Energy Distribution Research Articles

A comprehensive investigation of EGR (exhaust gas recirculation) effects on energy distribution and emissions of a turbo-charging diesel engine under World Harmonized transient cycle

A comprehensive investigation of EGR (exhaust gas recirculation) effects on energy distribution and emissions of a turbo-charging diesel engine under World Harmonized transient cycle

OptiPower AI: A deep reinforcement learning framework for intelligent cluster energy management and V2X optimization in industrial applications

Peer-to-peer (P2P) energy trading has emerged as a practical solution for efficient energy management, particularly with the rising affordability of renewable energy and electric vehicles (EVs). This paper introduces the Optimal Power Artificial Intelligence (OptiPower AI) algorithm, a significant advancement in Intelligent Cluster Energy Management (ICEM). OptiPower AI combines Double Deep Q-Network (DDQN)-based Deep Reinforcement Learning (DRL), Vehicle-to-Everything (V2X) technology, and P2P energy trading to optimize energy distribution among clusters of prosumers and consumers. The system efficiently manages Renewable Energy Sources (RES) and EVs, achieving a 19.18% reduction in energy costs and a 50.02% decrease in average energy prices across V2X and P2P scenarios.OptiPower AI uses DRL to dynamically allocate energy and implement real-time pricing, enhancing energy efficiency and user satisfaction. Simulations based on meteorological data from Tunisia validate the system’s ability to improve thermal comfort, increase energy savings, and lower costs. The model’s parameters enable accurate forecasting and allocation, showcasing OptiPower AI’s reliability in variable demand conditions. This work advances the application of DRL in decentralized, sustainable P2P energy management systems for industrial clusters, addressing critical challenges in energy distribution, efficiency, and cost reduction.

Turbulent anisotropy and energy distribution over submerged cubical model

Turbulent anisotropy and energy distribution over submerged cubical model

Investigation of temporal and spatial distribution of tidal energy in Liuheng waterway via coastal acoustic tomography

Investigation of temporal and spatial distribution of tidal energy in Liuheng waterway via coastal acoustic tomography

Influence of plasma-chemical processes on the parameters of an atmospheric pressure Helium microdischarge.

This study investigates the influence of plasma-chemical processes on the parameters of an atmospheric pressure (AP) Helium microdischarge. Using a one-dimensional fluid model with both Maxwellian and non-Maxwellian electron energy distribution function (EEDF), we analyze the spatial profile of plasma parameters in a microdischarge with a 0.2mm gap. There are significant differences in the recorded rate coefficients for dissociative recombination (DR) and three-body recombination (TR), which are essential for determining the densities of excited atoms and molecules in the high-pressure plasma, where the balance of charged particles is determined by volume recombination. A careful examination of the relevant literature reveals these contradictions. In order to adequately represent the main channels of creation and destruction of excited and charged particles, it is necessary to take into account the atomic states of Helium up to the quantum level n=4. The full set of plasma-chemical reactions includes elastic electron collisions, excitation and de-excitation by electrons and atoms, direct and stepwise ionization, associative ionization, molecular excimer creation and destruction, ion conversion, recombination, and radiation. The results reveal that the selected rate coefficients and EEDF form significantly influence the simulation outcomes. This highlights the need to develop improved theoretical models to enhance the control and use of plasma technologies.

Simultaneous sequestration of Cd(II) and Se(IV) by sulfidated nanoscale zerovalent iron impregnated in shrimp shell-derived biochar: Mechanism and site energy distribution analysis

Simultaneous sequestration of Cd(II) and Se(IV) by sulfidated nanoscale zerovalent iron impregnated in shrimp shell-derived biochar: Mechanism and site energy distribution analysis

Enhanced adsorption of tetracycline by lanthanum/iron co-modified rice shell biochar: Synthesis, adsorption performance, site energy distribution and regeneration

