Master Thesis #1

Multiparametric spectral stacking in the era of new generation spectroscopic surveys (4MOST, DESI, WEAVE)

SCIENTIFIC CONTEST

The advent of new generation spectroscopic surveys is opening an unprecedented opportunity to investigate how galaxies form and evolve across cosmic time and in different environments. Surveys such as 4MOST, DESI and WEAVE are providing, or will soon provide, spectra for millions of galaxies over large cosmological volumes, allowing us to study galaxy populations across a wide range of stellar masses, star formation histories, environments, and redshifts.

A major scientific challenge, however, is that the spectra of many individual galaxies in these surveys have relatively low Signal-to-Noise Ratio (SNR). This limits our ability to measure accurately the detailed properties of their stellar populations and ionized gas. 

Spectral stacking (co-adding) provides a powerful way to overcome this limitation. By combining the spectra of galaxies with similar physical properties, it is possible to recover spectral features that are too faint to be detected in individual objects. Stacked spectra can therefore provide access to stellar population and gas properties that would otherwise remain inaccessible, while allowing us to investigate systematic trends across large galaxy populations.

THESIS OBJECTIVES

The main goal of the project is to develop and apply a physically motivated spectral stacking methodology to investigate the properties and evolution of galaxies at intermediate redshift.

The student will contribute to the development of an innovative approach in which galaxies are selected in a multi-parameter physical space, rather than being grouped using simple one dimensional or broad observational bins. Parameters such as stellar mass, colour, star formation activity, redshift, and other available galaxy properties can be combined to identify samples of galaxies that are as physically homogeneous as possible. The resulting stacked spectra will then allow the student to investigate faint spectral features and derive physical properties that cannot be robustly measured for individual galaxies.

The project builds on an existing software framework that has already demonstrated its potential. Rather than starting from scratch, the student will refine and generalize the algorithm, making it flexible enough to handle different datasets and sets of physical parameters. The final stage will consist of validating and optimizing the method using real observational data. The resulting methodology will provide a valuable tool for the analysis of the much larger datasets that will become available from 4MOST/WAVES and WEAVE-StePS.

SCIENCE APPLICATIONS (tailored to the student's interests)

Once the stacking methodology has been validated, a substantial part of the thesis will be devoted to a scientific application selected according to the student's interests. Possible scientific applications include comparing the metallicity of the ionized gas with the stellar metallicity in star-forming galaxies at intermediate redshift, in order to investigate the connection between chemical enrichment, star formation and galaxy evolution. Another possibility is to study galaxies transitioning through the Green Valley, using stacked spectra to identify the spectral signatures associated with the quenching of star formation and to constrain their stellar populations and recent star formation histories.

Other applications can be explored depending on the student's interests and on the properties of the available datasets.