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Abstract Machine learning methods are well established in the classification of quasars (QSOs). However, the advent of light-curve observations adds a great amount of complexity to the problem. Our goal is to use the Zwicky Transient Facility (ZTF) to create a catalog of QSOs. We process the ZTF DR20 light curves with a transformer artificial neural network and combine different surveys with extreme gradient boosting. Based on ZTFg-band and Wide-field Infrared Survey Explorer (WISE) observations, we find 4,849,574 objects classified as QSOs with confidence higher than 90% (QZO). We robustly classify objects fainter than the 5σsignal-to-noise ratio (SNR) limit atg= 20.8 by requiringg < n_obs/80 + 20.375. For 33% of QZO objects, with available WISE data, we publish redshifts with estimated error Δz/(1 + z) = 0.14. We find that ZTF classification is superior to the Pan-STARRS static bands, and on par with WISE and Gaia measurements, but the light curves provide the most important features for QSO classification in the ZTF data set. Using ZTFg-band data with at least 100 observational epochs per light curve, we obtain a 97% F1 score for QSOs. We find that with 3 day median cadence, a survey time span of at least 900 days is required to achieve a 90% QSO F1 score. However, one can obtain the same score with a survey time span of 1800 days and the median cadence prolonged to 12 days. 

In contemporary information ecologies saturated with misinformation, disinformation, and a distrust of science itself, public data communication faces significant hurdles. Although visualization research has broadened criteria for effective design, governing paradigms privilege the accurate and efficient transmission of data. Drawing on theory from linguistic anthropology, we argue that such approaches—focused on encoding and decoding propositional content—cannot fully account for how people engage with visualizations and why particular visualizations might invite adversarial or receptive responses. In this paper, we present evidence that data visualizations communicate not only semantic, propositional meaning—meaning about data—but also social, indexical meaning—meaning beyond data. From a series of ethnographically-informed interviews, we document how readers make rich and varied assessments of a visualization’s “vibes”—inferences about the social provenance of a visualization based on its design features. Furthermore, these social attributions have the power to influence reception, as readers’ decisions about how to engage with a visualization concern not only content, or even aesthetic appeal, but also their sense of alignment or disalignment with the entities they imagine to be involved in its production and circulation. We argue these inferences hinge on a function of human sign systems that has thus far been little studied in data visualization: socio-indexicality, whereby the formal features (rather than the content) of communication evoke social contexts, identities, and characteristics. Demonstrating the presence and significance of this socio-indexical function in visualization, this paper offers both a conceptual foundation and practical intervention for troubleshooting breakdowns in public data communication. 

What impressions might readers form with visualizations that go beyond the data they encode? In this paper, we build on recent work that demonstrates the socio-indexical function of visualization, showing that visualizations communicate more than the data they explicitly encode. Bridging this with prior work examining public discourse about visualizations, we contribute an analytic framework for describing inferences about an artifact’s social provenance. Via a series of attribution-elicitation surveys, we offer descriptive evidence that these social inferences: (1) can be studied asynchronously, (2) are not unique to a particular sociocultural group or a function of limited data literacy, and (3) may influence assessments of trust. Further, we demonstrate (4) how design features act in concert with the topic and underlying messages of an artifact’s data to give rise to such ‘beyond-data’ readings. We conclude by discussing the design and research implications of inferences about social provenance, and why we believe broadening the scope of research on human factors in visualization to include sociocultural phenomena can yield actionable design recommendations to address urgent challenges in public data communication. 

Abstract We examine a century of radial velocity, visual magnitude, and astrometric observations of the nearest red supergiant, Betelgeuse, in order to reexamine the century-old assertion that Betelgeuse might be a spectroscopic binary. These data reveal Betelgeuse varying stochastically over years and decades due to its boiling, convective envelope, periodically with a 5.78 yr long secondary period (LSP), and quasiperiodically from pulsations with periods of several hundred days. We show that the LSP is consistent between astrometric and radial velocity data sets, and argue that it indicates a low-mass companion to Betelgeuse, less than a solar mass, orbiting in a 2110 day period at a separation of just over twice Betelgeuse’s radius. The companion star would be nearly 20 times less massive and a million times fainter than Betelgeuse, with similar effective temperature, effectively hiding it in plain sight near one of the best-studied stars in the night sky. The astrometric data favor an edge-on binary with orbital plane aligned with Betelgeuse’s measured spin axis. Tidal spin–orbit interaction drains angular momentum from the orbit and spins up Betelgeuse, explaining the spin–orbit alignment and Betelgeuse’s anomalously rapid spin. In the future, the orbit will decay until the companion is swallowed by Betelgeuse in the next 10,000 yr. 

