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Record W4402265314 · doi:10.1051/0004-6361/202450699

RIGEL: Simulating dwarf galaxies at solar mass resolution with radiative transfer and feedback from individual massive stars

2024· article· en· W4402265314 on OpenAlexfundno aff
Yunwei Deng, Hui Li, Boyuan Liu, Rahul Kannan, Greg L. Bryan

Bibliographic record

VenueAstronomy and Astrophysics · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstronomy and Astrophysical Research
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsPhysicsAstrophysicsRadiative transferDwarf galaxyAstronomyStarsGalaxySolar mass

Abstract

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Context. Feedback from stars in the form of radiation, stellar winds, and supernovae is crucial to regulating the star formation activity of galaxies. Dwarf galaxies are especially susceptible to these processes, making them an ideal test bed for studying the effects of stellar feedback in detail. Recent numerical models have aimed to resolve the interstellar medium (ISM) in dwarf galaxies with a very high resolution of several solar masses. However, when it comes to modeling the radiative feedback from stars, many models opt for simplified approaches instead of explicitly solving radiative transfer (RT) because of the computational complexity involved. Aims. We introduce the Realistic ISM modeling in Galaxy Evolution and Lifecycles (RIGEL) model, a novel framework to self-consistently model the effects of stellar feedback in the multiphase ISM of dwarf galaxies with explicit RT on a star-by-star basis. Methods. The RIGEL model integrates detailed implementations of feedback from individual massive stars into the state-of-the-art radiation-hydrodynamics code, AREPO-RT . It forms individual massive stars from the resolved multiphase ISM by sampling the initial mass function and tracks their evolution individually. The lifetimes, photon production rates, mass-loss rates, and wind velocities of these stars are determined by their initial masses and metallicities based on a library that incorporates a variety of stellar models. The RT equations are solved explicitly in seven spectral bins accounting for the infrared to He II ionizing bands, using a moment-base scheme with the M1 closure relation. The thermochemistry model tracks the nonequilibrium H, He chemistry as well as the equilibrium abundance of C I , C II , O I , O II , and CO in the irradiated ISM to capture the thermodynamics of all ISM phases, from cold molecular gas to hot ionized gas. Results. We evaluated the performance of the RIGEL model using 1 M ⊙ resolution simulations of isolated dwarf galaxies. We found that the star formation rate (SFR) and interstellar radiation field (ISRF) show strong positive correlations with the metallicity of the galaxy. Photoionization and photoheating can reduce the SFR by an order of magnitude by removing the available cold, dense gas fuel for star formation. The presence of ISRF also significantly changes the thermal structure of the ISM. Radiative feedback occurs immediately after the birth of massive stars and rapidly disperses the molecular clouds within 1 Myr. As a consequence, radiative feedback reduces the age spread of star clusters to less than 2 Myr, prohibits the formation of massive star clusters, and shapes the cluster initial mass function to a steep power-law form with a slope of ∼ − 2. The mass-loading factor (measured at z = 1 kpc) of the fiducial galaxy has a median of η M ∼ 50, while turning off radiative feedback reduces this factor by an order of magnitude. Conclusions. We demonstrate that RIGEL effectively captures the nonlinear coupling of early radiative feedback and supernova feedback in the multiphase ISM of dwarf galaxies. This novel framework enables the utilization of a comprehensive stellar feedback and ISM model in cosmological simulations of dwarf galaxies and various galactic environments spanning a wide dynamic range in both space and time.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.904
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.232
Teacher spread0.221 · 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 teacher head, not a consensus.

Study designOther design
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

Citations28
Published2024
Admission routes1
Has abstractyes

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