https://iphysresearch.github.io/blog/tag/massive-black-hole-binaries/Massive Black Hole BinariesHugo Blox Builder (https://hugoblox.com)en-usMon, 22 Jun 2026 00:00:00 +0000https://iphysresearch.github.io/blog/media/sharing.pnghttps://iphysresearch.github.io/blog/tag/massive-black-hole-binaries/https://iphysresearch.github.io/blog/mypublication/2026_mbhb_source_confusion/Mon, 22 Jun 2026 00:00:00 +0000https://iphysresearch.github.io/blog/mypublication/2026_mbhb_source_confusion/<p>The analysis studies overlap in both time and frequency. It compares waveform models to assess how higher-order modes change the inference consequences of source confusion.</p> <h2 id="sources">Sources</h2> <ul> <li><a href="https://link.aps.org/doi/10.1103/15ln-mtps" target="_blank" rel="noopener">Publisher record</a></li> <li><a href="https://arxiv.org/abs/2504.09679" target="_blank" rel="noopener">arXiv abstract and submission history</a></li> <li><a href="https://api.crossref.org/works/10.1103/15ln-mtps" target="_blank" rel="noopener">Publisher metadata via Crossref</a></li> </ul>https://iphysresearch.github.io/blog/mypublication/2025_cvae_bayesian_sampling/Fri, 30 May 2025 00:00:00 +0000https://iphysresearch.github.io/blog/mypublication/2025_cvae_bayesian_sampling/<p>The learned model provides a rapid initial estimate, followed by conventional sampling. This combination is evaluated as a way to reduce sampling effort while retaining Bayesian precision.</p> <h2 id="sources">Sources</h2> <ul> <li><a href="https://link.aps.org/doi/10.1103/8r8b-hckx" target="_blank" rel="noopener">Publisher record</a></li> <li><a href="https://arxiv.org/abs/2502.09266" target="_blank" rel="noopener">arXiv abstract and submission history</a></li> <li><a href="https://api.crossref.org/works/10.1103/8r8b-hckx" target="_blank" rel="noopener">Publisher metadata via Crossref</a></li> </ul>