The Connection between Galaxy Mergers, Star Formation, and Active Galactic Nuclei Activity in the HSC-SSP
Bibliographic record
Abstract
Abstract Internal gas inflows driven by galaxy mergers are considered to enhance star formation rates (SFRs), fuel supermassive black hole growth, and stimulate active galactic nuclei (AGNs). However, quantifying these phenomena remains a challenge, due to difficulties both in classifying mergers and in quantifying galaxy and AGN properties. We quantitatively examine the merger–SFR–AGN connection using Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) galaxies using novel methods for both galaxy classification and property measurements. Mergers in HSC-SSP observational images are identified through fine-tuning Zoobot, a pretrained deep representation learning model, using images and labels based on the Galaxy Cruise project. We use galaxy and AGN properties that were produced by fitting Galaxy and Mass Assembly spectra using the spectral energy distribution fitting code ProSpect, which fits panchromatically across the far-ultraviolet through far-infrared wavelengths and obtains galaxy and AGN properties simultaneously. Small differences are seen in SFR and AGN activity between mergers and controls, with ΔSFR = −0.009 ± 0.003 dex, Δf AGN = −0.010 ± 0.033 dex, and ΔL AGN = 0.002 ± 0.025 dex. After further visual purification of the merger sample, we find ΔSFR = −0.033 ± 0.014 dex, Δf AGN = −0.024 ± 0.170 dex, and ΔL AGN = 0.019 ± 0.129 dex for pairs, and ΔSFR = −0.057 ± 0.024 dex, Δf AGN = 0.286 ± 0.270 dex, and ΔL AGN = 0.329 ± 0.195 dex for postmergers. These numbers suggest secular processes being an important driver for star formation and AGN activity, and present a cautionary tale when using longer-timescale tracers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".