We present updated non-adiabatic and inhomogeneous evolution models for Uranus and Neptune, employing an interior composition of methane, ammonia, water, and rocks. Following formation trends of the gas giants, Uranus and Neptune formation models are applied, where both planets begin with layers stable to convection. Both planets are subject to convective mixing throughout their evolution. Uranus undergoes modest convective mixing, preserving much of its primordial internal heat. In contrast, Neptune's interior undergoes extensive mixing, homogenization, and adiabatic cooling of the outer 40\% of its envelope. The subsequent release of internal energy in Neptune, driven by the convective instability of its primordial outer compositional gradient, accounts for its higher luminosity relative to Uranus. Thus, the observed luminosity differences between Uranus and Neptune could be primarily dictated by the convective stability of their outer envelopes. The extensive convective mixing in Neptune leads to a higher metallicity in its outer region compared to Uranus, a feature seen in atmospheric measurements and shown in past interior models of Neptune. Due to Neptune's more pronounced cooling, our models predict favorable conditions for hydrogen-water immiscibility in its envelope.
(abridged) In a series of publications, we describe a comprehensive comparison of Event Horizon Telescope (EHT) data with theoretical models of Sgr A* and M87*. Here, we report on improvements made to our observational data reduction pipeline and present the generation of observables derived from the EHT models. We make use of ray-traced GRMHD simulations that are based on different black hole spacetime metrics and accretion physics parameters. These broad classes of models provide a good representation of the primary targets observed by the EHT. To generate realistic synthetic data from our models, we took the signal path as well as the calibration process, and thereby the aforementioned improvements, into account. We could thus produce synthetic visibilities akin to calibrated EHT data and identify salient features for the discrimination of model parameters. We have produced a library consisting of an unparalleled 962,000 synthetic Sgr A* and M87* datasets. In terms of baseline coverage and noise properties, the library encompasses 2017 EHT measurements as well as future observations with an extended telescope array. We differentiate between robust visibility data products related to model features and data products that are strongly affected by data corruption effects. Parameter inference is mostly limited by intrinsic model variability, which highlights the importance of long-term monitoring observations with the EHT. In later papers in this series, we will show how a Bayesian neural network trained on our synthetic data is capable of dealing with the model variability and extracting physical parameters from EHT observations. With our calibration improvements, our newly reduced EHT datasets have a considerably better quality compared to previously analyzed data.
(abridged) In this second paper in our publication series, we present the open-source Zingularity framework for parameter inference with deep Bayesian artificial neural networks. We carried out out supervised learning with synthetic millimeter very long baseline interferometry observations of the EHT. Our ground-truth models are based on GRMHD simulations of Sgr A* and M87* on horizon scales. We investigated how well Zingularity neural networks are able to infer key model parameters from EHT observations, such as the black hole spin and the magnetic state of the accretion disk, when uncertainties in the data are accurately taken into account. Zingularity makes use of the TensorFlow Probability library and is able to handle large amounts of data with a combination of the efficient TFRecord data format plus the Horovod framework. Our approach is the first analysis of EHT data with Bayesian neural networks, where an unprecedented training data size, under consideration of a closely modeled EHT signal path, and the full information content of the observational data are used. Zingularity infers parameters based on salient features in the data and is containerized. Through parameter surveys and dedicated validation tests, we identified neural network architectures, that are robust against internal stochastic processes and unaffected by noise in the observational and model data. We give examples of how different data properties affect the network training. We show how the Bayesian nature of our networks gives trustworthy uncertainties and uncovers failure modes for uncharacterizable data. It is easy to achieve low validation errors during training on synthetic data with neural networks, particularly when the forward modeling is too simplified. Through careful studies, we demonstrate that our trained networks can generalize well so that reliable results can be obtained from observational data.
