23 research outputs found
Fitting the integrated Spectral Energy Distributions of Galaxies
Fitting the spectral energy distributions (SEDs) of galaxies is an almost
universally used technique that has matured significantly in the last decade.
Model predictions and fitting procedures have improved significantly over this
time, attempting to keep up with the vastly increased volume and quality of
available data. We review here the field of SED fitting, describing the
modelling of ultraviolet to infrared galaxy SEDs, the creation of
multiwavelength data sets, and the methods used to fit model SEDs to observed
galaxy data sets. We touch upon the achievements and challenges in the major
ingredients of SED fitting, with a special emphasis on describing the interplay
between the quality of the available data, the quality of the available models,
and the best fitting technique to use in order to obtain a realistic
measurement as well as realistic uncertainties. We conclude that SED fitting
can be used effectively to derive a range of physical properties of galaxies,
such as redshift, stellar masses, star formation rates, dust masses, and
metallicities, with care taken not to over-interpret the available data. Yet
there still exist many issues such as estimating the age of the oldest stars in
a galaxy, finer details ofdust properties and dust-star geometry, and the
influences of poorly understood, luminous stellar types and phases. The
challenge for the coming years will be to improve both the models and the
observational data sets to resolve these uncertainties. The present review will
be made available on an interactive, moderated web page (sedfitting.org), where
the community can access and change the text. The intention is to expand the
text and keep it up to date over the coming years.Comment: 54 pages, 26 figures, Accepted for publication in Astrophysics &
Space Scienc
Content and performance of the MiniMUGA genotyping array: A new tool to improve rigor and reproducibility in mouse research
The laboratory mouse is the most widely used animal model for biomedical research, due in part to its well-annotated genome, wealth of genetic resources, and the ability to precisely manipulate its genome. Despite the importance of genetics for mouse research, genetic quality control (QC) is not standardized, in part due to the lack of cost-effective, informative, and robust platforms. Genotyping arrays are standard tools for mouse research and remain an attractive alternative even in the era of high-throughput whole-genome sequencing. Here, we describe the content and performance of a new iteration of the Mouse Universal Genotyping Array (MUGA), MiniMUGA, an array-based genetic QC platform with over 11,000 probes. In addition to robust discrimination between most classical and wild-derived laboratory strains, MiniMUGA was designed to contain features not available in other platforms: (1) chromosomal sex determination, (2) discrimination between substrains from multiple commercial vendors, (3) diagnostic SNPs for popular laboratory strains, (4) detection of constructs used in genetically engineered mice, and (5) an easy-to-interpret report summarizing these results. In-depth annotation of all probes should facilitate custom analyses by individual researchers. To determine the performance of MiniMUGA, we genotyped 6899 samples from a wide variety of genetic backgrounds. The performance of MiniMUGA compares favorably with three previous iterations of the MUGA family of arrays, both in discrimination capabilities and robustness. We have generated publicly available consensus genotypes for 241 inbred strains including classical, wild-derived, and recombinant inbred lines. Here, we also report the detection of a substantial number of XO and XXY individuals across a variety of sample types, new markers that expand the utility of reduced complexity crosses to genetic backgrounds other than C57BL/6, and the robust detection of 17 genetic constructs. We provide preliminary evidence that the array can be used to identify both partial sex chromosome duplication and mosaicism, and that diagnostic SNPs can be used to determine how long inbred mice have been bred independently from the relevant main stock. We conclude that MiniMUGA is a valuable platform for genetic QC, and an important new tool to increase the rigor and reproducibility of mouse research