The Square Kilometre Array (SKA) will be the largest radio astronomy observatory ever built, providing unprecedented sensitivity over a very broad frequency band from 50 MHz to 15.3 GHz. The SKA’s low frequency component (SKA-Low), which will observe in the 50–350 MHz band, will be built at the Murchison Radio-astronomy Observatory (MRO) in Western Australia. It will consist of 512 stations each composed of 256 dual-polarised antennas, and the sensitivity of an individual station is pivotal to the performance of the entire SKA-Low telescope. The answer to the question in the title is, it depends. The sensitivity of a low frequency array, such as an SKA-Low station, depends strongly on the pointing direction of the digitally formed station beam and the local sidereal time (LST), and is different for the two orthogonal polarisations of the antennas. The accurate prediction of the SKA-Low sensitivity in an arbitrary direction in the sky is crucial for future observation planning. Here, we present a sensitivity calculator for the SKA-Low radio telescope, using a database of pre-computed sensitivity values for two realisations of an SKA-Low station architecture. One realisation uses the log-periodic antennas selected for SKA-Low. The second uses a known benchmark, in the form of the bowtie dipoles of the Murchison Widefield Array. Prototype stations of both types were deployed at the MRO in 2019, and since then have been collecting commissioning and verification data. These data were used to measure the sensitivity of the stations at several frequencies and over at least 24 h intervals, and were compared to the predictions described in this paper. The sensitivity values stored in the SQLite database were pre-computed for the X, Y, and Stokes I polarisations in 10 MHz frequency steps, $\scriptsize{1/2}$ hour LST intervals, and $5^\circ$ resolution in pointing directions. The database allows users to quickly and easily estimate the sensitivity of SKA-Low for arbitrary observing parameters (your favourite object) using interactive web-based or command line interfaces. The sensitivity can be calculated using publicly available web interface (http://sensitivity.skalow.link) or a command line python package (https://github.com/marcinsokolowski/station_beam), which can also be used to calculate the sensitivity for arbitrary pointing directions, frequencies, and times without interpolations.

We present a calibration component for the Murchison Widefield Array All-Sky Virtual Observatory (MWA ASVO) utilising a newly developed PostgreSQL database of calibration solutions. Since its inauguration in 2013, the MWA has recorded over 34 petabytes of data archived at the Pawsey Supercomputing Centre. According to the MWA Data Access policy, data become publicly available 18 months after collection. Therefore, most of the archival data are now available to the public. Access to public data was provided in 2017 via the MWA ASVO interface, which allowed researchers worldwide to download MWA uncalibrated data in standard radio astronomy data formats (CASA measurement sets or UV FITS files). The addition of the MWA ASVO calibration feature opens a new, powerful avenue for researchers without a detailed knowledge of the MWA telescope and data processing to download calibrated visibility data and create images using standard radio astronomy software packages. In order to populate the database with calibration solutions from the last 6 yr we developed fully automated pipelines. A near-real-time pipeline has been used to process new calibration observations as soon as they are collected and upload calibration solutions to the database, which enables monitoring of the interferometric performance of the telescope. Based on this database, we present an analysis of the stability of the MWA calibration solutions over long time intervals.

The Belgian Repository of fundamental Atomic data and Stellar Spectra (BRASS) aims to provide one of the largest systematic and homogeneous quality assessment to date of literature atomic data required for stellar spectroscopy. By comparing state-of-the-art synthetic spectrum calculations with extremely high-quality observed benchmark spectra, we have critically evaluated fundamental atomic data, such as line wavelengths and oscillator strengths, for thousands of astrophysically-relevant transitions found in the literature and across several major atomic data repositories. These proceedings provide a short overview of the BRASS project to date, highlighting our recent efforts to investigate and quality-assess the atomic literature data pertaining to over a thousand atomic transitions present in FGK-type stellar spectra. BRASS provides all quality assessed data, theoretical spectra, and observed spectra in a new interactive database under development at brass.sdf.org.

The Hirsch h-index is now widely used as a metric to compare individual researchers. To evaluate it in the context of Australian astronomy, the h-index for every member of the Astronomical Society of Australia (ASA) is found using NASA's Astrophysics Data System Bibliographic Services. Percentiles of the h-index distribution are detailed for a variety of categories of ASA members, including students. This enables a list of the top ten Australian researchers by h-index to be produced. These top researchers have h-index values in the range 53<h<77, which is less than that recently reported for the American Astronomical Society membership. We suggest that membership of extremely large consortia such as the Sloan Digital Sky Survey may partially explain the difference. We further suggest that many student ASA members with large h-index values have probably already received their Ph.D. and need to upgrade their ASA membership status. To attempt to specify the h-index distribution relative to opportunity, we also detail the percentiles of its distribution by years since Ph.D. award date. This shows a steady increase in h-index with seniority, as can be expected.

