https://cdsarc.cds.unistra.fr/viz-bin/cat/J/MNRAS/520/5938
#GiantStars #VisibleAstronomy #Spectroscopy #ChemicallyPeculiarStars
Weekly Update from the Open Journal of Astrophysics 20/06/2026
It’s Saturday again so it’s time for another update of activity at the Open Journal of Astrophysics. Since the last update we have published a further four papers, bringing the number in Volume 9 (2026) to 126 and the total so far published by OJAp up to 574.
I will 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, published on Monday 15th June, is “SN 2025adpq: A Type Ia supernova in a collisional ring formed during a major galaxy merger” by Brendan O’Connor (Carnegie Mellon University, USA) and 18 others based in the USA, Germany and Australia. The study reports the discovery of a Type Ia supernova, SN 2025adpq, within a collisional ring formed by a major galaxy merger., offset from the nucleus of the primary galaxy. It is published in the folder Astrophysics of Galaxies.
The overlay for this paper is here
You can find the officially accepted version on arXiv here and the announcement on Fediverse here:
https://fediscience.org/@OJ_Astro/116753183446523072
The second paper for this week, published on Tuesday June 16th in the folder Astrophysics of Galaxies is “The Colors of Ices: Measuring ice column density through photometry” by Adam Ginsburg (U. Florida, USA) and ten others based in the USA, Germany and Spain. This study demonstrates that JWST photometry can identify and quantify interstellar ices, using new open-source models, interstellar ices, finding significant abundance in non-star-forming gas, suggesting many avenues for further research.
The overlay looks like this:
The official version of the paper can be found on arXiv here and the Fediverse announcement here:
https://fediscience.org/@OJ_Astro/116758595971333611
The third paper of the week, published on Wednesday 17th June in the folder Cosmology and Nongalactic Astrophysics is “The Non-Gaussian Weak-Lensing Likelihood: A Multivariate Copula Construction and Impact on Cosmological Constraints” by Veronika Oehl and Tilman Tröster (both of ETH Zurich, Switzerland). This study presents a framework for computing non-Gaussian likelihoods for correlation functions, particularly useful in large-scale weak-lensing surveys. It suggests Gaussian likelihoods are sufficient for stage-IV surveys.
The overlay for this one is here:
The final, accepted version can be found on arXiv here and the Mastodon announcement is here:
https://fediscience.org/@OJ_Astro/116764509657843733
The fourth and final paper of the week, also ublished on Wednesday 17th June but in the folder Astrophysics of Galaxies, is “Black Hole Feedback, Galaxy Quenching and Outflows at Cosmic Dawn: Analysis of the SEEDZ Simulations” by Lewis R. Prole (Maynooth University, Ireland) and 15 others based in Ireland, Germany, USA and UK. The study analyzes the growth and feedback effects of massive black holes in SEEDZ simulations, suggesting that black hole feedback, not nearby supernovae or gas exhaustion, limits initial growth.
The overlay is here:
The final, accepted version can be found on arXiv here and the Mastodon announcement is here:
https://fediscience.org/@OJ_Astro/116764428903842241
And that concludes this week’s update. It has been another slow week on the publishing front, but the main reason is that we have a big backlog of papers accepted – about 10 of them – but still waiting for the authors to put their final versions on arXiv and we can’t do anything about that! I’ll do another update next Saturday.
#arXiv251000292v5 #arXiv260209104v2 #arXiv260315899v2 #arXiv260407336v2 #AstrophysicsOfGalaxies #blackHoles #collisionalRings #Copula #CosmicDawn #cosmologicalSurveys #CosmologyAndNonGalacticAstrophysics #DiamondOpenAccess #DiamondOpenAccessPublishing #galaxyMergers #Ice #InterstellarMedium #JWST #nonGaussianity #OpenAccess #OpenAccessPublishing #photometry #SEEDZSimulations #SN2025adpq #spectroscopy #Type1aSupernovae #weakGravitationalLensingWeekly Update from the Open Journal of Astrophysics 13/06/2026
It’s Saturday again so it’s time for another 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 122 and the total so far published by OJAp up to 570.
