It’s time once more for another Saturday update of activity at the Open Journal of Astrophysics. Since the last update we have published a further three papers, bringing the number in Volume 9 (2026) to 163 and the total so far published by OJAp up to 611.
I continue to include the posts made on our Mastodon account (on Fediscience); these announcements also show the DOI for each paper.
The first paper to report this week was published on Tuesday 28th July in the folder Cosmology and Nongalactic Astrophysics. “The Origins of the Bulk Flow” by Richard Watkins & Trajan Clark (Willamett U., USA) and Hume A Feldman (U. Kansas, USA) analyzes the origin of large-scale bulk flow using the CosmicFlows-4 catalog, finding that it is largely influenced by structures beyond 200 h-1 Mpc, challenging previous assumptions.
The overlay for this paper is here
You can find the officially accepted version on arXiv here and the announcement on Fediverse here:
The second paper for this week, also published on Tuesday 28th July in the folder Cosmology and Nongalactic Astrophysics is: “Enhancing weak lensing redshift distribution characterization by optimizing the Dark Energy Survey Self-Organizing Map Photo-z method” by A. Campos (Carnegie Mellon U., USA) and the DES collaboration (113 authors). This study improves the Self-Organizing Map methodology for estimating redshift distribution in galaxies, notably reducing overlap between redshift bins, in preparation for the Dark Energy Survey Year 6 data.Abstractfor Enhancing weak lensing redshift distribution characterization by optimizing the Dark Energy Survey Self-Organizing Map Photo-z method.
The overlay looks like this:
The official version of the paper can be found on arXiv here and the Fediverse announcement here:
The third paper of the week, published on Thursday 29th July in the folder Instrumentation and Methods for Astrophysics is “The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone” by Peter Behroozi of the University of Arizona (USA). This paper presents a new family of Markov Chain Monte Carlo sampling methods based on ray tracing, offering higher resilience to heating for stochastic gradients and other advantages.
The overlay for this one is here:
The final, accepted version can be found on arXiv here and the Mastodon announcement is here:
That’s all for this week. It’s been a bit slower than previous weeks no doubt because of the holiday season.
In the Dark 