The Parkes pulsar data archive currently provides access to 144044 data files obtained from observations carried out at the Parkes observatory since the year 1991. Around 105 files are from surveys of the sky, the remainder are observations of 775 individual pulsars and their corresponding calibration signals. Survey observations are included from the Parkes 70 cm and the Swinburne Intermediate Latitude surveys. Individual pulsar observations are included from young pulsar timing projects, the Parkes Pulsar Timing Array and from the PULSE@Parkes outreach program. The data files and access methods are compatible with Virtual Observatory protocols. This paper describes the data currently stored in the archive and presents ways in which these data can be searched and downloaded.

We present an analysis of the scientific (refereed) paper productivity of the current largest (diameter > 8m) ground-based optical (and infrared) telescopes during the ten-year period from 2000 to 2009. The telescopes for which we have gathered and analysed the scientific publication data are the two 10-m Keck telescopes, the four 8.2-m Very Large Telescopes (VLT), the two 8.1-m Gemini telescopes, the 8.2-m Subaru telescope, and the 9.2-m Hobby–Eberly Telescope (HET). We have analysed the numbers of papers published in various astronomical journals produced by using these telescopes. While the total numbers of papers from these observatories are largest for the VLT, followed by Keck, Gemini, Subaru, and HET, the number of papers produced by each component of the telescopes is largest for Keck, followed by VLT, Subaru, Gemini, and HET. In 2009, each telescope of the Keck, VLT, Gemini, Subaru, and HET observatories produced 135, 109, 93, 107, and 5 refereed papers, respectively. We have shown that each telescope of the Keck, VLT, Gemini, and Subaru observatories is producing 2.1 ± 0.9 Nature and Science papers annually and these papers make up 1.7 ± 0.8% of all refereed papers produced by using each of those telescopes. Extending this relation, we propose that this ratio of the number of Nature and Science papers to the total number of refereed papers that will be produced by future extremely large telescopes (ELTs) will remain similar. From a comparison of the publication trends of the above telescopes, we suggest that (i) having more than one telescope of the same kind at the same location and (ii) increasing the number of instruments available at the telescope are good ways to maximize the paper productivity.

In this work, two different algorithms: Linear Discriminant Analysis (LDA) and Support Vector Machines (SVMs) are combined for the classification of unresolved sources from SDSS DR8 and UKIDSS DR8. The experimental result shows that this joint approach is effective for our case.

Targeting quasar candidates is always an important task for large spectroscopic sky survey projects. Astronomers never give up thinking out effective approaches to separate quasars from stars. The previous methods on this issue almost belong to supervised methods or color-color cut. In this work, we compare the performance of a supervised method – Support Vector Machine (SVM)– with that of an unsupervised method one-class SVM. The performance of SVM is better than that of one-class SVM. But one-class SVM is an unsupervised algorithm which is helpful to recognize rare or mysterious objects. Combining supervised methods with unsupervised methods is effective to improve the performance of a single classifier.

Much of our knowledge about the solar dynamo is based on sunspot observations. It is thus desirable to extend the set of positional and morphological data of sunspots into the past. Gustav Spörer observed in Germany from Anklam (1861–1873) and Potsdam (1874–1894). He left detailed prints of sunspot groups, which we digitized and processed to mitigate artifacts left in the print by the passage of time. After careful geometrical correction, the sunspot data are now available as synoptic charts for almost 450 solar rotation periods. Individual sunspot positions can thus be precisely determined and spot areas can be accurately measured using morphological image processing techniques. These methods also allow us to determine tilt angles of active regions (Joy's law) and to assess the complexity of an active region.

We investigated the use of support vector machines (SVMs) to distinguish quasars from stars using survey databases from different wavebands. We first employed a random forest approach for selection and weighting of features. We find that SVMs are an effective method to preselect quasar candidates from multiwavelength data.

We investigate selection and weighting of features by applying a random forest algorithm to multiwavelength data. Then we employ a k-nearest neighbor method to distinguish quasars from stars. We then compare the performance of this approach based on all features, weighted features, and selected features. We find that the k-nearest neighbor approach combined with random forests effectively separates quasars from stars.