Taking the pulse of the outer Milky Way with the Halo Outskirts With Variable Stars (HOWVAST) survey: an RR Lyrae density profile out to >200 kpc
Bibliographic record
Abstract
ABSTRACT In order to constrain the evolutionary history of the Milky Way, we hunt for faint RR Lyrae stars (RRLs) using Dark Energy Camera data from the High cadence Transient Survey and the Halo Outskirts With Variable Stars survey. We report the detection of $\sim$500 RRLs, including previously identified stars and $\sim$90 RRLs not yet reported. We identify nine new RRLs beyond 100 kpc from the Sun, most of which are classified as fundamental-mode pulsators. The periods and amplitudes of the distant RRLs do not place them in either one of the two classical Oosterhoff groups, but in the Oosterhoff intermediate region. We detect two groups of clumped distant RRLs with similar distances and equatorial coordinates, which we interpret as an indication of their association with undiscovered bound or unbound satellites. We study the halo density profile using spheroidal and ellipsoidal ($q=0.7$) models, following a Markov chain Monte Carlo methodology. For a spheroidal halo, our derived radial profile is consistent with a broken power law with a break at $18.1^{+2.1}_{-1.1}$ kpc separating the inner and the outer halo, and an outer slope of $-4.47^{+0.11}_{-0.18}$. For an ellipsoidal halo, the break is located at $24.3^{+2.6}_{-3.2}$ kpc and the outer slope is $-4.57^{+0.17}_{-0.25}$. The break in the density profile is a feature visible in different directions of the halo. The similarity of these radial distributions with previous values reported in the literature seems to depend on the regions of the sky surveyed (direction and total area) and halo tracer used. Our findings are compatible with simulations and observations that predict that the outer regions of Milky Way-like galaxies are mainly composed of accreted material.
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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".