Hintze Centre for Astrophysical Surveys

Abstract:

The Hobby–Eberly Telescope Dark Energy Experiment (HETDEX) is a wide-field, integral-field spectroscopic survey designed to map the large-scale distribution of Lyα-emitting galaxies (LAEs) at 1.88 < z < 3.52 and constrain dark energy at cosmic noon. Using the 10 m Hobby–Eberly Telescope and the Visible Integral-Field Replicable Unit (IFU) Spectrograph, HETDEX obtains >35,000 spectra per exposure over 3500–5500 Å at R ∼ 800 with ∼1.″8 image quality, enabling an untargeted census of emission-line galaxies across 540 deg2. We present HETDEX Public Data Release 1 (PDR1), comprising 431,713 IFU observations covering 86.67 deg2 of noncontiguous sky in the Spring (13h, +51°) and Fall (1 .h 5, 0°) fields, along with legacy regions (Cosmic Evolution Survey, Great Observations Origins Deep Survey North, North Ecliptic Pole, SA22). PDR1 includes the HETDEX Public Source Catalog 2 (HPSC2), an expanded and reprocessed version of E. Mentuch Cooper et al. (2023) incorporating four additional years of data, improved quality control, and new machine learning classifiers. HPSC2 contains 426,654 LAEs, 491,411 [O II] emitters, 19,457 low-z galaxies, 18,303 active galactic nuclei, and 150,608 stars, providing coordinates, redshifts or stellar velocities, and 1D spectra for each source. Because the data cubes use local sky subtraction optimized for faint emission-line detection, they are not suited for absolute surface-brightness measurements or very extended nearby galaxies. Appendix materials include the full detection catalog, the 1.6 million–candidate LAE sample, and raw detection databases. All products are publicly accessible through the HETDEX data portal (https://hetdex.org/data-results/), including access to a public JupyterLab. HPSC2 is also publicly available via Zenodo (doi:10.5281/zenodo.19581262).

Abstract:

Context . Astronomical imaging aims to maximize signal capture while minimizing noise. It is difficult and expensive to enhance the signal-to-noise ratio directly on detectors, which has led to extensive research into advanced post-processing techniques. Aims . Removing background noise from images is a valuable preprocessing step for catalog-building tasks. We introduce BGRem, a machine-learning (ML)-based tool to remove background noise from astronomical images. Our aim is to improve image quality and enhance the performance of the subsequent analysis pipeline, from detecting faint sources to performing source characterization tasks. Methods . The BGRem tool uses a diffusion-based model with an attention U-Net as backbone, trained on simulated images for optical and gamma ( γ )-ray data from the MeerLICHT and Fermi-LAT telescopes. The tool learns to denoise astronomical images in a supervised manner over several diffusion steps. We performed preprocessing and postprocessing techniques, including normalization and median subtraction, on these images to make them suitable for the analysis pipeline. Results . We compared the performance of BGRem with SourceExtractor (SExtractor), a widely used tool for cataloging astronomical sources. The number of true positive sources using SExtractor increased by about 7% for MeerLICHT data when we used BGRem as a preprocessing step. We also show the generalizability of BGRem by testing it with optical images from different telescopes and on simulated γ -ray data representative of the Fermi-LAT telescope. In both cases, BGRem improves the source detection efficiency. Conclusions . The BGRem tool improves the source detection accuracy of traditional pixel-based methods by removing complex background noise. Using zero-shot approach, BGRem generalizes well to a wide range of optical images. The successful application of BGRem to simulated γ -ray images, alongside optical data, demonstrates its adaptability to distinct noise characteristics and observational domains. This cross-wavelength performance highlights its potential as a general-purpose background removal framework for multiwavelength astronomical surveys.

Abstract:

Context. We present the optical follow-up of SNe 2022ywf and 2023zgx, two examples from the Iax subclass of thermonuclear supernova (SN) events. With peak absolute magnitudes of M V = −13.7 and −14.4 mag, respectively, both objects belong to the extremely low-luminosity (EL) population of the class. Aims. The common origin of SNe in the Iax subclass remains under debate, since the distribution of certain observables may indicate that the extremely low-luminosity explosions form a distinct population. We aim to estimate the physical properties of the two EL objects, including mapping the ejecta structure. We compare the results with the predictions of the pure deflagration model with similar luminosity, as well as with the common features of other SNe Iax. Methods. We performed spectral tomography on the spectral series of SNe 2022ywf and 2023zgx around their maxima to map the physical properties of the ejecta. Together with the analysis of BgVriz photometry, we studied a wide range of observables to investigate their distribution against luminosity. We compared the constrained chemical abundances of the ejecta to the predictions of hydrodynamic simulations with similar peak luminosities. Results. Constant abundances provide a good match for the distribution of chemical elements for both SNe 2022ywf and 2023zgx. The discrepancies compared to the least luminous pure deflagration model N5def_hybrid are minor, especially at post-maximum epochs. The two SNe also share similar characteristics in their constrained density structures, as well as in the evolution of the photosphere. Conclusions. The analysis supports the assumption that pure deflagration models can reproduce the main characteristics of SNe Iax, even for the low-luminosity population. The presented indirect observational evidence indicates that these objects show similar intrinsic properties to the well-studied, relatively luminous Iax sample and fit into the velocity distribution of the subclass.

Abstract:

We report the discovery and multiwavelength characterization of the Galactic transient EPJ174942.2–384834, first detected by the Einstein Probe during a faint X-ray outburst in 2025 March. Coordinated follow-up observations revealed two major outbursts and a rebrightening over a 7 month period. Broadband X-ray spectral modeling shows that the outburst emission was dominated by thermal Comptonization of very soft seed photons. The absence of a detected thermal disk component, together with the low inferred seed-photon temperature, is consistent with a cool and possibly truncated accretion disk. The X-ray spectrum remained consistently hard throughout the outburst activity, with a power-law photon index of Γ ≈ 1–2, gradually softening as the flux declined. The optical/UV counterpart brightened in tandem with the X-ray emission and exhibited a blue continuum with broad Balmer absorption features. Together with the optical/UV–X-ray luminosity correlation, this supports a disk-dominated origin of the optical/UV outburst emission, with viscous heating likely playing a major role and irradiation possibly contributing, especially in the UV. No radio counterpart was detected, implying at most very faint jet activity. Taken together, the observed properties support the classification of EPJ174942.2–384834 as a very faint X-ray transient black hole candidate. This study demonstrates the ability of the Einstein Probe to uncover and characterize the faintest accreting compact objects in the Galaxy.

Abstract:

Using Global Entrepreneurship Monitor (GEM) 2024 data for Italy and the United Kingdom, this study examines how gender gaps in entrepreneurial perceptions, measured as perceived entrepreneurial skills and fear of failure, vary across age and education within contrasting institutional regimes. Drawing on a life‐course institutional framework, we test whether cumulative disadvantage and institutional moderation shape gendered perceptual trajectories differently in a coordinated market economy (Italy) and a liberal market economy (United Kingdom). Across both countries, men report higher perceived entrepreneurial skills and lower fear of failure, but patterns diverge over the life course. In the United Kingdom, gender gaps tend to narrow with age, whereas in Italy they persist or widen, particularly among highly educated individuals approaching retirement. Although sustainability orientation is not directly measured, the findings suggest that institutionally shaped gender gaps may indirectly influence inclusive participation in entrepreneurial ecosystems. These results highlight the importance of institutional flexibility and second‐chance norms in shaping gendered access to entrepreneurial activity.