Two Layers of Galaxy Aging in MaNGA DynPop: A Principle-Level Proof of Concept

We test a principle-level prediction from an age-dependent gravity framework using the public MaNGA DynPop DR17 catalogs. The prediction is that younger galaxies, at fixed stellar mass, should show larger mass-to-light discrepancies between dynamical and stellar-population estimates. We join three public DynPop products (JAM dynamical catalogs, stellar-population/star-formation-history catalogs, and circular-velocity-curve tables), apply quality cuts, and split 5,952 galaxies into eight stellar-mass bins of 744 galaxies each, then compare the youngest and oldest quartiles within each bin. The raw mass-to-light discrepancy (DML) is larger for young galaxies in 8/8 mass bins (observed SPS) and 6/8 bins (intrinsic SPS). A component decomposition shows that this raw result is mostly driven by the SPS denominator: older stellar populations have higher stellar mass-to-light ratios, as standard stellar-population synthesis predicts. Under a collinearity-free control model removing SPS mass-to-light, metallicity, and structural variables, a smaller dynamical residual persists in 7/8 mass bins across all three SPS control sets. Parametric dark-matter fractions do not show the same signal. The strongest controlled residual tracks the dust-carrying SPS definition. We present this as a proof of concept and invitation to specialist replication, not a detection claim.

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-07-01
DOI
https://doi.org/10.5281/zenodo.21088606
Primary Topic
Galaxies: Formation, Evolution, Phenomena
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

Two Layers of Galaxy Aging in MaNGA DynPop: A Principle-Level Proof of Concept

Louis McGinty
Zenodo (CERN European Organization for Nuclear Research)
Galaxies: Formation, Evolution, Phenomena
preprint

Two Layers of Galaxy Aging in MaNGA DynPop: A Principle-Level Proof of Concept

Louis McGinty
preprint en

Abstract

We test a principle-level prediction from an age-dependent gravity framework using the public MaNGA DynPop DR17 catalogs. The prediction is that younger galaxies, at fixed stellar mass, should show larger mass-to-light discrepancies between dynamical and stellar-population estimates. We join three public DynPop products (JAM dynamical catalogs, stellar-population/star-formation-history catalogs, and circular-velocity-curve tables), apply quality cuts, and split 5,952 galaxies into eight stellar-mass bins of 744 galaxies each, then compare the youngest and oldest quartiles within each bin. The raw mass-to-light discrepancy (DML) is larger for young galaxies in 8/8 mass bins (observed SPS) and 6/8 bins (intrinsic SPS). A component decomposition shows that this raw result is mostly driven by the SPS denominator: older stellar populations have higher stellar mass-to-light ratios, as standard stellar-population synthesis predicts. Under a collinearity-free control model removing SPS mass-to-light, metallicity, and structural variables, a smaller dynamical residual persists in 7/8 mass bins across all three SPS control sets. Parametric dark-matter fractions do not show the same signal. The strongest controlled residual tracks the dust-carrying SPS definition. We present this as a proof of concept and invitation to specialist replication, not a detection claim.

Zenodo (CERN European Organization for Nuclear Research)
Research Institute of Hermes (RU), Health and Education Research Management and Epidemiologic Services (United States) (US)
Galaxies: Formation, Evolution, Phenomena
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.