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Record W4399061654 · doi:10.1051/0004-6361/202449943

SDSS-V Local Volume Mapper (LVM): A glimpse into Orion

2024· preprint· en· W4399061654 on OpenAlexfundno aff
Kathryn Kreckel, Oleg V. Egorov, E. S. Egorova, Guillermo A. Blanc, Niv Drory, Marina Kounkel, J. Eduardo Méndez-Delgado, C. Román-Zúñiga, S. F. Sánchez, Guy S. Stringfellow, Amelia M. Stutz, Eleonora Zari, J. K. Barrera-Ballesteros, Dmitry Bizyaev, Joel R. Brownstein, Enrico Congiu, José G. Fernández-Trincado, Lynne A. Hillenbrand, Héctor J. Ibarra-Medel, Yu Jin, Evelyn J. Johnston, Amy Jones, Jinyoung Serena Kim, Juna A. Kollmeier, Shuo Kong, Dhanesh Krishnarao, Nimisha Kumari, J. Li, A. Mata-Sánchez, A. Mejía-Narváez, S. A. Popa, Hans‐Walter Rix, Natascha Sattler, Javier Serna, Amrita Singh, José Sánchez-Gallego, Aida Wofford, Tony Wong

Bibliographic record

VenueAstronomy and Astrophysics · 2024
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicAstronomy and Astrophysical Research
Canadian institutionsnot available
FundersLeibniz-GemeinschaftEuropean Research CouncilUniversity of Colorado BoulderUniversity of California, Los AngelesNational Astronomical Observatories, Chinese Academy of SciencesUniversity of Illinois at Urbana-ChampaignMax-Planck-Institut für AstronomieNew Mexico State UniversityNanjing UniversityYale UniversityUniversity of TorontoÉcole Polytechnique Fédérale de LausanneDeutsche ForschungsgemeinschaftSpace Telescope Science InstituteUniversidad Nacional Autónoma de MéxicoAlfred P. Sloan FoundationJohns Hopkins UniversityHarvard UniversityOhio State UniversityCarnegie Institution of WashingtonAgencia Nacional de Investigación y DesarrolloSmithsonian Astrophysical ObservatoryFlatiron HealthSmithsonian InstitutionYunnan UniversityNational Science FoundationChina National Textile and Apparel CouncilCalifornia Institute of TechnologyJet Propulsion LaboratoryLeibniz-Institut für Astrophysik PotsdamNational Aeronautics and Space Administration
KeywordsVolume (thermodynamics)Orion NebulaPhysicsAstrophysicsStars

Abstract

fetched live from OpenAlex

Context. The Orion Molecular Cloud complex, one of the nearest (D = 406 pc) and most extensively studied massive star-forming regions, is ideal for constraining the physics of stellar feedback, but its ~12 deg diameter on the sky requires a dedicated approach to mapping ionized gas structures within and around the nebula. Aims. The Sloan Digital Sky Survey (SDSS-V) Local Volume Mapper (LVM) is a new optical integral field unit (IFU) that will map the ionized gas within the Milky Way and Local Group galaxies, covering 4300 deg2 of the sky with the new LVM Instrument (LMV-I). Methods. We showcase optical emission line maps from LVM covering 12 deg2 inside of the Orion belt region, with 195 000 individual spectra combined to produce images at 0.07 pc (35.3″) resolution. This is the largest IFU map made (to date) of the Milky Way, and contains well-known nebulae (the Horsehead Nebula, Flame Nebula, IC 434, and IC 432), as well as ionized interfaces with the neighboring dense Orion B molecular cloud. Results. We resolve the ionization structure of each nebula, and map the increase in both the [S II]/Hα and [N II]/Hα line ratios at the outskirts of nebulae and along the ionization front with Orion B. [O III] line emission is only spatially resolved within the center of the Flame Nebula and IC 434, and our ~0.1 pc scale line ratio diagrams show how variations in these diagnostics are lost as we move from the resolved to the integrated view of each nebula. We detect ionized gas emission associated with the dusty bow wave driven ahead of the star σ Orionis, where the stellar wind interacts with the ambient interstellar medium. The Horsehead Nebula is seen as a dark occlusion of the bright surrounding photo-disassociation region. This small glimpse into Orion only hints at the rich science that will be enabled by the LVM.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.002

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.

Opus teacher head0.008
GPT teacher head0.254
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations24
Published2024
Admission routes1
Has abstractyes

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