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Record W4386044317 · doi:10.48550/arxiv.2308.08602

Reading Between the (Spectral) Lines: Magellan/IMACS spectroscopy of the Ultra-faint Dwarf Galaxies Eridanus IV and Centaurus I

2023· preprint· en· W4386044317 on OpenAlexafffund
Mairead Heiger, T. S. Li, Andrew B. Pace, Joshua D. Simon, Alexander P. Ji, Anirudh Chiti, Clécio R. Bom, J. A. Carballo-Bello, Jeffrey L. Carlin, W. Cerny, Yumi Choi, A. Drlica-Wagner, D. J. James, C. E. Martínez-Vázquez, G. E. Medina, Burçı̇n Mutlu-Pakdı̇l, M. Navabi, Noelia E. D. Noël, J. D. Sakowska, Guy S. Stringfellow

Bibliographic record

VenuearXiv (Cornell University) · 2023
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsCanadian Institute for Theoretical Astrophysics
FundersSLAC National Accelerator LaboratoryArgonne National LaboratoryIntegrated Electronics Engineering Center, Binghamton UniversityNatural Sciences and Engineering Research Council of CanadaNational Centre for Supercomputing ApplicationsEidgenössische Technische Hochschule ZürichInstitut de Física d'Altes EnergiesUniversity of SussexCenter for Cosmology and Astroparticle Physics, Ohio State UniversityCentro de Investigaciones Energéticas, Medioambientales y TecnológicasConselho Nacional de Desenvolvimento Científico e TecnológicoDeutsche ForschungsgemeinschaftUniversity of Illinois at Urbana-ChampaignLawrence Berkeley National LaboratoryUniversity of PennsylvaniaFinanciadora de Estudos e ProjetosFundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de JaneiroYale UniversityUniversity of PortsmouthKavli Institute for Cosmological Physics, University of ChicagoUniversity of California, Santa CruzOhio State UniversityUniversity College LondonNational Aeronautics and Space AdministrationU.S. Department of EnergyEuropean Space AgencyFermilabLaboratory Directed Research and DevelopmentNational Science FoundationScience and Technology Facilities Council
KeywordsPhysicsMetallicityAstrophysicsVelocity dispersionGalaxyStarsDwarf galaxySpectroscopyMilky WayAstronomy

Abstract

fetched live from OpenAlex

We present a spectroscopic analysis of Eridanus IV (Eri IV) and Centaurus I (Cen I), two ultra-faint dwarf galaxies of the Milky Way. Using IMACS/Magellan spectroscopy, we identify 28 member stars of Eri IV and 34 member stars of Cen I. For Eri IV, we measure a systemic velocity of $v_{sys} = -31.5^{+1.3}_{-1.2}\:\mathrm{km\:s^{-1}}$ and velocity dispersion $σ_{v}= 6.1^{+1.2}_{-0.9}\:\mathrm{km\:s^{-1}}$. Additionally, we measure the metallicities of 16 member stars of Eri IV. We find a metallicity of $\mathrm{[Fe/H]}=-2.87^{+0.08}_{-0.07}$ and resolve a dispersion of $σ_{\mathrm{[Fe/H]}} = 0.20\pm0.09$. The mean metallicity is marginally lower than all other known ultra-faint dwarf galaxies, making it one of the most metal-poor galaxies discovered thus far. Eri IV also has a somewhat unusual right-skewed metallicity distribution. For Cen I, we find a velocity $v_{sys} = 44.9\pm0.8\:\mathrm{km\:s^{-1}}$ and velocity dispersion $σ_{v} = 4.2^{+0.6}_{-0.5} \:\mathrm{km\:s^{-1}}$. We measure the metallicities of 27 member stars of Cen I, and find a mean metallicity $\mathrm{[Fe/H]} = -2.57\pm0.08$ and metallicity dispersion $σ_{\mathrm{[Fe/H]}} = 0.38^{+0.07}_{-0.05}$. We calculate the systemic proper motion, orbit, and the astrophysical J-factor for each system, the latter of which indicates that Eri IV is a good target for indirect dark matter detection. We also find no strong evidence for tidal stripping of Cen I or Eri IV. Overall, our measurements confirm that Eri IV and Cen I are dark matter-dominated galaxies with properties largely consistent with other known ultra-faint dwarf galaxies. The low metallicity, right-skewed metallicity distribution, and high J-factor make Eri IV an especially interesting candidate for further followup.

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)0.000
metaresearch head score (Gemma)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.042
GPT teacher head0.199
Teacher spread0.157 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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