic large-scale structures over the past ~8 billion years. Executed as a major consortium survey utilizing the 4-metre Multi-Object Spectroscopic Telescope (4MOST) on the European Southern Observatory’s (ESO) VISTA telescope at Paranal (Chile), WAVES is poised to collect highly complete spectroscopic data for approximately 1.7 million galaxies. The survey is strategically split into two primary components: WAVES-Wide, covering ~1,200 square degrees to sample the local Universe (z < 0.2), and WAVES-Deep, probing a narrower area (~65 square degrees) down to a higher redshift limit (z < 0.8). By constructing highly complete catalogs of galaxy pairs, groups, and filaments, WAVES aims to uncover the underlying dark matter distribution 

THESIS OBJECTIVES

While the primary observational strategy of WAVES is optimized for massive multiplexing to secure robust spectroscopic redshifts, individual spectra often exhibit a low Signal-to-Noise Ratio (S/N). This baseline characteristic limits detailed stellar population analysis on an object-by-object basis. The aim of this thesis is to circumvent this limitation by exploiting the comprehensive data collected during the first year of WAVES observations through the implementation of a co-addition framework. By applying a multi-parametric stacking technique, and considering the vast amount of data available from the very first year of the survey, it will be possible to perform an unprecedented study of the evolutionary stage of galaxies at intermediate redshifts. This initial dataset encompasses approximately 120,000 galaxies in the DEEP area. Through an analysis based on comparisons with up-to-date spectrophotometric models (with the potential to develop further modeling aspects), the project aims to characterize intermediate-redshift galaxies in terms of stellar age, chemical composition, and past star formation history, as a function of their mass and the environment they reside in. The selected PhD candidate will be at the forefront of exploiting this unique combination of spectroscopic and multi-wavelength data to investigate the physical mechanisms driving galaxy formation and evolution, and to determine how these processes are connected to the large-scale environment in which galaxies reside. 

MAIN SCIENCE CASE

The main scientific goal of this thesis will be to investigate the chemical evolution of galaxies through accurate measurements of both gas-phase and stellar metallicities, with particular attention to their detailed chemical composition.

A significant part of the thesis will focus on the technical analysis of the different metallicity calibrations used in the literature, with the aim of assessing their systematic differences and enabling a robust comparison between the measurements obtained in this work and previous studies. In particular, the thesis will investigate the connection between gas-phase and stellar metallicity, in order to explore the link between chemical enrichment, star formation, and galaxy evolution.

State-of-the-art spectrophotometric models particularly suited to this type of analysis, such as sMILES (Knowles et al. 2023) and TMJ (Thomas, Maraston & Johansson 2011), will provide the basis for deriving stellar metallicities. These will be obtained through comparisons between the absorption features measured in the stellar continuum and those predicted by the models, adopting a Bayesian statistical approach. Existing codes developed for this type of analysis can be exploited, with the possibility of developing and testing new approaches building upon the available tools.

Gas-phase metallicities will instead be derived from measurements of emission lines and their line ratios, using established and tested calibration relations (e.g. Curti et al. 2017). Since gas-phase metallicity measurements primarily trace oxygen abundance, while stellar metallicities are more directly sensitive to the iron abundance, a detailed analysis will be carried out to consistently compare the two tracers and investigate their relation.

The final goal of the thesis will be to reconstruct the chemical evolutionary history of galaxies as a function of their stellar mass and environment, providing new insights into the interplay between chemical enrichment, star formation, and galaxy evolution.

WORK PLAN AND TIMELINE

The first year of the PhD will be dedicated to developing and testing the multi-parametric stacking approach for the spectra. During this phase, the PhD candidate may co-supervise a Master’s thesis student to support the workload. Furthermore, as data steadily become available, the candidate will actively participate in the 4MOST-WAVES team activities regarding data quality assessment, thereby becoming a fully integrated member of the collaboration. A 4MOST All Hands Meeting is scheduled to take place in Perth in May-June 2027, followed by the specific 4MOST-WAVES team meeting. The candidate will have the opportunity to attend both the events, providing significant international visibility and networking opportunities with international experts. 

The second year and part of the third year will be fully devoted to the scientific analysis itself. In addition to internal research, this phase will involve collaborative visits to Italian observatories where leading experts in stellar populations (e.g., S. Zibetti and A. Gallazzi in Florence; F. La Barbera in Naples) and nebular emissions (e.g., G. Curti in Bologna; A. Concas in Cagliari) are based. Furthermore, extended research stays are planned at international institutes where the reference spectrophotometric models are developed (e.g., the University of Portsmouth in the UK and the IAC in Tenerife, Spain).

This dense network of collaborations, combined with being an active part of the 4MOST-WAVES team, will offer the candidate numerous publication opportunities, closely tied to their productivity, skills, and research pace.