Previous Issue
Volume 3, June
 
 

Astronomy, Volume 3, Issue 3 (September 2024) – 2 articles

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Select all
Export citation of selected articles as:
19 pages, 2148 KiB  
Conference Report
Unsupervised Domain Adaptation for Constraining Star Formation Histories
by Sankalp Gilda, Antoine de Mathelin, Sabine Bellstedt and Guillaume Richard
Astronomy 2024, 3(3), 189-207; https://doi.org/10.3390/astronomy3030012 - 3 Jul 2024
Viewed by 174
Abstract
In astronomy, understanding the evolutionary trajectories of galaxies necessitates a robust analysis of their star formation histories (SFHs), a task complicated by our inability to observe these vast celestial entities throughout their billion-year lifespans. This study pioneers the application of the Kullback–Leibler Importance [...] Read more.
In astronomy, understanding the evolutionary trajectories of galaxies necessitates a robust analysis of their star formation histories (SFHs), a task complicated by our inability to observe these vast celestial entities throughout their billion-year lifespans. This study pioneers the application of the Kullback–Leibler Importance Estimation Procedure (KLIEP), an unsupervised domain adaptation technique, to address this challenge. By adeptly applying KLIEP, we harness the power of machine learning to innovatively predict SFHs, utilizing simulated galaxy models to forge a novel linkage between simulation and observation. This methodology signifies a substantial advancement beyond the traditional Bayesian approaches to Spectral Energy Distribution (SED) analysis, which are often undermined by the absence of empirical SFH benchmarks. Our empirical investigations reveal that KLIEP markedly enhances the precision and reliability of SFH inference, offering a significant leap forward compared to existing methodologies. The results underscore the potential of KLIEP in refining our comprehension of galactic evolution, paving the way for its application in analyzing actual astronomical observations. Accompanying this paper, we provide access to the supporting code and dataset on GitHub, encouraging further exploration and validation of the efficacy of the KLIEP in the field. Full article
Show Figures

Figure 1

22 pages, 648 KiB  
Article
RisingTides: An Analytic Modeling Code of Tidal Effects in Binary Neutron Star Mergers
by Alexander O’Dell and Maria C. Babiuc Hamilton
Astronomy 2024, 3(3), 167-188; https://doi.org/10.3390/astronomy3030011 - 2 Jul 2024
Viewed by 224
Abstract
Gravitational waves produced by binary neutron star mergers offer a unique window into matter behavior under extreme conditions. In this context, we analytically model the effect of matter on gravitational waves from binary neutron star mergers. We start with a binary black hole [...] Read more.
Gravitational waves produced by binary neutron star mergers offer a unique window into matter behavior under extreme conditions. In this context, we analytically model the effect of matter on gravitational waves from binary neutron star mergers. We start with a binary black hole system, leveraging the post-Newtonian formalism for the inspiral and the Backwards-one-Body model for the merger. We combine the two methods to generate a baseline waveform and we validate our results against numerical relativity simulations. Next, we integrate tidal effects in phase and amplitude to account for matter and spacetime interaction using the NRTidal model and test its accuracy against numerical relativity predictions for two equations of state, finding a mismatch around the merger. Subsequently, we lift the restriction on the coefficients to be independent of the tidal deformability and recalibrate them using the numerical relativity predictions. We obtain better fits for phase and amplitude around the merger and are able to extend the phase modeling beyond the merger. We implement our method in new open-source, user-friendly Python code, steered by a Jupyter Notebook, named RisingTides. Our research offers new perspectives on analytically modeling the effect of tides on the gravitational waves from binary neutron star mergers. Full article
Show Figures

Figure 1

Previous Issue
Back to TopTop