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Record W1974463200 · doi:10.1051/0004-6361:20065295

Cosmic ray feedback in hydrodynamical simulations of galaxy formation

2007· article· en· W1974463200 on OpenAlexaff
M. Jubelgas, Volker Springel, T. A. Enßlin, Christoph Pfrommer

Bibliographic record

VenueAstronomy and Astrophysics · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsCanadian Institute for Theoretical Astrophysics
FundersMax-Planck-Gesellschaft
KeywordsPhysicsAstrophysicsCosmic rayGalaxySmoothed-particle hydrodynamicsStructure formationStar formationGalaxy formation and evolutionPopulationAstronomy

Abstract

fetched live from OpenAlex

It is well known that cosmic rays contribute significantly to the pressure of the interstellar medium in our own Galaxy, suggesting that they may play an important role in regulating star formation during the formation and evolution of galaxies. We here discuss a novel numerical treatment of the physics of cosmic rays and its implementation in the parallel smoothed particle hydrodynamics code GADGET-2. In our methodology, the non-thermal cosmic ray population of each gaseous fluid element is approximated by a simple power law spectrum in particle momentum, characterized by an amplitude, a cut-off, and a fixed slope. Adiabatic compression and a number of physical source and sink terms are modelled which modify the cosmic ray pressure of each particle. The most important sources considered are injection by supernovae and diffusive shock acceleration, while the primary sinks are thermalization by Coulomb interactions, and catastrophic losses by hadronic interactions. We also include diffusion of cosmic rays. Using a number of test problems, we show that our scheme is numerically robust and efficient, allowing us to carry out the first cosmological structure formation simulations that account for cosmic ray physics, together with radiative cooling and star formation. In simulations of isolated galaxies, we find that cosmic rays can significantly reduce the star formation efficiencies of small galaxies, with virial velocities below ~, an effect that becomes progressively stronger towards low-mass scales. In cosmological simulations of the formation of dwarf galaxies at high redshift, we find that the total mass-to-light ratio of small halos and the faint end of the luminosity function are affected. The latter becomes slightly flatter. When cosmic ray acceleration in shock waves is followed as well, we find that up to of the energy dissipated at structure formation shocks can appear as cosmic ray pressure at redshifts around , but this fraction drops to ~ at low redshifts when the shock distribution becomes increasingly dominated by lower Mach numbers. Despite this large cosmic ray energy content in the high-redshift intergalactic medium, the flux power spectrum of the Lyman-α forest is only affected on very small scales of , and at a weak level of . Within virialized objects, we find lower contributions of CR-pressure, due to the increased efficiency of loss processes at higher densities, the lower Mach numbers of shocks inside halos, and the softer adiabatic index of CRs, which disadvantages them when a composite of thermal gas and cosmic rays is adiabatically compressed. The total energy in cosmic rays relative to the thermal energy within the virial radius drops from 20% for halos to 5% for rich galaxy clusters of mass in non-radiative simulations. Interestingly, the lower effective adiabatic index also increases the compressibility of the intrahalo medium, an effect that slightly increases the central concentration of the gas and the baryon fraction within the virial radius. We find that this can enhance the cooling rate onto central cluster galaxies, even though the galaxies in the cluster periphery become slightly less luminous as a result of cosmic ray feedback.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.213
Teacher spread0.207 · 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 designSimulation or modeling
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

Citations200
Published2007
Admission routes1
Has abstractyes

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