We present the results of our study of ASASSN–14dx, a previously known but poorly characterized cataclysmic variable (CV). The source was observed as part of an ongoing high-time-resolution photometric survey of CVs, which revealed that, in addition to the known 82.8-min orbital period, it also exhibits other transient periods, the strongest of

Early B-type stars with initial masses between 8 and 15 M ⊙ are frequently found in multiple systems, as is evidenced by multi-epoch spectroscopic campaigns in the Milky Way and the Large Magellanic Cloud (LMC). Previous studies have shown no strong metallicity dependence in the close-binary (a < 10 au) fraction or orbital-period distributions

Context. Over ten mid-infrared (mid-IR) and X-ray models are currently attempting to describe the nuclear obscuring material of active galactic nuclei (AGNs), but many questions remain unresolved. Aims. This study aims to determine the physical parameters of the obscuring material in nearby AGNs and explore their relationship with nuclear activity

The Wide Angle Search for Planets (WASP) survey provided some of the first transiting hot Jupiter candidates. With the addition of the Transiting Exoplanet Survey Satellite (TESS), many WASP planet candidates have now been revisited and given updated transit parameters. Here, we present nine transiting planets orbiting FGK stars that were

Magnetic fields play a significant role in star-forming processes on core to clump scales. We investigate magnetic field orientations and strengths in the massive star-forming clump P2 within the filamentary infrared dark cloud G28.34+0.06 using dust polarization observations made using SCUBA-2/POL-2 on the James Clerk Maxwell Telescope (JCMT) as

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