(abridged) In the first two papers of this publication series, we present a comprehensive library of synthetic EHT observations and used this library to train and validate Bayesian neural networks for the parameter inference of accreting supermassive black hole systems. The considered models are ray-traced GRMHD simulations of Sgr A* and M87*. In this work, we infer the best-fitting accretion and black hole parameters from 2017 EHT data and predict improvements that will come with future upgrades of the array. Compared to previous EHT analyses, we considered a substantially larger synthetic data library and the most complete set of information from the observational data. We made use of the Bayesian nature of the trained neural networks and apply bootstrapping of known systematics in the observational data to obtain parameter posteriors. Within a wide GRMHD parameter space, we find M87* to be best described by a spin between 0.5 and 0.94 with a retrograde MAD accretion flow and strong synchrotron emission from the jet. Sgr A* has a high spin of $\sim$ 0.8 $-$ 0.9 and a prograde accretion flow beyond the standard MAD/SANE models with a comparatively weak jet emission, seen at a $\sim$ 20$^\circ$ $-$ 40$^\circ$ inclination and $\sim$ 106$^\circ$ $-$ 137$^\circ$ position angle. While previous EHT analyses could rule out specific regions in the model parameter space considered here, we are able to obtain narrow parameter posteriors with our Zingularity framework without being impacted by the unknown foreground Faraday screens and data calibration biases. We further demonstrate that the AMT extension to the EHT will reduce parameter inference errors by a factor of three for non-Kerr models, enabling more robust tests of general relativity. It will be instructive to produce new GRMHD models with the inferred interpolated parameters to study their accretion rate plus jet power.
Supermassive black hole feedback is the currently favoured mechanism to regulate the star formation rate of galaxies and prevent the formation of ultra-massive galaxies ($M_\star>10^{12}M_\odot$). However, the mechanism through which the outflowing energy is transferred to the surrounding medium strongly varies from one galaxy evolution model to another, such that a unified model for AGN feedback does not currently exist. The hot atmospheres of galaxy groups are highly sensitive laboratories of the feedback process, as the injected black hole energy is comparable to the binding energy of halo gas particles. Here we report multi-wavelength observations of the fossil galaxy group SDSSTG 4436. The hot atmosphere of this system exhibits a highly relaxed morphology centred on the giant elliptical galaxy NGC~3298. The X-ray emission from the system features a compact core ($<$10 kpc) and a steep increase in the entropy and cooling time of the gas, with the cooling time reaching the age of the Universe $\sim15$ kpc from the centre of the galaxy. The observed entropy profile implies a total injected energy of $\sim1.5\times10^{61}$ ergs, which given the high level of relaxation could not have been injected by a recent merging event. Star formation in the central galaxy NGC~3298 is strongly quenched and its stellar population is very old ($\sim$10.6 Gyr). The currently detected radio jets have low power and are confined within the central compact core. All the available evidence implies that this system was affected by giant AGN outbursts which excessively heated the neighbouring gas and prevented the formation of a self-regulated feedback cycle. Our findings imply that AGN outbursts can be energetic enough to unbind gas particles and lead to the disruption of cool cores.
Recent observations at low redshift have revealed that some post-starburst galaxies retain significant molecular gas reservoirs despite low ongoing star formation rates, challenging theoretical predictions for galaxy quenching. To test whether this finding holds during the peak epoch of quenching, here we present ALMA CO(2-1) observations of five spectroscopically confirmed post-starburst galaxies at z ~ 1.4 from the HeavyMetal survey. While four galaxies are undetected in CO emission, we detect M_H2 ~ 10^9.7 Msun of molecular gas in one system. The detected system is a close pair of massive (M* = 10^(11.1-11.2) Msun) post-starburst galaxies with no clear tidal features, likely caught in the early stages of a major merger. These results suggest that mergers may be a key factor in retaining molecular gas while simultaneously suppressing star formation in quenched galaxies at high redshift, possibly by driving increased turbulence that decreases star formation efficiency. Unlike previous studies at z < 1, we find no correlation between molecular gas mass and time since quenching. This may be explained by the fact that -- despite having similar UVJ colors -- all galaxies in our sample have post-burst ages older than typical gas-rich quenched systems at low redshift. Our results highlight the importance of major mergers in shaping the cold gas content of quiescent galaxies during the peak epoch of quenching.
Quasi-periodic pulsations (QPPs) at sub-second periods are frequently detected in the time series of X-rays during stellar flares. However, such rapid pulsations are rarely reported in the hard X-ray (HXR) emission of the small solar flare. We explored the QPP patterns with fast-time variations in HXR and radio emissions produced in a small solar flare on 2025 January 19. By applying the Fast Fourier Transform, the fast-variation pulsations at a quasi-period of about 1 s are identified in the HXR channel of 20-80 keV, which were simultaneously measured by the Hard X-ray Imager and the Konus-Wind. The rapid pulsations with a same quasi-period were also detected in the radio emission at a lower frequency range of about 40-100 MHz. The restructured HXR images show that the QPP patterns mainly locate in footpoint areas that connect by hot plasma loops, and they appear in the flare impulsive phase. Our observations suggest that the fast-variation pulsations could be associated with nonthermal electrons that are periodically accelerated by the intermittent magnetic reconnection, and the 1-s period may be modulated by the coalescence instability between current-carrying loops and magnetic islands.