The data volumes generated by theWALLABY atomic Hydrogen (HI) survey using the Australian Square Kilometre Array Pathfinder (ASKAP) necessitate greater automation and reliable automation in the task of source-finding and cataloguing. To this end, we introduce and explore a novel deep learning framework for detecting low Signal-to-Noise Ratio (SNR) HI sources in an automated fashion. Specifically, our proposed method provides an automated process for separating true HI detections from false positives when used in combination with the Source Finding Application (SoFiA) output candidate catalogues. Leveraging the spatial and depth capabilities of 3D ConvolutionalNeuralNetworks (CNNs), our method is specifically designed to recognize patterns and features in three-dimensional space, making it uniquely suited for rejecting false positive sources in low SNR scenarios generated by conventional linear methods. As a result, our approach is significantly more accurate in source detection and results in considerably fewer false detections compared to previous linear statistics-based source finding algorithms. Performance tests using mock galaxies injected into real ASKAP data cubes reveal our method’s capability to achieve near-100% completeness and reliability at a relatively low integrated SNR ∼ 3 – 5. An at-scale version of this tool will greatly maximise the science output from the upcoming widefield HI surveys.

We have conducted a widefield, wideband, snapshot survey using the Australian SKA Pathfinder (ASKAP) referred to as the Rapid ASKAP Continuum Survey (RACS). RACS covers ≈ 90% of the sky, with multiple observing epochs in three frequency bands sampling the ASKAP frequency range of 700–1 800 MHz. This paper describes the third major epoch at 1 655.5 MHz, RACS-high, and the subsequent imaging and catalogue data release. The RACS-high observations at 1 655.5MHz are otherwise similar to the previously released RACS-mid (at 1367.5 MHz), and were calibrated and imaged with minimal changes. From the 1 493 images covering the sky up to declination ≈ +48°, we present a catalogue of 2 677 509 radio sources. The catalogue is constructed from images with a median root-mean-square noise of ≈ 195 μJy PSF−1 (point-spread function) and a median angular resolution of 11″. 8 × 8″. 1. The overall reliability of the catalogue is estimated to be 99.18 %, and we find a decrease in reliability as angular resolution improves. We estimate the brightness scale to be accurate to 10 %, and the astrometric accuracy to be within ≈ 0″. 6 in right ascension and ≈ 0″. 7 in declination after correction of a systematic declination-dependent offset. All data products from RACS-high, including calibrated visibility datasets, images from individual observations, full-sensitivity mosaics, and the all-sky catalogue are available at the CSIRO ASKAP Science Data Archive.

Researchers regularly use large survey studies to examine public political opinion. Surveys running over days and months will necessarily incorporate religious occasions that can introduce variation in public opinion. Using recent survey data from Israel, this study demonstrates that giving surveys on religious occasions (e.g., the Sabbath, Hannukah, Sukkot) can elicit different opinion responses. These effects are found among both religious and non-religious respondents. While incorporating these fluctuations is realistic in longer-term surveys, surveys fielded in a short window inadvertently drawing heavily on a holiday or holy day sample may bias their findings. This study thus urges researchers to be cognizant of ambient religious context when conducting survey studies.

We examine the energy distribution of the fast radio burst (FRB) population using a well-defined sample of 63 FRBs from the Australian Square Kilometre Array Pathfinder (ASKAP) radio telescope, 28 of which are localised to a host galaxy. We apply the luminosity-volume ($V/V_{\mathrm{max}}$) test to examine the distribution of these transient sources, accounting for cosmological and instrumental effects, and determine the energy distribution for the sampled population over the redshift range $0.01 \lesssim z \lesssim 1.02$. We find the distribution between $10^{23}$ and $10^{26}$ J Hz$^{-1}$ to be consistent with both a pure power-law with differential slope $\gamma=-1.96 \pm 0.15$, and a Schechter function with $\gamma = -1.82 \pm 0.12$ and downturn energy $E_\mathrm{max} \sim 6.3 \, \times 10^{25}$ J Hz$^{-1}$. We identify systematic effects which currently limit our ability to probe the luminosity function outside this range and give a prescription for their treatment. Finally, we find that with the current dataset, we are unable to distinguish between the evolutionary and spectral models considered in this work.

When surveys contain direct questions about sensitive topics, participants may not provide their true answers. Indirect question techniques incentivize truthful answers by concealing participants’ responses in various ways. The Crosswise Model aims to do this by pairing a sensitive target item with a non-sensitive baseline item, and only asking participants to indicate whether their responses to the two items are the same or different. Selection of the baseline item is crucial to guarantee participants’ perceived and actual privacy and to enable reliable estimates of the sensitive trait. This research makes the following contributions. First, it describes an integrated methodology to select the baseline item, based on conceptual and statistical considerations. The resulting methodology distinguishes four statistical models. Second, it proposes novel Bayesian estimation methods to implement these models. Third, it shows that the new models introduced here improve efficiency over common applications of the Crosswise Model and may relax the required statistical assumptions. These three contributions facilitate applying the methodology in a variety of settings. An empirical application on attitudes toward LGBT issues shows the potential of the Crosswise Model. An interactive app, Python and MATLAB codes support broader adoption of the model.