Undergraduate physics and astronomy students are expected to engage with scientific literature as they begin their research careers, yet reading comprehension skills are rarely explicitly taught in major courses. We seek to determine the efficacy of a reading assignment designed to improve undergraduate astronomy (or related) majors’ perceived ability to engage with research literature by using accessible summaries of current research written by experts in the field. During the 2022–2023 academic year, faculty members from six institutions incorporated reading assignments using accessible summaries from Astrobites into their undergraduate astronomy major courses, surveyed their students before and after the activities, and participated in follow-up interviews with our research team. Quantitative and qualitative survey data from 52 students show that students’ perceptions of their abilities to understand jargon and identify the main takeaways of a paper significantly improved with the use of the tested assignment template. Additionally, students reported increased confidence in their abilities within astronomy after exposure to these assignments, and instructors having valued a ready-to-use resource for incorporating reading comprehension into their pedagogy. This exploratory case study, using Astrobites-based assignments, suggests that incorporating current research into the undergraduate classroom through accessible literature summaries may increase students’ confidence and ability to engage with research literature, thereby assisting in their preparation for participation in research careers. 

ABSTRACT We present a search for luminous long-duration ambiguous nuclear transients (ANTs) similar to the unprecedented discovery of the extreme ambiguous event AT2021lwx with a $$\gt 150$$ d rise time and luminosity $$10^{45.7}$$ erg s$$^{-1}$$. We use the Lasair transient broker to search Zwicky Transient Facility (ZTF) data for transients lasting more than one year and exhibiting smooth declines. Our search returns 59 events, 7 of which we classify as ANTs assumed to be driven by accretion onto supermassive black holes. We propose the remaining 52 are stochastic variability from regular supermassive black hole accretion rather than distinct transients. We supplement the seven ANTs with three nuclear transients in ZTF that fail the light curve selection but have clear single flares and spectra that do not resemble typical active galactic nucleus. All of these 11 ANTs have a mid-infrared flare from an assumed dust echo, implying the ubiquity of dust around the black holes giving rise to ANTs. No events are more luminous than AT2021lwx, but one (ZTF19aamrjar) has twice the duration and a higher integrated energy release. On the other extreme, ZTF20abodaps reaches a luminosity close to AT2021lwx with a rise time $$\lt 20$$ d and that fades smoothly in $$\gt 600$$ d. We define a portion of rise-time versus flare amplitude space that selects ANTs with $$\sim 50$$ per cent purity against variable AGNs. We calculate a volumetric rate of $$\gtrsim 3\times 10^{-11}$$ Mpc$$^{-1}$$ yr$$^{-1}$$, consistent with the events being caused by tidal disruptions of intermediate and high-mass stars. 

Aims.TheVera C. RubinObservatory’s Legacy Survey of Space and Time (LSST) will revolutionize time-domain astronomy by detecting millions of different transients. In particular, it is expected to increase the number of known type Ia supernovae (SN Ia) by a factor of 100 compared to existing samples up to redshift ∼1.2. Such a high number of events will dramatically reduce statistical uncertainties in the analysis of the properties and rates of these objects. However, the impact of all other sources of uncertainty on the measurement of the SN Ia rate must still be evaluated. The comprehension and reduction of such uncertainties will be fundamental both for cosmology and stellar evolution studies, as measuring the SN Ia rate can put constraints on the evolutionary scenarios of different SN Ia progenitors. Methods.We used simulated data from the Dark Energy Science Collaboration (DESC) Data Challenge 2 (DC2) and LSST Data Preview 0 to measure the SN Ia rate on a 15 deg²region of the “wide-fast-deep” area. We selected a sample of SN candidates detected in difference images, associated them to the host galaxy with a specially developed algorithm, and retrieved their photometric redshifts. We then tested different light-curve classification methods, with and without redshift priors (albeit ignoring contamination from other transients, as DC2 contains only SN Ia). We discuss how the distribution in redshift measured for the SN candidates changes according to the selected host galaxy and redshift estimate. Results.We measured the SN Ia rate, analyzing the impact of uncertainties due to photometric redshift, host-galaxy association and classification on the distribution in redshift of the starting sample. We find that we are missing 17% of the SN Ia, on average, with respect to the simulated sample. As 10% of the mismatch is due to the uncertainty on the photometric redshift alone (which also affects classification when used as a prior), we conclude that this parameter is the major source of uncertainty. We discuss possible reduction of the errors in the measurement of the SN Ia rate, including synergies with other surveys, which may help us to use the rate to discriminate different progenitor models.