Galaxy Structure and Mode of Star Formation in the SFR-Mass Plane from z ~ 2.5 to z ~ 0.1 (1107.0317v2)

Abstract: We analyze the dependence of galaxy structure (size and Sersic index) and mode of star formation (\Sigma_SFR and SFR_IR/SFR_UV) on the position of galaxies in the SFR versus Mass diagram. Our sample comprises roughly 640000 galaxies at z~0.1, 130000 galaxies at z~1, and 36000 galaxies at z~2. Structural measurements for all but the z~0.1 galaxies were based on HST imaging, and SFRs are derived using a Herschel-calibrated ladder of SFR indicators. We find that a correlation between the structure and stellar population of galaxies (i.e., a 'Hubble sequence') is already in place since at least z~2.5. At all epochs, typical star-forming galaxies on the main sequence are well approximated by exponential disks, while the profiles of quiescent galaxies are better described by de Vaucouleurs profiles. In the upper envelope of the main sequence, the relation between the SFR and Sersic index reverses, suggesting a rapid build-up of the central mass concentration in these starbursting outliers. We observe quiescent, moderately and highly star-forming systems to co-exist over an order of magnitude or more in stellar mass. At each mass and redshift, galaxies on the main sequence have the largest size. The rate of size growth correlates with specific SFR, and so does \Sigma_SFR at each redshift. A simple model using an empirically determined SF law and metallicity scaling, in combination with an assumed geometry for dust and stars is able to relate the observed \Sigma_SFR and SFR_IR/SFR_UV, provided a more patchy dust geometry is assumed for high-redshift galaxies.

• The paper reveals the early establishment of the Hubble sequence at z ~2.5, linking disk profiles in star-forming galaxies with de Vaucouleurs profiles in quiescent ones.
• The analysis demonstrates significant size evolution, where star-forming galaxies expand and exhibit higher specific SFRs compared to their more compact quiescent counterparts.
• A nuanced relationship between the Sersic index and star formation is uncovered, indicating increased central mass concentration in high-SFR galaxies that may lead to quenching.

Analysis of Galaxy Structure and Star Formation from Redshift 2.5 to 0.1

This paper investigates the interplay between galaxy structure and star formation processes within the star formation rate (SFR) versus stellar mass plane across a broad range of cosmic time, from redshift $z \sim 2.5$ to $z \sim 0.1$. Utilizing a large sample of galaxies, the paper aims to discern patterns and evolutionary trends by employing structural measurements obtained from Hubble Space Telescope (HST) imaging and SFRs derived from a Herschel-calibrated amalgamation of indicators.

Key Findings

1. Early Emergence of the Hubble Sequence: The paper finds that a correlation between galaxy structure and stellar populations, akin to the Hubble sequence, is in place as early as $z \sim 2.5$. Star-forming galaxies tend to have exponential disk profiles, whereas quiescent galaxies are better represented by de Vaucouleurs profiles, independent of the epoch.
2. Size Evolution: The analysis delineates a clear pattern of galaxy size growth over time. At each redshift, galaxies on the star-forming main sequence (SFMS) exhibit the largest sizes, while quiescent galaxies are more compact for their mass. This differential size evolution is correlated with specific star formation rates (sSFR), correlating an increase in galaxy size with higher sSFRs.
3. Sersic Index and Star Formation: A nuanced relationship between the Sersic index and star formation emerges, with the paper noting a reversal at the upper envelope of the SFMS — the Sersic index increases, suggesting a heightened central mass concentration in starburst galaxies. This indicates a potential evolutionary trajectory from high-SFR systems to quiescent galaxies post-quenching.
4. Star Formation Surface Density ($\Sigma_{SFR}$): The surface density of star formation ($\Sigma_{SFR}$) correlates closely with specific SFR, remaining consistent across redshifts, but shifts towards higher values in more actively star-forming galaxies with redshift. This implies a substantive role of increased molecular gas fractions in early universe galaxies.
5. Obscuration and SFR Ratios: The $SFR_{IR}/SFR_{UV}$ ratio, indicative of obscuration, climbs both within the SFMS and towards its high-SFR end. The results hint at complex gas-phase metallicity scaling, integral in obscuration processes, which appear consistent across redshifts.

Theoretical and Practical Implications

The identified structural main sequence and its persistence across epochs reinforce models where gradual internal processes or environmental effects modulate galaxy evolution. A plausible quenching mechanism, potentially involving major mergers, is likely interconnected with morphological transitions, transitioning star-forming to quiescent phases. These findings invite models to account for distinct pathways formed under varying timescales of starburst and quenching cycles.

Moreover, the lack of a sharp demarcation in halo mass for quenching seen in these results points towards alternative triggers of star formation cessation, possibly related to dynamics internal to the galaxy or its baryonic concentration.

Speculations on AI Developments

In a broader context, these insights may inspire AI models simulating galactic evolution, offering a robust framework for extrapolating star formation histories and galaxy morphologies within cosmological simulations. As algorithms increasingly mimic observed cosmic structures, incorporating intricate multi-parameter dependences such as mass-metallicity relations and molecular gas scaling laws will be pivotal in enhancing model fidelity.

The research's extensive dataset, characterized by unprecedented sample sizes, offers an enriched baseline for validating AI-driven exploratory models in astrophysical studies, strengthening the synergy between observational data and theoretical forecasts. Future AI applications could further refine these paradigms, integrating machine learning approaches capable of interpreting the complex interdependencies presented by galaxy evolutionary processes.