On bursty star formation during cosmological reionization -- influence on the metal and dust content of low-mass galaxies

Abstract

Observations indicate that high-redshift galaxies undergo episodic star formation bursts, driving strong outflows that expel gas and suppress accretion. We investigate the consequences for metal and dust content of galaxies at $z\ge 5$ using our semi-analytical model, ASHVINI. We track gas-phase and stellar metallicities ($Z_{\mathrm{g}},Z_{\ast }$) and dust mass (M${}_{\mathrm{d}}$) in dark matter haloes spanning $M_{\mathrm{halo}}=10^6$-$10^{11}$ $M_{\odot }$, comparing continuous and bursty star formation scenarios - which reflect underlying assumptions of instantaneous and delayed feedback - and we allow for metallicity-dependent feedback efficiency. Delayed feedback induces oscillations in $Z_{\mathrm{g}}$ and $Z_{\ast }$, with $Z_{\mathrm{g}}$ declining sharply at low stellar and halo masses; the mass scale of this decline increases toward lower redshift. Reionization introduces significant scatter in $Z_{\mathrm{g}}$, producing an upturn followed by rapid decline. Metallicity-dependent feedback moderates this decline at $z=7-10$, flattening the $Z_{\mathrm{g}}$-mass relation to $\simeq 0.03$-$0.04Z_{\odot }$. Dust production tracks $Z_{\mathrm{g}}$ but is sensitive to burst history, causing delayed enrichment. Our results show that burst-driven feedback decouples $Z_{\mathrm{g}}$ and $Z_{\ast }$, imprints intrinsic scatter in mass-metallicity relations, and delays dust growth. These effects are strongest in low-mass halos ($M_{\mathrm{halo}}\sim 10^7{M}_{\odot }$), where shallow potentials amplify the impact of feedback. Our results are consistent with recent hydrodynamical and semi-analytical simulations and provide context for interpreting JWST (James Webb Space Telescope) metallicity and dust measurements, highlighting the importance of episodic star formation in early galaxy chemical evolution.

Publications of the Astronomical Society of Australia published version; 13 pages, 8 figures; code and data available at https://github.com/Anand-JM97/Ashvini