The GLEAM 200-MHz local radio luminosity function for AGN and star-forming galaxies

Galaxie

Murchison Widefield Array and XMM-Newton observations of the Galactic supernova remnant G5.9+3.1

Aims. In this paper we discuss the radio continuum and X-ray properties of the so-far poorly studied Galactic supernova remnant (SNR) G5.9 + 3.1. Methods. We present the radio spectral energy distribution (SED) of the Galactic SNR G5.9 + 3.1 obtained with the Murchison Widefield Array (MWA). Combining these new observations with the surveys at other radio continuum frequencies, we discuss the integrated radio continuum spectrum of this particular remnant. We have also analyzed an archival XMM-Newton observation, which represents the first detection of X-ray emission from this remnant. Results. The SNR SED is very well explained by a simple power-law relation. The synchrotron radio spectral index of G5.9 + 3.1 is estimated to be 0.42 ± 0.03 and the integrated flux density at 1 GHz to be around 2.7 Jy. Furthermore, we propose that the identified point radio source, located centrally inside the SNR shell, is most probably a compact remnant of the supernova explosion. The shell-like X-ray morphology of G5.9 + 3.1 as revealed by XMM-Newton broadly matches the spatial distribution of the radio emission, where the radio-bright eastern and western rims are also readily detected in the X-ray while the radio-weak northern and southern rims are weak or absent in the X-ray. Extracted MOS1+MOS2+PN spectra from the whole SNR as well as the north, east, and west rims of the SNR are fit successfully with an optically thin thermal plasma model in collisional ionization equilibrium with a column density NH ~ 0.80 × 1022 cm−2 and fitted temperatures spanning the range kT ~ 0.14–0.23 keV for all of the regions. The derived electron number densities ne for the whole SNR and the rims are also roughly comparable (ranging from ~0.20f−1∕2 to ~0.40f−1∕2 cm−3, where f is the volume filling factor). We also estimate the swept-up mass of the X-ray emitting plasma associated with G5.9+3.1 to be ~46f−1∕2 M⊙.D. Onić, M. D. Filipović, I. Bojičić, N. Hurley-Walker, B. Arbutina, T. G. Pannuti, C. Maitra, D. Urošević, F. Haberl, N. Maxted, G. F. Wong, G. Rowell, M. E. Bell, J. R. Callingham, K. S. Dwarakanath, B.-Q. For, P. J. Hancock, L. Hindson, M. Johnston-Hollitt, A. D. Kapińska, E. Lenc, B. McKinley, J. Morgan, A. R. Offringa, L. E. Porter, P. Procopio, L. Staveley-Smith, R. B. Wayth, C. Wu and Q. Zhen

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press