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Activity Number:
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265
- Innovations in Statistics for Astronomy and Space Physics
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 4, 2020 : 1:00 PM to 2:50 PM
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Sponsor:
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SSC (Statistical Society of Canada)
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Abstract #312249
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Title:
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Gibbs Point Process Model for Objects in the Star Formation Complexes of M33
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Author(s):
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Dayi Li* and Pauline Barmby and Ian McLeod
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Companies:
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Western University and Western University and Western University
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Keywords:
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star formation;
galaxies: individual: M33;
spatial statistics;
Gibbs point processes;
statistical modelling;
Bayesian inference
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Abstract:
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We demonstrate the power of Gibbs point process models in the spatial statistics literature when applied to stellar population studies. We conduct a rigorous analysis of the empirical spatial distributions of objects in the star formation complexes of M33, including giant molecular clouds (GMCs), and young stellar cluster candidates (YSCCs). We choose a hierarchical model structure from GMCs to YSCCs based on the natural formation hierarchy between them. This approach circumvents the limitations of the empirical two-point correlation function analysis by naturally accounting for the inhomogeneity present in the distribution of YSCCs. We also investigate the effects of GMCs' properties on their spatial distributions. We confirm that the distribution of GMCs and YSCCs are highly correlated. We found that the spatial distributions of YSCCs reaches a peak of clustering pattern at $\sim$ 250 pc scale compared to a Poisson process and this clustering mainly occurs at regions where the galactocentric distance $\gtrsim 4.5$ kpc. Furthermore, the galactocentric distance of GMCs and the mass of GMCs have a strong positive effect on the correlation strength between GMCs and YSCCs.
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Authors who are presenting talks have a * after their name.
