entropy-core-problem

Repository for suppelementary material from my publications on the entropy core problem

https://github.com/edoaltamura/entropy-core-problem

Science Score: 44.0%

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Keywords

cosmology data-science galaxy-clusters high-performance-computing hydrodynamics simulations
Last synced: 6 months ago · JSON representation ·

Repository

Repository for suppelementary material from my publications on the entropy core problem

Basic Info
  • Host: GitHub
  • Owner: edoaltamura
  • License: cc0-1.0
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 853 KB
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Topics
cosmology data-science galaxy-clusters high-performance-computing hydrodynamics simulations
Created over 3 years ago · Last pushed almost 2 years ago
Metadata Files
Readme License Citation

README.md

Entropy cores Entropy cores MIT License PyPI - Python Version DiRAC Stargazers Issues

Logo

The entropy core problem

Data products from the study by Altamura et al. (2022)

Profiles

The data are supplied in hdf5 format. They can be inspectes with h5dump, HDFView and can be read programmatically using the supplied file load_data.py.

load_data.py organises the data in categories through the following classes: - RefModelExtendedSample - PropertiesReducedSample - ProfilesReducedSample

For each class, the data structure is the same as in the hdf5 files and can be navigated using class attributes. The datasets already include the units from the unyt module.

This is an example code to plot the profile for the group (VR2915) at high resolution (+1res in the hdf5 file or plus_1res in the class instance attributes), run with the reference EAGLE-like model (Ref). ```python from load_data import ProfilesReducedSample

profiles = ProfilesReducedSample() entropyprofile = profiles.data.VR2915plus1res.Ref.entropyprofile

The entropy profile is in dimensionless units, normalised to the self-similar scaling $K_{500}$

print(entropy_profile)

The radial distance of the shells used to bin the particles is scaled by $r_{500}$

radialbincentres = profiles.data.VR2915plus1res.Ref.radialbincentres

from matplotlib import pyplot as plt

To display the dimensionless profile, use matplotlib

plt.plot(radialbincentres, entropyprofile) plt.xlabel(r"$r/r{500}$") plt.ylabel(r"$K/K_{500}$") plt.show() ```

Data structure: RefModelExtendedSample

text RefModelExtendedSample() | |-- resolution_minus_8res | |-- entropy_profile | |-- gas_fraction | |-- m500 | |-- radial_bin_centres | |-- star_fraction | |-- VR_numbers | |-- resolution_plus_1res |-- ...

Data structure: PropertiesReducedSample

text PropertiesReducedSample() | |-- redshift_0 | |-- VR18_minus_1res | | |-- AGNdT8 | | | |-- entropy_core | | | |-- fbary {Baryon fraction == fgas + fstar} | | | |-- fgas | | | |-- fstar | | | |-- m500 | | | |-- mbh {Mass of the SMBH} | | | |-- mgas {Mass of the hot gas inside r500} | | | |-- mstar_100kpc {Stellar mass of the BCG} | | | |-- ssfr_100kpc {1Gyr-averaged specific star-formation rate in the BCG} | | | | | |-- AGNdT9 [same fields] | | |-- Bipolar [...] | | |-- Isotropic [...] | | |-- Random [...] | | |-- Ref [...] | | |-- alpha0 [...] | | |-- noAGN [...] | | |-- noMetalCooling [...] | | |-- noSN [...] | | | |-- VR18_plus_8res | | |-- ... | | | |-- VR2915_plus_1res | | |-- ... | | | |-- VR2915_minus_8res | |-- ... | | |-- redshift_1 |-- ...

Data structure: ProfilesReducedSample

text ProfilesReducedSample() | |-- VR18_minus_1res | |-- AGNdT8 | | |-- density_profile | | |-- entropy_profile | | |-- temperature_profile | | |-- radial_bin_centers | | | |-- AGNdT9 [same fields] | |-- Bipolar [...] | |-- Isotropic [...] | |-- Random [...] | |-- Ref [...] | |-- alpha0 [...] | |-- noAGN [...] | |-- noMetalCooling [...] | |-- noSN [...] | |-- VR18_plus_8res | |-- ... | |-- VR2915_plus_1res | |-- ... | |-- VR2915_minus_8res |-- ...

