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ASymba: Comparing Observations and Simulations

Mathieu Perron-Cormier

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Summer student Mishita Khurana has been using MOCHI for galaxies identified as isolated, group, and cluster centrals/satellites and examining HI asymmetries

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Investigating HI asymmetries in diff. environments

Mishita�Khurana

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Select galaxies from Simba(-C) based on measured properties from observations (gas fraction, stellar mass, SFR, MHI)...

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MIGHTEE-HI and LADUMA mock samples

…and create mock data cube of simulated galaxy and use for e.g. BTFR, asymmetry, stacking, etc. science!

Glowacki et al., in prep.

Work by �Marcin Glowacki!

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See also Nadine Hank

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WALLABY

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Observations are not perfect

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WALLABY is the best statistical sample, but you must overcome noise/resolution

# Resolution Elements

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Context - Cosmological Simulations

  • Big simulations where we try to simulate the universe right
  • Simulations are just starting to be able to model cold gas
  • Are simulation predictions on HI morphology good?

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Borrow, J., Anglés-Alcázar, D., & Davé, R. (2019).

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What

  • ASymba
  • SIMBA, SIMBA-C simulation
    • HI, temperature floor
  • Asymmetries
    • Robust to noise and resolution
    • (if you know what you are doing)

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Types of Asymmetry

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Pipeline

Sim Data

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WALLABY detections

Matched sim data

Mock Observation

Mock Samples

Morphology Characterisation

Mock

Sample

Morphologies

Morphology Characterisation

WALLABY

Morphologies

COMPARE!!!

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With simple mass matching, we can create a WALLABY-like sample

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MOCHI

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Quality Spectra at Low Resolutions

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MOCHI

  • SPH / MFM
  • Work in progress: Voronoi cells
    • Technically works, just very memory intensive

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See more at Observing HI in the Simulations Session!

Github

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Comparing WALLABY with SIMBA mock

WALLABY

Simba

  • 68th
  • 95th
  • 99th percentiles

Limited by numbers

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The future is Bayesian

Even with greater numbers, having more reliability allows you to select sub-samples (ex: environment matching)

Learn more at Source Characterisation Session!

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ASymba