The Andromeda (M31) Optical And Infrared Disk Survey (Androids)
Bibliographic record
Abstract
The spectral energy distributions of galaxies inform us about a galaxy's stellar populations and interstellar medium, revealing stories of galaxy formation and evolution. How we interpret this light depends in part on our proximity to the galaxy. For nearby galaxies, detailed star formation histories can be extracted from the resolved stellar populations, while more distant galaxies feature the contributions of entire stellar populations within their integrated spectral energy distribution. This thesis aims to resolve whether the techniques used to investigate stellar populations in distant galaxies are consistent with those available for nearby galaxies. As the nearest spiral galaxy to us, Andromeda (M31) is the ideal test-bed for the joint study of resolved stellar populations and panchromatic spectral energy distributions. We present the Andromeda Optical and Infrared Disk Survey (ANDROIDS), which adds new near-UV to near-IR (<em>u*g′r′i′JK<sub>s</sub></em>) imaging using the MegaCam and WIRCam cameras at the Canada-France-Hawaii telescope to the available M31 panchromatic dataset. In order to accurately subtract photometric background from our extremely wide-field (14 square degree) mosaics, we present integrated observing and data reduction techniques with sky-target nodding, optimization of image-to-image surface brightness, and a novel hierarchical Bayesian model to trace the background signal while modelling the astrophysical SED. We model the spectral energy distributions of M31 pixels with MAGPHYS (da Cunha et al 2008) and compare those results to resolved stellar population models of the same pixel from the Panchromatic Hubble Andromeda Treasury (PHAT) survey (Williams et al 2017). We find substantial (0.3 dex) differences in stellar mass estimates despite a common use of the Chabrier (2003) initial mass function. Stellar mass estimated from the resolved stellar population is larger than any mass estimate from SED models or colour-\(M_*/L\) relations (CMLRs). There is also considerable diversity among CMLR estimators, largely driven by differences in the star formation history prior distribution. We find broad consistency between the star formation history estimated by integrated spectral energy distributions and resolved stars. Generally, spectral energy distribution models yield a stronger inside-out radial metallicity gradient and bias towards younger mean ages than resolved stellar population models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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; both teacher heads agree on what is shown here.
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".