Rubin-LSST

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:

The division of the Milky Way’s disk into “thin” and “thick” components is a common practice, but one that is often ambiguously defined. The two ways of dividing stars are not equivalent: many stars belonging to the stellar population at high [α/Fe] (the “chemical thick disk”) do not belong to the larger-scale-height exponential density component of G. Gilmore & N. Reid (the “kinematic thick disk”). Furthermore, the existence of two distinct kinematic components is debated. This issue has surfaced in discussions about the recently discovered interstellar object 3I/ATLAS, which likely originated around an old star, and has been variously classified as a member of both the “thin disk” and the “thick disk” by different works. We illustrate that the origins of interstellar objects should be discussed with care, and relative to specific, identified populations of stars.

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.