Hintze Centre for Astrophysical Surveys

Abstract:

Abstract We report multiwavelength studies of the transient EP250916a, detected by the Einstein Probe on 2025 September 16. Located at low Galactic latitude, the source exhibited a rapid X-ray brightening, reaching an unabsorbed 0.5–10 keV flux of (6.4 ± 0.1) × 10 −10  erg cm −2  s −1 , followed by a plateau and a two-stage decay lasting over 40 days. Swift/X-Ray Telescope (XRT) monitoring shows a persistently hard spectrum (Γ ≈ 1.6–2.2) with only modest softening during decay, while a Nuclear Spectroscopic Telescope Array (NuSTAR)  observation confirms a hard-state continuum extending up to 70 keV. Timing analysis of XMM-Newton data reveals a weak quasiperiodic oscillation (QPO) at ∼13 Hz. No other coherent pulsations or thermonuclear bursts are detected. Broadband spectral modeling favors a nonthermal power-law continuum with partial-covering absorption and shows no significant thermal disk component. Optical imaging obtained with Nordic Optical Telescope, Las Campanas Observatory, and GaiaDR3 identifies two faint sources within the 2″ Swift/XRT positional uncertainty. A MeerKAT observation at 1.28 GHz yielded no radio counterpart, with a 3 σ upper limit of 60  μ Jy beam −1 . The combination of a long-lasting outburst, a hard nonthermal X-ray spectrum, a weak QPO detection, the absence of coherent timing features, and faint potential optical counterparts disfavors a stellar-flare or extragalactic origin and supports an accreting compact-object scenario. Comparisons with similar faint, hard-state transients place EP250916a within a growing population of low-luminosity, hard-state black hole X-ray binary candidates.

Abstract:

We present the results of the single-component Sérsic profile fitting for the magnitude-limited sample of I E < 23 galaxies within the 63.1 deg 2 area of the Euclid Quick Data Release (Q1). The associated morphological catalogue includes two sets of structural parameters fitted using SourceXtractor++ : one for VIS I E images and one for a combination of three NISP images in Y E , J E , and H E bands. We compared the resulting Sérsic parameters to other morphological measurements provided in the Q1 data release and to the equivalent parameters based on higher-resolution Hubble Space Telescope imaging. These comparisons confirmed the consistency and the reliability of the fits to Q1 data. Our analysis of colour gradients shows that NISP profiles systematically have smaller effective radii ( R e ) and larger Sérsic indices ( n ) than in VIS. In addition, we highlight trends in NISP-to-VIS parameter ratios with both magnitude and n VIS . From the 2D bimodality of the ( u − r ) colour-log( n ) plane, we defined a ( u − r ) lim ( n ) that separates early- and late-type galaxies (ETGs and LTGs). We used the two sub-populations to examine the variations of n across well-known scaling relations at z < 1. The ETGs display a steeper size–stellar mass relation than the LTGs, indicating a difference in the main drivers of their mass assembly. Similarly, LTGs and ETGs occupy different parts of the stellar mass–star-formation rate plane, with ETGs at higher masses than LTGs and further below the main sequence of star-forming galaxies. This clear separation highlights the link known between the shutdown of star formation and morphological transformations in the Euclid imaging data set. In conclusion, our analysis demonstrates both the robustness of the Sérsic fits available in the Q1 morphological catalogue and the wealth of information they provide for studies of galaxy evolution with Euclid .

Abstract:

The Euclid Wide Survey (EWS) is expected to identify in the order of 100 000 galaxy-galaxy strong lenses across 14 000deg 2 . The Euclid Quick Data Release (Q1) of 63.1deg 2 Euclid images provides an excellent opportunity to test our lens-finding ability, and to verify the anticipated lens frequency in the EWS. Following the Q1 data release, eight machine learning networks from five teams were applied to approximately one million images. This was followed by a citizen science inspection of a subset of around 100 000 images, of which 65% received high network scores, with the remainder randomly selected. The top scoring outputs were inspected by experts to establish confident (grade A), likely (grade B), possible (grade C), and unlikely lenses. In this paper we combine the citizen science and machine learning classifiers into an ensemble, demonstrating that a combined approach can produce a purer and more complete sample than the original individual classifiers. Using the expert-graded subset as ground truth, we find that this ensemble can provide a purity of 52 ± 2% (grade A/B lenses) with 50% completeness (for context, due to the rarity of lenses a random classifier would have a purity of 0.05% and the best machine learning network in this work achieved 7.3% purity for the same completeness). We discuss future lessons for the first major Euclid data release (DR1), where the big-data challenges will become more significant and will require analysing more than ∼300 million galaxies, and thus the time investment of both experts and citizens must be carefully managed.

Abstract:

We present a detailed visual morphology catalogue for the Euclid Quick Release 1 (Q1). Our catalogue includes galaxy features such as bars, spiral arms, and ongoing mergers, for the 378 000 bright ( I E < 20.5) or extended (area ≥700 pixels) galaxies in Q1. The catalogue was created by finetuning the Zoobot galaxy foundation models on annotations from an intensive one-month campaign by Galaxy Zoo volunteers. Our measurements are fully automated, and hence fully scaleable. This catalogue is the first 0.4% of the approximately 100 million galaxies where Euclid will ultimately resolve detailed morphology.

Abstract:

Strong gravitational lensing has the potential to provide a powerful probe of astrophysics and cosmology, but fewer than 1000 strong lenses have been confirmed so far. With a 0 . ″ 16 resolution covering a third of the sky, the Euclid telescope will revolutionise the identification of strong lenses, with 170 000 lenses forecasted to be discovered amongst the 1.5 billion galaxies it will observe. We present an analysis of the performance of five machine-learning models at finding strong gravitational lenses in the quick release of Euclid data (Q1) covering 63 deg 2 . The models have been validated by citizen scientists and expert visual inspection. We focus on the best-performing network: a fine-tuned version of the Zoobot pretrained model originally trained to classify galaxy morphologies in heterogeneous astronomical imaging surveys. Of the one million Q1 objects that Zoobot was tasked to find strong lenses within, the top 1000 ranked objects contain 122 grade A lenses (almost-certain lenses) and 41 grade B lenses (probable lenses). A deeper search with the five networks combined with visual inspection yielded 250 (247) grade A (B) lenses, of which 224 (182) are ranked in the top 20 000 by Zoobot . When extrapolated to the full Euclid survey, the highest ranked one million images will contain 75 000 grade A or B strong gravitational lenses.