A major challenge in extracting information from current and upcoming surveys of cosmological large-scale structure (LSS) is the limited availability of computationally expensive high-fidelity simulations. We introduce calibrated neural quantile estimation, a new simulation-based inference (SBI) method that leverages a large number of approximate simulations for training and a small number of high-fidelity simulations for calibration. This approach guarantees an unbiased posterior regardless of approximate simulation accuracy, while achieving near-optimal constraining power when the approximate simulations are reasonably accurate. As a proof of concept, we demonstrate that cosmological parameters can be inferred at field level from projected 2-dimensional dark matter density maps up to k_{max}∼1.5  h/Mpc at z=0 by training on ∼10^{4} particle-mesh simulations with transfer function correction and calibrating with ∼10^{2} particle-particle (PP) simulations. The calibrated posteriors closely match those obtained by directly training on ∼10^{4} expensive PP simulations, but at a fraction of the computational cost. Our method offers a practical and scalable framework for SBI of cosmological LSS, enabling precise inference across vast volumes and down to small scales.

We present the Black Hole Explorer (BHEX), a mission that will produce the sharpest images in the history of astronomy by extending submillimeter Very-Long-Baseline Interferometry (VLBI) to space. BHEX will discover and measure the bright and narrow "photon ring" that is predicted to exist in images of black holes, produced from light that has orbited the black hole before escaping. This discovery will expose universal features of a black hole's spacetime that are distinct from the complex astrophysics of the emitting plasma, allowing the first direct measurements of a supermassive black hole's spin. In addition to studying the properties of the nearby supermassive black holes M87* and Sgr A*, BHEX will measure the properties of dozens of additional supermassive black holes, providing crucial insights into the processes that drive their creation and growth. BHEX will also connect these supermassive black holes to their relativistic jets, elucidating the power source for the brightest and most efficient engines in the universe. BHEX will address fundamental open questions in the physics and astrophysics of black holes that cannot be answered without submillimeter space VLBI. The mission is enabled by recent technological breakthroughs, including the development of ultra-high-speed downlink using laser communications, and it leverages billions of dollars of existing ground infrastructure. We present the motivation for BHEX, its science goals and associated requirements, and the pathway to launch within the next decade.

We calculate the interferometric signatures of black hole photon rings beyond the universal regime by perturbatively including the effects of finite ring width. Our approach first slices a thick ring into a series of thin rings, each of which falls within the universal regime. We thus calculate the visibility of the thick ring by aggregating the contributions from each thin ring and then perturbatively expand the result into polynomials of the baseline length u . We show that the visibility amplitude of a thick ring depends on its "center-of-light" diameter; it also includes additional higher-order corrections due to the width of the ring, with the leading correction terms proportional to u 2 for the envelope and u 3 for the phase. We apply our method to images ray traced from general-relativistic magnetohydrodynamic simulations and demonstrate that incorporating the higher-order corrections is crucial for accurately modeling the visibility of the first photon ring around M87*.

The angular momentum of galaxies (galaxy spin ) contains rich information about the initial condition of the universe, yet it is challenging to efficiently measure the spin direction for the tremendous amount of galaxies that are being mapped by ongoing and forthcoming cosmological surveys. We present a machine-learning-based classifier for the Z-wise versus S-wise spirals, which can help to break the degeneracy in the galaxy spin direction measurement. The proposed chirality equivariant residual network (CE-ResNet) is manifestly equivariant under a reflection of the input image, which guarantees that there is no inherent asymmetry between the Z-wise and S-wise probability estimators. We train the model with Sloan Digital Sky Survey images, with the training labels given by the Galaxy Zoo 1 project. A combination of data augmentation techniques is used during the training, making the model more robust to be applied to other surveys. We find an ∼30% increase in both types of spirals when Dark Energy Spectroscopic Instrument (DESI) images are used for classification, due to the better imaging quality of DESI. We verify that the ∼7 σ difference between the numbers of Z-wise and S-wise spirals is due to human bias, since the discrepancy drops to <1.8 σ with our CE-ResNet classification results. We discuss the potential systematics relevant to future cosmological applications.

We study the observational signatures of magnetically arrested black hole accretion with non-rotating inflow onto a rotating black hole; we consider a range of angles between the black hole spin and the initial magnetic field orientation. We compare the results of our General Relativistic Magneto-Hydrodynamic simulations to more commonly used rotating initial conditions and to the Event Horizon Telescope (EHT) observations of M87. We find that the mm intensity images, polarization images, and synchrotron emission spectra are very similar among the different simulations when post-processed with the same electron temperature model; observational differences due to different electron temperature models are significantly larger than those due to the different realizations of magnetically arrested accretion. The orientation of the mm synchrotron polarization is particularly insensitive to the initial magnetic field orientation, the electron temperature model, and the rotation of the inflowing plasma. The largest difference among the simulations with different initial rotation and magnetic tilt is in the strength and stability of the jet; spherical inflow leads to kink-unstable jets. We discuss the implications of our results for current and future EHT observations and for theoretical models of event-horizon-scale black hole accretion.

Normalizing constant (also called partition function, Bayesian evidence, or marginal likelihood) is one of the central goals of Bayesian inference, yet most of the existing methods are both expensive and inaccurate. Here we develop a new approach, starting from posterior samples obtained with a standard Markov Chain Monte Carlo (MCMC). We apply a novel Normalizing Flow (NF) approach to obtain an analytic density estimator from these samples, followed by Optimal Bridge Sampling (OBS) to obtain the normalizing constant. We compare our method which we call Gaussianized Bridge Sampling (GBS) to existing methods such as Nested Sampling (NS) and Annealed Importance Sampling (AIS) on several examples, showing our method is both significantly faster and substantially more accurate than these methods, and comes with a reliable error estimation.

Normalizing constant (Bayesian evidence) is one of the central goals of Bayesian inference, yet most of the existing methods are both expensive and inaccurate. Here we develop a new approach, starting from posterior samples obtained from a standard Markov Chain Monte Carlo (MCMC). We apply a novel Normalizing Flow (NF) approach to obtain an analytic density estimator from these samples, followed by Optimal Bridge Sampling (OBS) to obtain the normalizing constant. We compare our method to existing methods such as Nested Sampling (NS) and Annealed Importance Sampling (AIS) on several examples, showing our method is both computationally faster and significantly more accurate than these methods, and comes with a reliable error estimation.