It is commonly held that even where questionnaire response is poor, correlational studies are affected only by loss of degrees of freedom or precision. We show that this supposition is not true. If the decision to respond is correlated with a substantive variable of interest, then regression or analysis of variance methods based upon the questionnaire results may be adversely affected by self-selection bias. Moreover such bias may arise even where response is 100%. The problem in both cases arises where selection information is passed to the score indirectly via the disturbance or individual effects, rather than entirely via the observable explanatory variables. We suggest tests for the ensuing self-selection bias and possible ways of handling the ensuing problems of inference.

I develop a survey method for estimating social influence over individual political expression, by combining the content-richness of document scaling with the flexibility of survey research. I introduce the “What Would You Say?” question, which measures self-reported usage of political catchphrases in a hypothetical social context, which I manipulate in a between-subjects experiment. Using Wordsticks, an ordinal item response theory model inspired by Wordfish, I estimate each respondent’s lexical ideology and outspokenness, scaling their political lexicon in a two-dimensional space. I then identify self-censorship and preference falsification as causal effects of social context on respondents’ outspokenness and lexical ideology, respectively. This improves upon existing survey measures of political expression: it avoids conflating expressive behavior with populist attitudes, it defines preference falsification in terms of code-switching, and it moves beyond trait measures of self-censorship, to characterize relative shifts in the content of expression between different contexts. I validate the method and present experiments demonstrating its application to contemporary concerns about self-censorship and polarization, and I conclude by discussing its interpretation and future uses.

A key objective for upcoming surveys, and when re-analysing archival data, is the identification of variable stellar sources. However, the selection of these sources is often complicated by the unavailability of light curve data. Utilising a self-organising map (SOM), we demonstrate the selection of diverse variable source types from a catalogue of variable and non-variable SDSS Stripe 82 sources whilst employing only the median $u-g$, $g-r$, $r-i$, and $i-z$ photometric colours for each source as input, without using source magnitudes. This includes the separation of main sequence variable stars that are otherwise degenerate with non-variable sources ($u-g$,$g-r$) and ($r-i$,$i-z$) colour-spaces. We separate variable sources on the main sequence from all other variable and non-variable sources with a purity of $80.0\%$ and completeness of $25.1\%$, figures which can be modified depending on the application. We also explore the varying ability of the same method to simultaneously select other types of variable sources from the heterogeneous sample, including variable quasars and RR-Lyrae stars. The demonstrated ability of this method to select variable main sequence stars in colour-space holds promise for application in future survey reduction pipelines and for the analysis of archival data, where light curves may not be available or may be prohibitively expensive to obtain.

This paper distinguishes news about short-lived events from news about changes in longer term prospects using surveys of expectations. Employing a multivariate GARCH-in-Mean model for the US, the paper illustrates how the different types of news influence business cycle dynamics. The influence of transitory output shocks can be relatively large on impact but gradually diminishes over two to three years. Permanent shocks drive the business cycle, generating immediate stock price reactions and gradually building output effects, although they have more immediate output effects during recessions through the uncertainties they create. Markedly different macroeconomic dynamics are found if these explicitly identified types of news or uncertainty feedbacks are omitted from the analysis.

For the vast majority of us salary is one of the key reasons we work. With bills to pay and mouths to feed we want to know we are being fairly compensated for the jobs we do. One way to check this is to use the BIALL Annual Salary Survey which considers not only salaries but also working conditions and benefits, offering a comparison to similar organisations and roles. In this article Julie Christmas, Claire Mazer and the team from CB Resourcing highlight some of the findings from the 2023 survey and consider the reasons behind these.

Survey experiments often yield intention-to-treat effects that are either statistically and/or practically “non-significant.” There has been a commendable shift toward publishing such results, either to avoid the “file drawer problem” and/or to encourage studies that conclude in favor of the null hypothesis. But how can researchers more confidently adjudicate between true, versus erroneous, nonsignificant results? Guidance on this critically important question has yet to be synthesized into a single, comprehensive text. The present essay therefore highlights seven “alternative explanations” that can lead to (erroneous) nonsignificant findings. It details how researchers can more rigorously anticipate and investigate these alternative explanations in the design and analysis stages of their studies, and also offers recommendations for subsequent studies. Researchers are thus provided with a set of strategies for better designing their experiments, and more thoroughly investigating their survey-experimental data, before concluding that a given result is indicative of “no significant effect.”