arXiv subjects

G. Eadie

Publications and source records attributed to G. Eadie.

3 recordsLinked to original sources

arXiv2025

On the relation between magnetic field strength and gas density in the interstellar medium: A multiscale analysis

The magnetic field strength to gas density relation in the interstellar medium is of fundamental importance. We present and compare Bayesian analyses of the B-n relation for two comprehensive observational data sets: a Zeeman data set and 700 observations using the Davis-Chandrasekhar-Fermi (DCF) method. Using a hierarchical Bayesian analysis we present a general, multi-scale broken power-law relation, $B=B_0(n/n_0)^α$ , with $α=α_1$ for $n n_0$, and with $B_0$ the field strength at $n_0$. For the Zeeman data we find: $α_1={0.15^{+0.06}_{-0.09}}$ for diffuse gas and $α_2 = {0.53^{+0.09}_{-0.07}}$ for dense gas with $n_0 = 4.00^{+12.7}_{-2.90} \times 10^3$ cm$^{-3}$. For the DCF data we find: $α_1={0.26^{+0.15}_{-0.15}}$ and $α_2={0.77_{-0.15}^{+0.14}}$, with $n_0=13.9^{+10.1}_{-7.30} \times 10^4$ cm$^{-3}$, where the uncertainties give 68\% credible intervals. We perform a similar analysis on nineteen numerical magnetohydrodynamic simulations covering a wide range of physical conditions from protostellar disks to dwarf and Milky Way-like galaxies, completed with the AREPO, Flash, Pencil, and Ramses codes. The resulting exponents depend on several physical factors such as dynamo effects and their time scales, turbulence, and initial seed field strength. \textcolor{red}{We find that the dwarf and Milky Way-like galaxy simulations produce results closest to the observations.

astro-ph.GA↗

arXiv2019

LRP2020: Machine Learning Advantages in Canadian Astrophysics

The application of machine learning (ML) methods to the analysis of astrophysical datasets is on the rise, particularly as the computing power and complex algorithms become more powerful and accessible. As the field of ML enjoys a continuous stream of breakthroughs, its applications demonstrate the great potential of ML, ranging from achieving tens of millions of times increase in analysis speed (e.g., modeling of gravitational lenses or analysing spectroscopic surveys) to solutions of previously unsolved problems (e.g., foreground subtraction or efficient telescope operations). The number of astronomical publications that include ML has been steadily increasing since 2010. With the advent of extremely large datasets from a new generation of surveys in the 2020s, ML methods will become an indispensable tool in astrophysics. Canada is an unambiguous world leader in the development of the field of machine learning, attracting large investments and skilled researchers to its prestigious AI Research Institutions. This provides a unique opportunity for Canada to also be a world leader in the application of machine learning in the field of astrophysics, and foster the training of a new generation of highly skilled researchers.

astro-ph.IM↗

arXiv2019

Astro2020 Science White Paper: The Next Decade of Astroinformatics and Astrostatistics

Over the past century, major advances in astronomy and astrophysics have been largely driven by improvements in instrumentation and data collection. With the amassing of high quality data from new telescopes, and especially with the advent of deep and large astronomical surveys, it is becoming clear that future advances will also rely heavily on how those data are analyzed and interpreted. New methodologies derived from advances in statistics, computer science, and machine learning are beginning to be employed in sophisticated investigations that are not only bringing forth new discoveries, but are placing them on a solid footing. Progress in wide-field sky surveys, interferometric imaging, precision cosmology, exoplanet detection and characterization, and many subfields of stellar, Galactic and extragalactic astronomy, has resulted in complex data analysis challenges that must be solved to perform scientific inference. Research in astrostatistics and astroinformatics will be necessary to develop the state-of-the-art methodology needed in astronomy. Overcoming these challenges requires dedicated, interdisciplinary research. We recommend: (1) increasing funding for interdisciplinary projects in astrostatistics and astroinformatics; (2) dedicating space and time at conferences for interdisciplinary research and promotion; (3) developing sustainable funding for long-term astrostatisics appointments; and (4) funding infrastructure development for data archives and archive support, state-of-the-art algorithms, and efficient computing.

astro-ph.IM↗