Hintze Centre for Astrophysical Surveys

Abstract:

We present ATLAS100 – a sample of 1729 supernovae and other explosive optical transients within ∼100 Mpc observed by the ATLAS survey over a span of 5.75 yr from 2017 September 21 to 2023 June 21. The volume-limited sample includes transients associated with galaxies with a spectroscopic redshift of , and spectroscopically classified transients within this redshift threshold where a host redshift was not available in existing catalogues. Our host galaxy list is constructed from aggregating all available galaxy redshift and distance catalogues. We carefully select all transients within a projected radius of 50 kpc of these hosts. The ATLAS100 transient sample has a host galaxy redshift completeness fraction of 83 per cent, consistent with expectations for the redshift completeness of local galaxy catalogues. Within this volume, the spectroscopic classifications are 87 per cent complete and we reclassify many ambiguous transients with joint light curve and spectroscopic considerations. Here, we release the catalogue together with compiled, binned, and cleaned ATLAS photometry for all transients. We fit the light curve data to derive peak luminosities and characteristic time-scales. We explore the sample characteristics, demographics, and discuss the completeness and purity of the sample. This is the first in a series of papers that will explore the rates and physical parameters of a complete and large sample of nearby supernovae and transients brighter than .

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:

Dark matter dominates the matter content of the Universe, and its properties can be constrained through large-scale structure probes such as the cross-correlation between the unresolved gamma-ray background (UGRB) and weak gravitational lensing. We analysed 15 years of Fermi–LAT data, constructing UGRB intensity maps in ten energy bins (0.5–1000 GeV), and cross-correlated them with KiDS-Legacy shear in six tomographic bins. The measurements were performed using angular power spectra estimated with the pseudo- C ℓ method. No significant cross-correlation was found. Based on this non-detection, we present 95% upper bounds on the weakly interacting massive particle decay rate Γ dec and velocity-averaged annihilation cross-section ⟨ σ ann v ⟩ as functions of mass. We compared our results with bounds from other cosmological tracers and from local probes, and we found them to be complementary, particularly at low masses (GeV/TeV). In addition, using a Euclid -like lensing survey cross-correlated with Fermi–LAT, we forecast approximately two to four times tighter limits, highlighting the potential of forthcoming data to strengthen constraints on dark matter annihilation and decay.