Rubin-LSST

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. Angular redshift fluctuations (ARFs) are a new cosmological observable recently proposed in the literature. It measures the 2D angular deviations of the average redshift of a given matter tracer under an input redshift shell. Since it depends on galaxy bias, it can be used to constrain primordial non-Gaussianity through the scale-dependent bias effect. Aims. We analyzed a sample of quasars built on Gaia satellite and unWISE data, Quaia to measure the local non-Gaussianity parameter f NL . This sample is particularly suitable for measuring f NL due to its large volume coverage. Methods. We measured the ARF power spectra from the Quaia catalog and combined their information with the 2D (projected) galaxy density and their cross-correlation with the Planck PR4 cosmic microwave background lensing maps to jointly constrain f NL . Results. Assuming the universality relation, we measure f NL = −3 ± 14 at the 68% confidence level by combining Quaia quasar angular density and ARFs with their CMB lensing cross-correlations. Neglecting the ARF – CMB lensing cross-correlation leads to a significant improvement in the model’s goodness-of-fit and yields comparable constraints, f NL = −5 −15 +16 . This result is the second tightest constraint on f NL using LSS two-point statistics to date and the best measurement achieved using two-point projected summary statistics, improving the previous measurement from Quaia by up to ∼25%. Our results support the inclusion of ARFs as an additional cosmological observable in future 2D analyses of upcoming datasets from large surveys.

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.