Hintze Centre for Astrophysical Surveys

Abstract:

Abstract Radio emission during X-ray binary outbursts is dominated by synchrotron radiation from relativistic jets, but is usually studied through total-intensity diagnostics such as flux density, spectra, variability, and proper motion. Radio spectropolarimetry provides a complementary probe of the magneto-ionic plasma through Faraday rotation and depolarisation. When the Faraday rotating material is local to the source, these effects can constrain the jet plasma composition and mass content, but this approach is rarely applied to transient jetted sources. We present MeerKAT L-band spectropolarimetry of the black hole X-ray binary Swift J1727 during its 2023 outburst, focusing on the brightest radio flaring interval, when relativistic jets were being launched intermittently. Using multiple spectropolarimetric techniques, we identify transient Faraday-complex structure coincident with the major radio flares. The close temporal association with the flaring activity, together with the stability of the foreground Faraday screen, favours an origin local to the jet rather than in the ISM or in a separate local screen external to the emitting plasma. Since internal Faraday rotation is suppressed in a pure electron–positron plasma, the data favour a dominant electron–proton component. Interpreting the characteristic Faraday thickness as internal rotation, and anchoring the magnetic-field and size scales with synchrotron self-absorption arguments, we infer a characteristic Faraday-rotating mass of order Mrot ~ 1021 g, corresponding to only a small fraction, frot ~ 10−3, of the accreted mass available during the flare. These results show that time-domain spectropolarimetry can turn transient Faraday complexity into a diagnostic of jet composition, mass loading, and plasma evolution in X-ray binary outbursts, and potentially other transient jetted sources.

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.