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New astro-ph.* submissions cross listed on cs.LG, physics.data-an, stat.*, cs.AI staritng 202509122000 and ending 202509182000

Feed last updated: 2025-09-18T00:00:00-04:00

VADER: A Variational Autoencoder to Infer Planetary Masses and Gas-Dust Disk Properties Around Young Stars

Authors: Sayed Shafaat Mahmud, Sayantan Auddy, Neal Turner, Jeffrey S. Bary
Comments: 6 pages, 5 figures, Accepted and Published at International Conference on Machine Learning, Machine Learning for Astrophysics Workshop 2025
Primary Category: astro-ph.EP
All Categories: astro-ph.EP, astro-ph.IM, cs.LG

We present \textbf{VADER} (Variational Autoencoder for Disks Embedded with Rings), for inferring both planet mass and global disk properties from high-resolution ALMA dust continuum images of protoplanetary disks (PPDs). VADER, a probabilistic deep learning model, enables uncertainty-aware inference of planet masses, $\alpha$-viscosity, dust-to-gas ratio, Stokes number, flaring index, and the number of planets directly from protoplanetary disk images. VADER is trained on over 100{,}000 synthetic images of PPDs generated from \texttt{FARGO3D} simulations post-processed with \texttt{RADMC3D}. Our trained model predicts physical planet and disk parameters with $R^2 > 0.9$ from dust continuum images of PPDs. Applied to 23 real disks, VADER's mass estimates are consistent with literature values and reveal latent correlations that reflect known disk physics. Our results establish VAE-based generative models as robust tools for probabilistic astrophysical inference, with direct applications to interpreting protoplanetary disk substructures in the era of large interferometric surveys.


Quantum Computing Tools for Fast Detection of Gravitational Waves in the Context of LISA Space Mission

Authors: Maria-Catalina Isfan, Laurentiu-Ioan Caramete, Ana Caramete, Daniel Tonoiu, Alexandru Nicolin-Zaczek
Comments: Submitted to Classical and Quantum Gravity
Primary Category: gr-qc
All Categories: gr-qc, astro-ph.CO, astro-ph.IM, physics.data-an

The field of gravitational wave (GW) detection is progressing rapidly, with several next-generation observatories on the horizon, including LISA. GW data is challenging to analyze due to highly variable signals shaped by source properties and the presence of complex noise. These factors emphasize the need for robust, advanced analysis tools. In this context, we have initiated the development of a low-latency GW detection pipeline based on quantum neural networks (QNNs). Previously, we demonstrated that QNNs can recognize GWs simulated using post-Newtonian approximations in the Newtonian limit. We then extended this work using data from the LISA Consortium, training QNNs to distinguish between noisy GW signals and pure noise. Currently, we are evaluating performance on the Sangria LISA Data Challenge dataset and comparing it against classical methods. Our results show that QNNs can reliably distinguish GW signals embedded in noise, achieving classification accuracies above 98\%. Notably, our QNN identified 5 out of 6 mergers in the Sangria blind dataset. The remaining merger, characterized by the lowest amplitude, highlights an area for future improvement in model sensitivity. This can potentially be addressed using additional mock training datasets, which we are preparing, and by testing different QNN architectures and ansatzes.


Nucleation regions in the Large-Scale Structure II: Morphology and dynamical state of supercluster cores

Authors: J. M. Zúñiga, C. A. Caretta, H. Andernach
Comments: This article has been accepted for publication in the Publications of the Astronomical Society of Australia (PASA) journal. Due to tecchnical problems with PASA macros in arXiv the current version is presented in MNRAS style. The manuscript file with nine (9) figures and six (6) tables embedded
Primary Category: astro-ph.CO
All Categories: astro-ph.CO, astro-ph.GA, math.DG, math.PR, physics.data-an

This work explores the morphology and dynamical properties of cores within rich superclusters, highlighting their role as transitional structures in the large-scale structure of the Universe. Using projected and radial velocity distributions of member galaxies, we identify cores as dense structures that, despite being gravitationally bound, are not yet dynamically relaxed. However, they exhibit a tendency toward virialisation, evolving in a self-similar manner to massive galaxy clusters but on a larger scale. Morphological analysis reveals that cores are predominantly filamentary, reflecting quasi-linear formation processes consistent with the Zeldovich approximation. Our estimates of the entropy confirm their intermediate dynamical state, with relaxation levels varying across the sample. Mass estimates indicate efficient accretion processes, concentrating matter into gravitationally bound systems. We conclude that cores are important environments where galaxy evolution and hierarchical assembly occur, bridging the gap between supercluster-scale structures and virialised clusters.


Improving cosmological reach of a gravitational wave observatory using Deep Loop Shaping

Authors: Jonas Buchli, Brendan Tracey, Tomislav Andric, Christopher Wipf, Yu Him Justin Chiu, Matthias Lochbrunner, Craig Donner, Rana X. Adhikari, Jan Harms, Iain Barr, Roland Hafner, Andrea Huber, Abbas Abdolmaleki, Charlie Beattie, Joseph Betzwieser, Serkan Cabi, Jonas Degrave, Yuzhu Dong, Leslie Fritz, Anchal Gupta, Oliver Groth, Sandy Huang, Tamara Norman, Hannah Openshaw, Jameson Rollins, Greg Thornton, George Van Den Driessche, Markus Wulfmeier, Pushmeet Kohli, Martin Riedmiller, LIGO Instrument Team
Comments: No comment found
Primary Category: astro-ph.IM
All Categories: astro-ph.IM, cs.LG, cs.SY, eess.SY, gr-qc

Improved low-frequency sensitivity of gravitational wave observatories would unlock study of intermediate-mass black hole mergers, binary black hole eccentricity, and provide early warnings for multi-messenger observations of binary neutron star mergers. Today's mirror stabilization control injects harmful noise, constituting a major obstacle to sensitivity improvements. We eliminated this noise through Deep Loop Shaping, a reinforcement learning method using frequency domain rewards. We proved our methodology on the LIGO Livingston Observatory (LLO). Our controller reduced control noise in the 10--30Hz band by over 30x, and up to 100x in sub-bands surpassing the design goal motivated by the quantum limit. These results highlight the potential of Deep Loop Shaping to improve current and future GW observatories, and more broadly instrumentation and control systems.