#ConformalPrediction #NormalizingFlows #Calibration #MultivariateModels #UncertaintyQuantification #AIResearch #MachineLearning

Weekly Update from the Open Journal of Astrophysics – 26/04/2025

It’s Satuday morning once again, and time for another update of papers published at the Open Journal of Astrophysics. Since the last update we have published two papers, which brings the number in Volume 8 (2025) up to 44 and the total so far published by OJAp up to 279.

The first paper to report is “Approximating non-Gaussian Bayesian partitions with normalising flows: statistics, inference and application to cosmology” by Tobias Röspel, Adrian Schlosser & Björn Malte Schäfer (Universität Heidelberg, Germany) which was published on April 23rd 2025 in the folder Cosmology and NonGalactic Astrophysics. It is an introduction to normalizing flows – a machine learning technique for transforming distributions – and its application to the extraction of cosmological parameters from supernova data.

The overlay is here:

You can find the officially accepted version on arXiv here.

The other paper this week is “Dwarf Galaxies in the TNG50 Field: connecting their Star-formation Rates with their Environments” by Joy Bhattacharyya & Annika H.G. Peter (Ohio State University, USA) and Alexie Leauthaud (UC Santa Cruz, USA).  This one was published on 24th April 2025 in the older Astrophysics of Galaxies and it studies dwarf galaxies with properties similar to the Large and Small Magellanic Clouds that form in different environments in the TNG50 simulation of the IllustrisTNG project.

The overlay is here:

and you can find the final accepted version on arXiv here.

That’s all for this week. I’ll have another update next Saturday.

#arXiv250101946v2 #arXiv250104791v3 #AstrophysicsOfGalaxies #Cosmology #CosmologyAndNonGalacticAstrophysics #DiamondOpenAccess #dwarfGalaxies #Illustris #MachineLearning #normalizingFlows #OpenJournalOfAstrophysics #TheOpenJournalOfAstrophysics

#arxivfeed:

"Validation Diagnostics for SBI algorithms based on Normalizing Flows"
https://arxiv.org/abs/2211.09602

#SBI #bayesian #inference #MachineLearning #DeepLearning #NormalizingFlows