Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userApproximate Bayesian Computation for Astronomical Model Analysis: A Case Study in Galaxy Demographics and Morphological Transformation at High Redshift
110
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Approximate Bayesian Computation (ABC) represents a powerful methodology for the analysis of complex stochastic systems for which the likelihood of the observed data under an arbitrary set of input parameters may be entirely intractable-the latter condition rendering useless the standard machinery of tractable likelihood-based, Bayesian statistical inference (e.g. conventional Markov Chain Monte Carlo simulation; MCMC). In this article we demonstrate the potential of ABC for astronomical model analysis by application to a case study in the morphological transformation of high redshift galaxies. To this end we develop, first, a stochastic model for the competing processes of merging and secular evolution in the early Universe; and second, through an ABC-based comparison against the observed demographics of massive (M_gal > 10^11 M_sun) galaxies (at 1.5 < z < 3) in the CANDELS/EGS dataset we derive posterior probability densities for the key parameters of this model. The Sequential Monte Carlo (SMC) implementation of ABC exhibited herein, featuring both a self-generating target sequence and self-refining MCMC kernel, is amongst the most efficient of contemporary approaches to this important statistical algorithm. We highlight as well through our chosen case study the value of careful summary statistic selection, and demonstrate two modern strategies for assessment and optimisation in this regard. Ultimately, our ABC analysis of the high redshift morphological mix returns tight constraints on the evolving merger rate in the early Universe and favours major merging (with disc survival or rapid reformation) over secular evolution as the mechanism most responsible for building up the first generation of bulges in early-type disks.
rate research
Read More
We provide a brief overview of the Galaxy Zoo and Zooniverse projects, including a short discussion of the history of, and motivation for, these projects as well as reviewing the science these innovative internet-based citizen science projects have produced so far. We briefly describe the method of applying en-masse human pattern recognition capabilities to complex data in data-intensive research. We also provide a discussion of the lessons learned from developing and running these community--based projects including thoughts on future applications of this methodology. This review is intended to give the reader a quick and simple introduction to the Zooniverse.
We derive the optimal proposal density for Approximate Bayesian Computation (ABC) using Sequential Monte Carlo (SMC) (or Population Monte Carlo, PMC). The criterion for optimality is that the SMC/PMC-ABC sampler maximise the effective number of samples per parameter proposal. The optimal proposal density represents the optimal trade-off between favoring high acceptance rate and reducing the variance of the importance weights of accepted samples. We discuss two convenient approximations of this proposal and show that the optimal proposal density gives a significant boost in the expected sampling efficiency compared to standard kernels that are in common use in the ABC literature, especially as the number of parameters increases.
Aims: We present a custom support vector machine classification package for photometric redshift estimation, including comparisons with other methods. We also explore the efficacy of including galaxy shape information in redshift estimation. Support vector machines, a type of machine learning, utilize optimization theory and supervised learning algorithms to construct predictive models based on the information content of data in a way that can treat different input features symmetrically. Methods: The custom support vector machine package we have developed is designated SPIDERz and made available to the community. As test data for evaluating performance and comparison with other methods, we apply SPIDERz to four distinct data sets: 1) the publicly available portion of the PHAT-1 catalog based on the GOODS-N field with spectroscopic redshifts in the range $z < 3.6$, 2) 14365 galaxies from the COSMOS bright survey with photometric band magnitudes, morphology, and spectroscopic redshifts inside $z < 1.4$, 3) 3048 galaxies from the overlap of COSMOS photometry and morphology with 3D-HST spectroscopy extending to $z < 3.9$, and 4) 2612 galaxies with five-band photometric magnitudes and morphology from the All-wavelength Extended Groth Strip International Survey and $z < 1.57$. Results: We find that SPIDER-z achieves results competitive with other empirical packages on the PHAT-1 data, and performs quite well in estimating redshifts with the COSMOS and AEGIS data, including in the cases of a large redshift range ($0 < z < 3.9$). We also determine from analyses with both the COSMOS and AEGIS data that the inclusion of morphological information does not have a statistically significant benefit for photometric redshift estimation with the techniques employed here.
ABCpy is a highly modular scientific library for Approximate Bayesian Computation (ABC) written in Python. The main contribution of this paper is to document a software engineering effort that enables domain scientists to easily apply ABC to their research without being ABC experts; using ABCpy they can easily run large parallel simulations without much knowledge about parallelization. Further, ABCpy enables ABC experts to easily develop new inference schemes and evaluate them in a standardized environment and to extend the library with new algorithms. These benefits come mainly from the modularity of ABCpy. We give an overview of the design of ABCpy and provide a performance evaluation concentrating on parallelization. This points us towards the inherent imbalance in some of the ABC algorithms. We develop a dynamic scheduling MPI implementation to mitigate this issue and evaluate the various ABC algorithms according to their adaptability towards high-performance computing.
3-D printing moves beyond interactive 3-D graphics and provides an excellent tool for both visual and tactile learners, since 3-D printing can now easily communicate complex geometries and full color information. Some limitations of interactive 3-D graphics are also alleviated by 3-D printable models, including issues of limited software support, portability, accessibility, and sustainability. We describe the motivations, methods, and results of our work on using 3-D printing (1) to visualize and understand the Eta Car Homunculus nebula and central binary system and (2) for astronomy outreach and education, specifically, with visually impaired students. One new result we present is the ability to 3-D print full-color models of Eta Cars colliding stellar winds. We also demonstrate how 3-D printing has helped us communicate our improved understanding of the detailed structure of Eta Cars Homunculus nebula and central binary colliding stellar winds, and their links to each other. Attached to this article are full-color 3-D printable files of both a red-blue Homunculus model and the Eta Car colliding stellar winds at orbital phase 1.045. 3-D printing could prove to be vital to how astronomers reach out and share their work with each other, the public, and new audiences.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
