The physics of recombination lines in the He I singlet system is expected to be relatively simple, supported by accurate atomic models. We examine the intensities of He I singlets λ3614, λ3965, λ5016, λ6678, and λ7281 and the triplet He I λ5876 in various types of ionized nebulae and compare them with theoretical predictions to test the validity of

Context. The aim of this work is to provide evidence of the imprints left by large-scale structure on galaxy properties by selecting extensive samples of galaxies from different environments, while matching their intrinsic and local environmental characteristics. Aims. We investigated the effects of the large-scale structure on the g ‑ r stellar

A wide variety of Galactic sources show transient emission at soft and hard X-ray energies: low- and high-mass X-ray binaries containing compact objects, isolated neutron stars exhibiting extreme variability as magnetars as well as pulsar-wind nebulae. Although most of them can show emission up to MeV and/or GeV energies, many have not yet been

We present the first joint analysis of the power spectrum and bispectrum using the Data Release 1 (DR1) of the Dark Energy Spectroscopic Instrument (DESI), focusing on Luminous Red Galaxies (LRGs) and quasars (QSOs) across a redshift range of 0.4 ≤ z ≤ 2.1. By combining the two- and three-point statistics, we are able to partially break the

Solar flares predominantly release their luminous energy in the chromosphere, where flare ribbons mark the footpoints of energy deposition and provide key insights into magnetic reconnection processes. Using high-resolution imaging and spectral observations, we examined fine structures within flare ribbons in a GOES C2.4 class solar flare observed

Context. The connection between galaxies and their host dark matter halos encompasses a range of intricate and interrelated processes, playing a pivotal role in our understanding of galaxy formation and evolution. Traditionally, this link has been established through physical or empirical models. On the other hand, machine learning techniques are