A novel La/Fe co-modified biochar derived from rice shell (La/Fe@RSBC) was prepared and employed in tetracycline (TC) adsorption from water. The characterizations, kinetics, isotherms, thermodynamics, and site energy distribution (SED) were studied to investigate TC adsorption behaviors. La/Fe@RSBC exhibited the maximum adsorption capacity towards TC of 414.84 mg/g, which was 1.27 ∼ 2.41 folds than that of RSBC, La@RSBC, and Fe@RSBC. The possible adsorption mechanism of TC dominantly involved H bond, surface complexation, pore filling, electrostatic attraction, and π-π electron donor-acceptor (EDA) interaction. Moreover, TC adsorption behavior was spontaneous and endothermic, significantly related to the compositions and dosage of La/Fe@RSBC, initial pH, and solution temperature. Additionally, SED results promulgated that co-loaded Fe and La synergistically enhanced the affinity of biochar and provided more adsorption sites for TC at a higher temperature. The residual TC after regeneration by ethanol dominantly inhibited the third stage of adsorption, that is, the adsorption of TC on the inner surface of La/Fe@RSBC in next run. Importantly, H2O2 combined with La/Fe@RSBC-mediated advanced oxidation process could effectively clear residual TC after ethanol desorption, which obviously improved the service life of La/Fe@RSBC. In addition, the swine wastewater treatment demonstrated that La/Fe@RSBC had a promising potential application in practical application.

A three-parameter model for dark matter halos based on general relativity and quantum field theory

In this paper, we present a three-parameter model for the distributions of “dark matter” around local spherically symmetric and static distributions of normal matter, usually referred to as “dark matter halos”. The model is based on the assumption that the spontaneous symmetry breaking mechanism involving the Higgs field in the standard model of elementary particle physics leads to the existence of a constant distribution of vacuum energy throughout space–time. This is to be interpreted as a constant distribution of proper energy, as measured by local observers in their proper reference frames. Due to the presence of this universal distribution of vacuum energy, the model requires the introduction of the cosmological term in the Einstein field equations, in order to regulate the behavior of the resulting geometry at cosmologically large distances. By implementing the model on galactic scales, we are able to establish the gravitational consequences of such a homogeneous distribution of proper energy density at these scales. This results in equivalent halo masses and orbital velocity curves that, at least qualitatively, match the observations for galactic “dark matter” halos. Although the detailed realization of the model presented and calculated here cannot be construed as a precise representation of galactic structure, given that most of the actual structure of the galaxy, such as the galactic disk itself, is ignored, and replaced by a single spherically symmetric bulge, in this paper we do introduce the conceptual framework that may be used to construct a more complete galactic model in the future.

Stress Relaxation and Creep Response of Glassy Hydrogels with Dense Physical Associations.

Various glassy hydrogels are developed by forming dense physical associations within the matrices, which exhibit forced elastic deformation and possess high stiffness, strength, and toughness. Here, the viscoplastic behaviors of the glassy hydrogel of poly(methacrylamide-co-methacrylic acid) are investigated by stress relaxation and creep measurements. We found that the characteristic time of stress relaxation of the glassy gel is much smaller than that of amorphous polymers. The varying hydrogen bond strength leads to a broad distribution of structural activation energies, which in turn affects the range of characteristic time. In the presence of water, the weak hydrogen bond associations are easily disrupted under applied strain, enhancing segmental mobility and reducing relaxation time in the preyield regime, while in the postyield regime, the relaxation time increases slightly since the chain stretching increases the energy barrier. In creep tests, the creep strain rate accelerates at the initial stage due to stress-activated segments and then decelerates as chains are extensively stretched. The stress required for structural activation during creep is much lower than the Young's modulus of the gel, reflecting the poor structural stability. To further analyze the underlying mechanism of the glassy gel, a micromechanical model is established based on an extension on shear transformation zone theory. By incorporating a state variable for hydrogen bond density, this model can capture the intricate mechanical responses of glassy gels. Our findings reveal that glassy hydrogels are far from the thermodynamic equilibrium state, exhibiting rapid segment activation under external loading. This work provides insights to the dynamics and structural stability of glassy materials and can promote the design and applications of tough hydrogels.