Raw simulation data for the simulated cluster

The raw data for the simulated cluster at $z=0$ run with the Ref model is publicly accessible and can be downloaded from Zenodo at this link.

If you use these raw data files for your work, please consider citing the MNRAS paper, as well as the Zenodo dataset with this BibTeX handle: text @dataset{edoardo_altamura_2023_8410619, author = {Edoardo Altamura}, title = {{Simulated galaxy cluster data at $z=0$ demonstrating the entropy core problem with the SWIFT-EAGLE galaxy formation model}}, month = oct, year = 2023, note = {{Main snapshot data: snap\_2640.hdf5 Main catalogue data: snap\_2640.properties}}, publisher = {Zenodo}, version = {1.0}, doi = {10.5281/zenodo.8410619}, url = {https://doi.org/10.5281/zenodo.8410619} }

Owner

  • Name: Edoardo Altamura
  • Login: edoaltamura
  • Kind: user
  • Location: Manchester
  • Company: Jodrell Bank Centre for Astrophysics

Researcher in computational cosmology and HPC at the University of Manchester. Virgo Consortium and ExCALIBUR Collaboration associate.

Citation (CITATION.bib)

@article{10.1093/mnras/stad342,
    author = {Altamura, Edoardo and Kay, Scott T and Bower, Richard G and Schaller, Matthieu and Bahé, Yannick M and Schaye, Joop and Borrow, Josh and Towler, Imogen},
    title = "{EAGLE-like simulation models do not solve the entropy core problem in groups and clusters of galaxies}",
    journal = {Monthly Notices of the Royal Astronomical Society},
    volume = {520},
    number = {2},
    pages = {3164-3186},
    year = {2023},
    month = {02},
    abstract = "{Recent high-resolution cosmological hydrodynamic simulations run with a variety of codes systematically predict large amounts of entropy in the intra-cluster medium at low redshift, leading to flat entropy profiles and a suppressed cool-core population. This prediction is at odds with X-ray observations of groups and clusters. We use a new implementation of the EAGLE galaxy formation model to investigate the sensitivity of the central entropy and the shape of the profiles to changes in the sub-grid model applied to a suite of zoom-in cosmological simulations of a group of mass M500 = 8.8 × 1012 M⊙ and a cluster of mass 2.9 × 1014 M⊙. Using our reference model, calibrated to match the stellar mass function of field galaxies, we confirm that our simulated groups and clusters contain hot gas with too high entropy in their cores. Additional simulations run without artificial conduction, metal cooling or active galactic nuclei (AGN) feedback produce lower entropy levels but still fail to reproduce observed profiles. Conversely, the two objects run without supernova feedback show a significant entropy increase which can be attributed to excessive cooling and star formation. Varying the AGN heating temperature does not greatly affect the profile shape, but only the overall normalization. Finally, we compared runs with four AGN heating schemes and obtained similar profiles, with the exception of bipolar AGN heating, which produces a higher and more uniform entropy distribution. Our study leaves open the question of whether the entropy core problem in simulations, and particularly the lack of power-law cool-core profiles, arise from incorrect physical assumptions, missing physical processes, or insufficient numerical resolution.}",
    issn = {0035-8711},
    doi = {10.1093/mnras/stad342},
    url = {https://doi.org/10.1093/mnras/stad342},
    eprint = {https://academic.oup.com/mnras/article-pdf/520/2/3164/49516994/stad342.pdf},
}

@PHDTHESIS{2023PhDT.........8A,
       author = {{Altamura}, Edoardo},
        title = "{Building models of the Universe with hydrodynamic simulations}",
     keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies, Physics - Computational Physics, Physics - Fluid Dynamics},
       school = {University of Manchester, UK},
         year = 2023,
        month = dec,
       adsurl = {https://ui.adsabs.harvard.edu/abs/2023PhDT.........8A},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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