Abstract/Details

A Bayesian approach to the semi-analytic model of galaxy formation


2010 2010

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Abstract (summary)

It is believed that a wide range of physical processes conspire to shape the observed galaxy population but it remains unsure of their detailed interactions. The semi-analytic model (SAM) of galaxy formation uses multi-dimensional parameterizations of the physical processes of galaxy formation and provides a tool to constrain these underlying physical interactions. Because of the high dimensionality and large uncertainties in the model, the parametric problem of galaxy formation can be profitably tackled with a Bayesian-inference based approach, which allows one to constrain theory with data in a statistically rigorous way. In this thesis, I present a newly developed method to build SAM upon the framework of Bayesian inference. I show that, aided by advanced Markov-Chain Monte-Carlo algorithms, the method has the power to efficiently combine information from diverse data sources, rigorously establish confidence bounds on model parameters, and provide powerful probability-based methods for hypothesis test. Using various data sets (stellar mass function, conditional stellar mass function, K-band luminosity function, and cold gas mass functions) of galaxies in the local Universe, I carry out a series of Bayesian model inferences. The results show that SAM contains huge degeneracies among its parameters, indicating that some of the conclusions drawn previously with the conventional approach may not be truly valid but need to be revisited by the Bayesian approach. Second, some of the degeneracy of the model can be broken by adopting multiple data sets that constrain different aspects of the galaxy population. Third, the inferences reveal that model has challenge to simultaneously explain some important observational results, suggesting that some key physics governing the evolution of star formation and feedback may still be missing from the model. These analyses show clearly that the Bayesian inference based SAM can be used to perform systematic and statistically rigorous investigation of galaxy formation based on various observations and help to design new observations that can effectively discriminate theoretical models.

Indexing (details)


Subject
Astrophysics
Classification
0596: Astrophysics
Identifier / keyword
Pure sciences, Diverse data sources, Galaxy evolution, Galaxy formation, Galaxy population
Title
A Bayesian approach to the semi-analytic model of galaxy formation
Author
Lu, Yu
Number of pages
212
Publication year
2010
Degree date
2010
School code
0118
Source
DAI-B 71/12, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9781124320113
Advisor
Mo, Houjun
Committee member
Katz, Neal S.; Machta, Jonathan; Mo, Houjun Mo; Tripp, Todd M.; Weinberg, Martin D.
University/institution
University of Massachusetts Amherst
Department
Astronomy
University location
United States -- Massachusetts
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
3427552
ProQuest document ID
815281747
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/815281747
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