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Data-driven approaches using deep learning are emerging as powerful techniques to extract non-Gaussian information from cosmological large-scale structure. This work presents the first simulation-based inference (SBI) pipeline that combines…

We investigate the relationship between AGN optical variability timescales, amplitudes, and supermassive black hole (SMBH) masses using homogeneous light curves from the All-Sky Automated Survey for SuperNovae (ASAS-SN). We fit a damped…

Black-box simulators are widely used in robotics, but optimizing their parameters remains challenging due to inaccessible likelihoods. Simulation-Based Inference (SBI) tackles this issue using simulation-driven approaches, estimating the…

Robotics · Computer Science 2025-10-20 Gahee Kim , Takamitsu Matsubara

We present a general method to analyze reverberation mapping data that provides both estimates for the black hole mass and for the geometry and dynamics of the broad line region (BLR) in active galactic nuclei (AGN). Our method directly…

Cosmology and Nongalactic Astrophysics · Physics 2015-05-27 Anna Pancoast , Brendon J. Brewer , Tommaso Treu

As one of the series of papers reporting on a large reverberation mapping campaign of super-Eddington accreting massive black holes (SEAMBHs) in active galactic nuclei (AGNs), we present the results of 10 SEAMBHs monitored spectroscopically…

The reverberation mapping (RM) technique has seen wide applications in probing geometry and kinematics of broad-line regions (BLRs) and measuring masses of supermassive black holes (SMBHs) in active galactic nuclei. However, the key…

Astrophysics of Galaxies · Physics 2024-12-17 Yan-Rong Li , Jian-Min Wang

We present a Simulation-Based Inference (SBI) framework for cosmological parameter estimation via void lensing analysis. Despite the absence of an analytical model of void lensing, SBI can effectively learn posterior distributions through…

Cosmology and Nongalactic Astrophysics · Physics 2025-07-09 Chen Su , Huanyuan Shan , Cheng Zhao , Wenshuo Xu , Jiajun Zhang

We test the potential of Bayesian synthesis of upcoming multi-instrument data to extract orbital parameters and individual light curves of close binary supermassive black holes (CB-SMBH) with subparsec separations. Next generation (ng)…

Instrumentation and Methods for Astrophysics · Physics 2024-04-29 Andjelka B. Kovacevic , Yu-Yang Songsheng , Jian-Min Wang , Luka C. Popovic

Reverberation mapping (RM) of active galactic nuclei (AGNs) has been used over the past three decades to determine AGN broad-line region (BLR) sizes and central black-hole masses, and their relations with the AGN's luminosity. Until…

Astrophysics of Galaxies · Physics 2021-06-03 Shai Kaspi , W. N. Brandt , Dan Maoz , Hagai Netzer , Donald P. Schneider , Ohad Shemmer , C. J. Grier

Very-long-baseline interferometry (VLBI) at frequencies above 230 GHz with Earth-diameter baselines gives spatial resolution finer than the ${\sim}50 \mu$as "shadow" of the supermassive black hole at the Galactic Center, Sagittarius A* (Sgr…

Instrumentation and Methods for Astrophysics · Physics 2019-08-21 Daniel C. M. Palumbo , Sheperd S. Doeleman , Michael D. Johnson , Katherine L. Bouman , Andrew A. Chael

Machine learning is a promising tool to reconstruct time-series phenomena, such as variability of active galactic nuclei (AGN), from sparsely-sampled data. Here we use three Continuous Auto-Regressive Moving Average (CARMA) representations…

High Energy Astrophysical Phenomena · Physics 2022-04-21 Xinyue Sheng , Nicholas Ross , Matt Nicholl

In this paper, we calibrate the luminosity relation of gamma-ray bursts (GRBs) from an Artificial Neural Network (ANN) framework for reconstructing the Hubble parameter \unboldmath{$H(z)$} from the latest observational Hubble data (OHD)…

Cosmology and Nongalactic Astrophysics · Physics 2025-04-04 Zhen Huang , Zhiguo Xiong , Xin Luo , Guangzhen Wang , Yu Liu , Nan Liang

One of the primary goals of space-borne gravitational wave detectors is to detect and analyze extreme-mass-ratio inspirals (EMRIs). This endeavor presents a significant challenge due to the complex and lengthy EMRI signals, further…

General Relativity and Quantum Cosmology · Physics 2023-12-01 Qianyun Yun , Wen-Biao Han , Yi-Yang Guo , He Wang , Minghui Du

We present a composite machine learning framework to estimate posterior probability distributions of bulge-to-total light ratio, half-light radius, and flux for Active Galactic Nucleus (AGN) host galaxies within $z<1.4$ and $m<23$ in the…

Fast Radio Bursts (FRBs) are a promising tool for studying the low-density universe as their dispersion measures (DM) are extremely sensitive probes of electron column density. Active Galactic Nuclei (AGN) inject energy into the…

Astrophysics of Galaxies · Physics 2022-03-14 Adam J. Batten , Alan R. Duffy , Chris Flynn , Vivek Gupta , Emma Ryan-Weber , Nastasha Wijers

Spectroastrometry measures source astrometry as a function of wavelength/velocity. Reverberations of spectroastrometric signals naturally arise in broad-line regions (BLRs) of active galactic nuclei (AGNs) as a result of the continuum…

Astrophysics of Galaxies · Physics 2023-01-31 Yan-Rong Li , Jian-Min Wang

Simulation-based inference (SBI) is constantly in search of more expressive and efficient algorithms to accurately infer the parameters of complex simulation models. In line with this goal, we present consistency models for posterior…

Machine Learning · Computer Science 2024-11-05 Marvin Schmitt , Valentin Pratz , Ullrich Köthe , Paul-Christian Bürkner , Stefan T Radev

AGN variability offers a direct probe of accretion physics, disk structure, and black hole growth, but progress has been limited by sample size, cadence heterogeneity, and photometric systematics. The Vera C. Rubin Observatory Legacy Survey…

The next generation of space- and ground-based facilities promise to reveal an entirely new picture of the gravitational wave sky: thousands of galactic and extragalactic binary signals, as well as stochastic gravitational wave backgrounds…

General Relativity and Quantum Cosmology · Physics 2024-07-03 James Alvey , Uddipta Bhardwaj , Valerie Domcke , Mauro Pieroni , Christoph Weniger

Simulation-Based Inference (SBI) is an approach to statistical inference where simulations from an assumed model are used to construct estimators and confidence sets. SBI is often used when the likelihood is intractable and to construct…

Methodology · Statistics 2025-08-05 Lorenzo Tomaselli , Valérie Ventura , Larry Wasserman