EROSITA X-ray analysis of the PeVatron candidate Westerlund 1

It is still unclear which fraction of cosmic rays above an energy of electronvolt is accelerated by the observed Galactic PeVatron population. These sources of unknown physical origin are detected through their γ-ray emission, which also identifies them as particle accelerators. However, their γ-ray data are typically degenerate between hadronic and leptonic emission scenarios, which hinders their straightforward association with the mainly hadronic cosmic ray population. In this study, we aimed to distinguish between leptonic and hadronic particle acceleration scenarios for the PeVatron candidate HESS J1646-458, which is associated with the star cluster Westerlund 1 (Wd 1). To this end, we first studied the diffuse X-ray emission from Wd 1 to better understand if its origin is of thermal or nonthermal nature. In addition, we searched for X-ray synchrotron emission from the associated PeVatron candidate HESS J1646-458 to put new constraints on the magnetic field strength and the leptonic particle population of this source. We used data from the all-sky surveys 1 to 4 of the extended Roentgen Survey with an Imaging Telescope Array eROSITA ) on board the Spectrum-Roentgen-Gamma orbital platform to spectrally analyze the diffuse emission from Wd 1 and HESS J1646-458. For Wd 1, we fitted and compared a purely thermal model and a model with a thermal and a nonthermal component. Next, we analyzed the spectra of four annuli around Wd 1 that coincide with HESS J1646-458 to search for synchrotron radiation. We find that eROSITA data cannot distinguish between thermal and nonthermal source scenarios for the diffuse emission from Wd 1 itself. For a thermal source scenario, the observed X-ray flux can be explained in large part by unresolved pre-main sequence stars or by thermalized stellar wind shocks. In the case of the PeVatron candidate HESS J1646-458, we find no evidence of synchrotron emission. We estimated an upper confidence bound of the synchrotron flux up to arcmin around Wd 1 of 1.9e-3 second . We used this result to study the spectral energy distribution of the source. From that, we obtained an upper $1σ$ confidence bound on the magnetic field strength of HESS J1646-458 of SI gauss Our upper bound on the magnetic field strength in HESS J1646-458 is compatible with a previous estimate in the literature for a fully leptonic source scenario. Therefore, a purely leptonic emission scenario is compatible with our results. The same is the case for hadronic and hybrid scenarios, for which even less synchrotron flux is expected compared to the leptonic scenario.

Decentralized Framework for Securing Smart Grids Using Blockchain and Machine Learning

Abstract: The transition to smart grids has revolutionized energy distribution, enabling more efficient and flexible power management through advanced communication and control systems. However, this interconnected structure makes smart grids vulnerable to cyberattacks, such as False Data Injection Attacks (FDIA), Distributed Denial of Service (DDoS) attacks, and data manipulation. These threats undermine the stability and reliability of the grid, and existing centralized security frameworks are often ill-equipped to address them due to their susceptibility to single points of failure and limited scalability. To overcome these challenges, this paper introduces a decentralized security framework that combines blockchain technology with machine learning (ML). The framework leverages blockchain to provide a transparent, immutable, and decentralized ledger, employing consensus mechanisms like Practical Byzantine Fault Tolerance (PBFT) or Proof of Authority (PoA) to ensure secure data validation. Alongside this, ML models, including Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN), are used to detect anomalies in time-series data, such as FDIA, with high precision. Smart contracts embedded in the blockchain enable automated, real-time responses to threats, such as isolating compromised nodes or rerouting energy flows to maintain grid stability. Through simulations replicating real-world cyberattacks, the proposed framework demonstrated over 95% detection accuracy, a 30% reduction in response times, and enhanced computational efficiency with lower energy consumption. These results affirm the effectiveness of the framework as a scalable and resilient solution for modern smart grid security.

The impact of transience in the interaction between orographic gravity waves and mean flow

Abstract A Lagrangian gravity-wave parameterization (MS-GWaM, Multi-Scale Gravity-Wave Model) that allows for fully transient wave-mean-flow interaction and horizontal propagation is applied to orographic gravity waves for the first time. Both linear and nonlinear mountain waves are modeled in idealized simulations within the pseudo-incompressible flow solver PincFlow. Two-dimensional flows over monochromatic orographies are considered, using MS-GWaM either in its fully transient implementation or in a steady-state implementation that represents classic mountain-wave parameterizations. Comparisons of wave-resolving simulations (not using MS-GWaM) and coarse-resolution simulations (using MS-GWaM) show that allowing for transience leads to a significantly more accurate forcing of the resolved mean flow. The parameterization is able to reproduce the transient forcing of linearly generated mountain waves that slowly propagate upwards, in contrast to the instantaneous distribution of wave energy in classic parameterizations. At high altitudes, wave breaking induces a wind reversal that is captured by the transient parameterization but inhibited in steady-state simulations, due to the assumption of critical level formation. This shows that transience can have a substantial impact in the interaction between mountain waves and mean flow.

