Hintze Centre for Astrophysical Surveys

Abstract:

Accretion onto black holes often leads to the launch of outflows that substantially influence their surrounding environments. The two primary forms of these outflows are X-ray disk winds—hot, ionized gases ejected from the accretion disk—and relativistic jets, which are collimated streams of particles often expelled along the rotational axis of the black hole. While previous studies have revealed a general association between spectral states and different types of outflow, the physical mechanisms governing wind and jet formation remain debated. Here, using coordinated NICER and MeerKAT observations of the recurrent black hole X-ray binary 4U 1630–472, we identify a clear anti-correlation between X-ray disk winds and jets: during three recent outbursts, only one type of outflow is detected at a time. Notably, this apparent exclusivity occurs even as the overall accretion luminosity remains within the range expected for a standard thin disk, characteristic of the canonical soft state. These results suggest a competition between outflow channels that may depend on how the accretion energy is partitioned between the disk and the corona. Our findings provide observational constraints on jet and wind formation in X-ray binaries and offer a fresh perspective on the interplay between different modes of accretion-driven feedback.

Abstract:

Public data release accompanying the ATLAS100 sample definition paper by Srivastav et al. (2026). The data release includes the cleaned and binned ATLAS light curves of 1729 transients in the sample. Also included is a catalog csv file with additional useful metadata for the transients in the sample, including host galaxy associations, any updated classifications, etc.

Abstract:

Abstract Early-time spectroscopy of supernovae (SNe), acquired within days of explosion, yields crucial insights into their outermost ejecta layers, facilitating the study of their environments, progenitor systems, and explosion mechanisms. Recent efforts in early discovery and follow-up of SNe have shown the potential insights that can be gained from early-time spectra. Surveys such as the Time-Domain Extragalactic Survey (TiDES), conducted with the 4-meter Multi-Object Spectroscopic Telescope (4MOST), will provide spectroscopic follow-up of transients discovered by the Legacy Survey of Space and Time (LSST). Current simulations indicate that early-time spectroscopic studies conducted with TiDES data will be limited by the current SN selection criteria. To enhance early-time SN spectroscopic studies from TiDES-like surveys, we propose a set of selection criteria focusing on young SNe (YSNe), which we define as SNe prior to −10 days before peak brightness. Utilising the Zwicky Transient Facility transient alerts, we developed criteria to select YSNe while minimising the sample’s contamination rate to 23percnt. The developed criteria were applied to LSST simulations, yielding a sample of 694 Deep Drilling Field survey SNe and 56260 Wide Fast Deep survey SNe for follow-up. We demonstrate that our criteria enables the selection of SNe at early-times, enhancing future early-time spectroscopic SN studies from TiDES-like surveys. Finally, we investigated 4MOST-like observing strategies to increase the sample of spectroscopically observed YSNe. We propose that a 4MOST-like observing strategy that follows LSST with a delay of 3 days is optimal for a TiDES-like SN survey in terms of the number of classifiable spectra obtained, while a 1 day delay is most optimal for enhancing the early-time science in conjunction with our YSN selection criteria.

Abstract:

Abstract We present morphological classifications of over 41 000 galaxies out to zphot ∼ 2.5 across six square degrees of the Euclid Deep Field North (EDFN) from the Hawaii Twenty Square Degree (H20) survey, a part of the wider Cosmic Dawn survey. Galaxy Zoo citizen scientists play a crucial role in the examination of large astronomical data sets through crowdsourced data mining of extragalactic imaging. This iteration, Galaxy Zoo: Cosmic Dawn (GZCD), saw tens of thousands of volunteers and the deep learning foundation model Zoobot collectively classify objects in ultra-deep multiband Hyper Suprime-Cam (HSC) imaging down to a depth of mHSC − i = 21.5. Here, we present the details and general analysis of this iteration, including the use of Zoobot in an active learning cycle to improve both model performance and volunteer experience, as well as the discovery of 51 new gravitational lenses in the EDFN. We also announce the public data release of the classifications for over 45 000 subjects, including more than 41 000 galaxies (median zphot of 0.42 ± 0.23), along with their associated image cutouts. This data set provides a valuable opportunity for follow-up imaging of objects in the EDFN as well as acting as a truth set for training deep learning models for application to ground-based surveys like that of the Ultraviolet Near-Infrared Optical Northern Survey (UNIONS) collaboration and the newly operational Vera C. Rubin Observatory.