36 research outputs found
Galactic and Extragalactic Samples of Supernova Remnants: How They Are Identified and What They Tell Us
Supernova remnants (SNRs) arise from the interaction between the ejecta of a
supernova (SN) explosion and the surrounding circumstellar and interstellar
medium. Some SNRs, mostly nearby SNRs, can be studied in great detail. However,
to understand SNRs as a whole, large samples of SNRs must be assembled and
studied. Here, we describe the radio, optical, and X-ray techniques which have
been used to identify and characterize almost 300 Galactic SNRs and more than
1200 extragalactic SNRs. We then discuss which types of SNRs are being found
and which are not. We examine the degree to which the luminosity functions,
surface-brightness distributions and multi-wavelength comparisons of the
samples can be interpreted to determine the class properties of SNRs and
describe efforts to establish the type of SN explosion associated with a SNR.
We conclude that in order to better understand the class properties of SNRs, it
is more important to study (and obtain additional data on) the SNRs in galaxies
with extant samples at multiple wavelength bands than it is to obtain samples
of SNRs in other galaxiesComment: Final 2016 draft of a chapter in "Handbook of Supernovae" edited by
Athem W. Alsabti and Paul Murdin. Final version available at
https://doi.org/10.1007/978-3-319-20794-0_90-
Shedding Light on the Galaxy Luminosity Function
From as early as the 1930s, astronomers have tried to quantify the
statistical nature of the evolution and large-scale structure of galaxies by
studying their luminosity distribution as a function of redshift - known as the
galaxy luminosity function (LF). Accurately constructing the LF remains a
popular and yet tricky pursuit in modern observational cosmology where the
presence of observational selection effects due to e.g. detection thresholds in
apparent magnitude, colour, surface brightness or some combination thereof can
render any given galaxy survey incomplete and thus introduce bias into the LF.
Over the last seventy years there have been numerous sophisticated
statistical approaches devised to tackle these issues; all have advantages --
but not one is perfect. This review takes a broad historical look at the key
statistical tools that have been developed over this period, discussing their
relative merits and highlighting any significant extensions and modifications.
In addition, the more generalised methods that have emerged within the last few
years are examined. These methods propose a more rigorous statistical framework
within which to determine the LF compared to some of the more traditional
methods. I also look at how photometric redshift estimations are being
incorporated into the LF methodology as well as considering the construction of
bivariate LFs. Finally, I review the ongoing development of completeness
estimators which test some of the fundamental assumptions going into LF
estimators and can be powerful probes of any residual systematic effects
inherent magnitude-redshift data.Comment: 95 pages, 23 figures, 3 tables. Now published in The Astronomy &
Astrophysics Review. This version: bring in line with A&AR format
requirements, also minor typo corrections made, additional citations and
higher rez images adde
Selecting high-dimensional mixed graphical models using minimal AIC or BIC forests
<p>Abstract</p> <p>Background</p> <p>Chow and Liu showed that the maximum likelihood tree for multivariate discrete distributions may be found using a maximum weight spanning tree algorithm, for example Kruskal's algorithm. The efficiency of the algorithm makes it tractable for high-dimensional problems.</p> <p>Results</p> <p>We extend Chow and Liu's approach in two ways: first, to find the forest optimizing a penalized likelihood criterion, for example AIC or BIC, and second, to handle data with both discrete and Gaussian variables. We apply the approach to three datasets: two from gene expression studies and the third from a genetics of gene expression study. The minimal BIC forest supplements a conventional analysis of differential expression by providing a tentative network for the differentially expressed genes. In the genetics of gene expression context the method identifies a network approximating the joint distribution of the DNA markers and the gene expression levels.</p> <p>Conclusions</p> <p>The approach is generally useful as a preliminary step towards understanding the overall dependence structure of high-dimensional discrete and/or continuous data. Trees and forests are unrealistically simple models for biological systems, but can provide useful insights. Uses include the following: identification of distinct connected components, which can be analysed separately (dimension reduction); identification of neighbourhoods for more detailed analyses; as initial models for search algorithms with a larger search space, for example decomposable models or Bayesian networks; and identification of interesting features, such as hub nodes.</p