Rare Occasions: Tidal Disruption Events Rarely Power the AGNs Observed in Dwarf Galaxies

Tidal disruption events (TDEs) could be an important growth channel for massive black holes in dwarf galaxies. Theoretical work suggests that the observed active galactic nuclei (AGNs) in dwarf galaxies are predominantly TDE-powered. To assess this claim, we perform variability analyses on the dwarf-hosted AGNs detected in the 7 Ms Chandra Deep Field-South survey, with observations spanning ≈16 yr. Based on the spectral energy distribution modeling with x-cigale, we select AGNs hosted by dwarf galaxies (stellar mass below 1010 M ⊙). We focus on X-ray sources with full-band detections, leading to a sample of 78 AGNs (0.122 ≤ z ≤ 3.515). We fit the X-ray light curves with a canonical TDE model of t −5/3 and a constant model. If the former outperforms the latter in fitting quality for a source, we consider the source as a potential TDE. We identify five potential TDEs, constituting a small fraction of our sample. Using true- and false-positive rates obtained from fitting models to simulated light curves, we perform Bayesian analysis to obtain the posterior of the TDE fraction for our sample. The posterior peaks close to zero (2.56%), and we obtain a 2σ upper limit of 9.80%. Therefore, our result indicates that the observed AGNs in dwarf galaxies are not predominantly powered by TDEs.

Probing the low-energy particle content of blazar jets through MeV observations

Many of the blazars observed by Fermi actually have the peak of their time-averaged gamma-ray emission outside the ∼GeV Fermi energy range, at ∼MeV energies. The detailed shape of the emission spectrum around the ∼MeV peak places important constraints on acceleration and radiation mechanisms in the blazar jet and may not be the simple broken power law obtained by extrapolating from the observed X-ray and GeV gamma-ray spectra. In particular, state-of-the-art simulations of particle acceleration by shocks show that a significant fraction (possibly up to ≈90%) of the available energy may go into bulk quasi-thermal heating of the plasma crossing the shock rather than producing a nonthermal power-law tail. Other gentler but possibly more pervasive acceleration mechanisms, such as shear acceleration at the jet boundary, may result in a further build-up of the low-energy (γ ≲ 102) particle population in the jet. As already discussed for the case of gamma-ray bursts, the presence of a low-energy Maxwellian-like bump in the jet particle energy distribution can strongly affect the spectrum of the emitted radiation, for example producing an excess over the emission expected from a power-law extrapolation of a blazar’s GeV-TeV spectrum. We explore the potential detectability of the spectral component ascribable to a hot quasi-thermal population of electrons in the high-energy emission of flat-spectrum radio quasars (FSRQs). We show that for the typical physical parameters of FSRQs, the expected spectral signature is located at ∼MeV energies. For the brightest Fermi FSRQ sources, the presence of such a component will be constrained by the upcoming MeV Compton Spectrometer and Imager (COSI) satellite.

Digital transformation in energy systems: a comprehensive review of AI, IoT, blockchain, and decentralised energy models

Digital transformation (DT) in the energy sector is pivotal in meeting energy transformation challenges. DT is reshaping energy production, distribution, and consumption by integrating advanced technologies such as artificial intelligence (AI), the Internet of Things (IoT), blockchain, and digital twins. While existing research has extensively documented individual technological applications, there remains a significant gap in understanding how these technologies interact synergistically in real-world implementations [11]. Comprehensive analyses comparing digital transformation outcomes across different socioeconomic contexts are limited, particularly regarding the scalability of swarm electrification models. These technologies collectively address the ‘three Ds’ – decentralisation, decarbonisation, and digitalisation – essential for the evolution of modern energy systems. By leveraging these innovations, the sector can significantly enhance efficiency, optimise renewable energy integration, and expand access to underserved regions.One of the most impactful applications of DT is in the realm of decentralised energy systems, exemplified by swarm electrification. This concept, pioneered by Groh et al [3], utilises interconnected solar home systems (SHSs) to form scalable microgrids that evolve from standalone setups to full integration with national grids. These systems empower communities by facilitating energy sharing, reducing operational costs, and creating new income streams. Case studies from Kenya, Madagascar, Yemen, Germany, and Bolivia demonstrate the real-world success of swarm electrification in bridging the energy access gap while advancing sustainability goals.AI plays a pivotal role in digital energy systems by enabling predictive maintenance, optimising energy flows, and improving system reliability. Algorithms analyse vast datasets in real time to forecast energy demand, detect anomalies, and automate grid management. IoT further complements AI by providing the physical infrastructure to gather and transmit data, enabling real-time monitoring and control of energy assets. Together, AI and IoT support the development of smart grids and energy communities, fostering greater flexibility and resilience in energy networks.Blockchain technology is emerging as a transformative tool for energy trading and distribution. By enabling peer-to-peer (P2P) energy markets, blockchain enhances transparency and reduces transaction costs. This decentralisation of energy trading allows consumers to become prosumers, actively participating in energy production and exchange. Projects such as Esmat et al. decentralised platforms exemplify how blockchain empowers individuals and communities to take ownership of their energy futures while ensuring security and scalability.Despite these advancements, the implementation of digital technologies in energy systems is facing significant challenges. High initial costs, the complexity of integration, and cybersecurity risks pose barriers to widespread deployment. Furthermore, the digital divide in underserved regions limits equitable access to these transformative solutions. Environmental concerns related to the energy consumption of digital infrastructures, such as data centres and blockchain networks, also require attention. Addressing these issues necessitates a multi-stakeholder approach involving policymakers, industry leaders, and researchers to create enabling environments for innovation.This review provides a comprehensive analysis of the role of DT in advancing energy systems, focusing on AI, IoT, blockchain, and swarm electrification. It synthesises insights from over 100 scholarly sources, including real-world case studies, and evaluates the social, economic and technological impact of digitalisation on energy systems. The study adopts a mixed-method approach, integrating literature analysis, quantitative modelling, and case study evaluations to provide actionable insights for policymakers and industry practitioners.The findings of this review highlight the transformative potential of DT in addressing energy challenges, particularly in achieving the United Nations Sustainable Development Goal 7 [2]: universal access to affordable, reliable, and modern energy. By adopting digital innovations, energy providers can enhance operational efficiency, integrate renewable energy sources, and support community-based energy initiatives. The concept of swarm electrification exemplifies how decentralised approaches can complement centralised grids, ensuring scalability and adaptability to local needs.Policy recommendations emphasise the need for financial incentives, capacity-building programmes, and regulatory frameworks to facilitate digital adoption. Investment in human capital is particularly critical, as skilled personnel are required to implement and manage complex digital systems. International cooperation and knowledge sharing are essential to ensure digital transformation efforts align with global sustainability goals.In conclusion, DT represents a paradigm shift in energy systems, offering solutions to some of the sector’s most pressing challenges. Realising its full potential requires overcoming technical, financial, and institutional barriers. This review underscores the importance of a collaborative, multidisciplinary approach to harnessing the power of digital technologies for sustainable energy