I will 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, published on Thursday 11th June, is “Dancing Streams In Merging Halos: Stellar Streams in a MW–LMC-like merger” by (all based in the USA): Sachi Weerasooriya (Carnegie Observatories), Tjitske Starkenburg (Northwestern U.), Emily C. Cunningham (Columbia U.) & Kathryn V. Johnston (Flatiron Institute). This article explores how galaxy mergers, like the Milky Way-Large Magellanic Cloud merger, significantly alter the properties and structures of stellar streams, challenging the recovery of their initial orbits. It is in the folder marked Astrophysics of Galaxies.
The overlay for this paper is here
You can find the officially accepted version on arXiv here and the announcement on Fediverse here:
https://fediscience.org/@OJ_Astro/116730200889106529
The second paper for this week, also published on Thursday 11th June but in the folder Cosmology and Nongalactic Astrophysics is “X-SORTER (X-ray Survey Of meRging clusTErs in Redmapper): X-ray and Spectroscopic Characterization of 12 Optically Selected Galaxy Cluster Merger Candidates” by Christopher Hopp, David Wittman, Rodrigo Stancioli, Zhuoran Gao & Faik Bouhrik (UC Davis) and Scott Adler (Rochester), all based in the USA. The X-SORTER program identifies merging galaxy clusters to study dark matter interactions, using optical indicators and X-ray observations. This method efficiently identifies active clusters suitable for detailed dark matter studies.
The overlay for this one looks like this:
The official version of the paper can be found on arXiv here and the Fediverse announcement here:
https://fediscience.org/@OJ_Astro/116730279994960097
The third and final paper of the week, published on Friday 12th June in the folder Earth and Planetary Astrophysics, is “JCMT Constraints on the Early-Time HCN and CO Emission and HCN Temporal Evolution of 3I/ATLAS” by Jason T. Hinkle (U. Illinois, USA) and 6 others based in the USA and Chile. This article presents observations of the third Interstellar Object, 3I/ATLAS, providing early sub-mm constraints on its activity. The findings suggest a steeper production rate slope than typical Solar System comets.
The overlay for this one is here:
The final, accepted version can be found on arXiv here and the Mastodon announcement is here:
https://fediscience.org/@OJ_Astro/116735805179724489
And that concludes this week’s update. It has been a slow week on the publishing front, but the main reason is that we have a big backlog of papers accepted but waiting for the authors to put their final versions on arXiv and we can’t do anything about that! I’ll do another update next Saturday.
#3IAtlas #arXiv250514792v2 #arXiv251202106v3 #arXiv260305596v4 #astrochemistry #AstrophysicsOfGalaxies #CO #cosmologicalSimulations #CosmologyAndNonGalacticAstrophysics #DiamondOpenAccess #DiamondOpenAccessPublishing #EarthAndPlanetaryAstrophysics #galaxyClusters #GalaxyHalos #galaxyMergers #HCN #HighEnergyAstrophysicalPhenomena #interstellarObjects #OpenAccess #OpenAccessPublishing #spectroscopy #StellarStreams #XSORTERWhy this $10 spectrometer chip could bring real-time chemical sensing to wearables
I'm presenting a webinar on
"Class modelling and outlier detection in NIR spectroscopy"
We have two sessions scheduled
🔸 Session 1 (best for the Americas): Jun 16th, 06:00 - 07:00 (UTC+10)
🔸 Session 2 (best for EMEA - APAC): Jun 16th, 18:00 - 19:00 (UTC+10)
Registration is free, but spots are limited.
Details and abstract are on the registration page 👇
https://www.trybooking.com/DMSTO
#NIR #spectroscopy #NearInfrared #Chemometrics #Python #MachineLearning