Star-forming Galaxies

▸ Mass and star formation functions of the Local Universe

I am part of the Star Formation Reference Survey project (SFRS; P.I. M. Ashby; Ashby et al. 2011). The 369 galaxies composing the sample are chosen to be representative of star formation rate (SFR), dust temperature, and specific star-formation rate (sSFR) in the Local Universe. This is a multiwavelength survey which aims to define global estimators that can systematically account for the different conditions under which star-formation occurs.

ARP273 – NASA, ESA, and the Hubble Heritage Team (STScI/AURA)

My role in the project is to lead the morphological analysis of the sample galaxies, and relate it to their star-formation activity. To do this, I performed 2D bulge/disk decomposition of the 2MASS/K-band images of the sample, which was used to derive the masses of the galaxies (and of their bulge/disk sub-components) and, ultimately, the sSFR. I produced the global stellar mass function, along with the separate mass functions for the bulge and disk sub-components: these are descriptive of the distribution of the already converted stellar mass in star-forming galaxies. Additionally, I produced the sSFR and the bivariate (mass vs. sSFR) volume-weighted functions, the latter being a more accurate representation of the star-forming main sequence. These functions are representative of the global star-formation activity in the Local Universe, and constitute the ideal reference benchmarks for simulations investigating star-formation in different environments.

MF_comparison

Reproduced from Bonfini et al. (2018, sub. to MNRAS). Comparison of total (green diamonds), disk (blue dots), and bulge (red squares) mass (top) and mass-density (bottom) functions for the SFRS galaxies. These are representative of the mass distribution of star-forming galaxies in the Local Universe.

 Ph.D. thesis (link)

▸ Physical processes in nearby active galaxies via SED fitting

As part of the the GOALS collaboration, I am currently producing a tool to seamlessly fit the future JWST spectra: this task is part of an accepted ERS proposal created by the team, which will observe Luminous Infra-Red Galaxies (LIRGS) with the IFUs of NIRSPEC and MIRI. Notably, our software will become one of the official tools for the analysis of JWST data. To this purpose I am creating an updated, performing variant of the CAFE mid-IR spectral modelling software (Marshall et al. 2007; 2018).

CAFE

Results from the CAFE spectral decomposition. The black solid line/points shows the observed spectrum/photometry. The orange line shows the overall best-fit model, while the shaded grey area displays its uncertainty. The separate sub-components are: star-burst stellar population (SB; yellow), hot (magenta), warm (blue), cool (green), and cold (red) dust, PAHs (violet), emission lines (red). The bottom panel shows the fit residuals expressed in units of data uncertainty.

We are testing our software on a selected sub-sample of GOALS galaxies, for which we have sublime multi-wavelength coverage from optical to mid-IR. In particular, we exploit spectro-photometric data from 2MASS, Akari, Spitzer, and Herschel . The objective is to investigate the local effects of environment on the properties of the ISM by studying the spectral features produced by e.g. silicate grains, PAHs, and warm hydrogen in LIRGS.

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