Specific Star Formation Rate Enhancement across the Galaxy Merger Sequence: Insights from Citizen Science Classifications

TL;DR

This study analyzes how specific star formation rates change across galaxy merger stages using citizen science classifications, confirming a positive correlation and demonstrating citizen science's effectiveness in galaxy evolution research.

Contribution

It introduces a large citizen science-based dataset to study star formation rate enhancement across galaxy mergers, validating visual classifications as a useful tool.

Findings

Weak but significant positive correlation between sSFR and merger stage

Best-fit relation: log(sSFR) increases with merger stage

Citizen science classifications effectively trace galaxy merger evolution

Abstract

We present an analysis of specific star formation rates (sSFR) across the galaxy merger sequence using visual classifications from the Zooniverse citizen science project "Cosmic Disco: Characterizing Galaxy Collisions". Our sample comprises 4884 galaxy systems pre-selected as merger candidates from SDSS DR17 ($0.01 < z < 0.05$, $M_* > 10^{8.5}M_\odot$) using Zoobot, of which 3690 were classified as mergers spanning pre-interaction through post-coalescence stages by citizen scientist volunteers. We find a weak but statistically significant positive correlation between $\log(\mathrm{sSFR})$ and visual merger stage ($r = 0.161$, $p = 7.23 \times 10^{-23}$), with a best-fit relation $\log\left(sSFR\right)=(0.148\pm0.015)\, S_{\rm Merg}-(1.865\pm0.038)$. The large RMS scatter (0.661 dex) reflects visual merger stages capturing wide merger timescales, and our results corroborate previous findings of increasing SFR enhancement with merger progression. This work shows that citizen science is a viable complement to automated and pair-based approaches to evaluate timescales for galaxies across the merger sequence.