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∗>108.5M⊙) 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(sSFR) and visual merger stage (r=0.161, p=7.23×10−23), with a best-fit relation log(sSFR)=(0.148±0.015)SMerg−(1.865±0.038). 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.
@article{arxiv.2605.08537,
title = {Specific Star Formation Rate Enhancement across the Galaxy Merger Sequence: Insights from Citizen Science Classifications},
author = {Jacob Lee and Alexandra Le Reste and Claudia Scarlata and Kameswara Mantha Bharadwaj},
journal= {arXiv preprint arXiv:2605.08537},
year = {2026}
}