Sines, transient, noise neural modeling of piano notes

This article introduces a novel method for emulating piano sounds. We propose to exploit the sine, transient, and noise decomposition to design a differentiable spectral modeling synthesizer replicating piano notes. Three sub-modules learn these components from piano recordings and generate the corresponding quasi-harmonic, transient, and noise signals. Splitting the emulation into three independently trainable models reduces the modeling tasks’ complexity. The quasi-harmonic content is produced using a differentiable sinusoidal model guided by physics-derived formulas, whose parameters are automatically estimated from audio recordings. The noise sub-module uses a learnable time-varying filter, and the transients are generated using a deep convolutional network. From singular notes, we emulate the coupling between different keys in trichords with a convolutional-based network. Results show the model matches the partial distribution of the target while predicting the energy in the higher part of the spectrum presents more challenges. The energy distribution in the spectra of the transient and noise components is accurate overall. While the model is more computationally and memory efficient, perceptual tests reveal limitations in accurately modeling the attack phase of notes. Despite this, it generally achieves perceptual accuracy in emulating single notes and trichords.

Quantum-inspired fluid simulation of two-dimensional turbulence with GPU acceleration

Tensor network algorithms can efficiently simulate complex quantum many-body systems by utilizing knowledge of their structure and entanglement. These methodologies have been adapted recently for solving the Navier-Stokes equations, which describe a spectrum of fluid phenomena, from the aerodynamics of vehicles to weather patterns. Within this quantum-inspired paradigm, velocity is encoded as matrix product states (MPS), effectively harnessing the analogy between interscale correlations of fluid dynamics and entanglement in quantum many-body physics. This particular tensor structure is also called quantics tensor train (QTT). By utilizing NVIDIA's cuQuantum library to perform parallel tensor computations on GPUs, our adaptation speeds up simulations by up to 12.1 times. This allows us to study the algorithm in terms of its applicability, scalability, and performance. By simulating two qualitatively different but commonly encountered 2D flow problems at high Reynolds numbers up to 1×107 using a fourth-order time stepping scheme, we find that the algorithm has a potential advantage over direct numerical simulations in the turbulent regime as the requirements for grid resolution increase drastically. In addition, we derive the scaling χ=O(poly(1/ε)) for the maximum bond dimension χ of MPS representing turbulent flow fields, with an error ε, based on the spectral distribution of turbulent kinetic energy. Our findings motivate further exploration of related quantum algorithms and other tensor network methods. Published by the American Physical Society 2025

Optimized Multi‐Scale Attention Convolutional Neural Network for Micro‐Grid Energy Management System Employing in Internet of Things

ABSTRACTThe combination of micro‐grid energy management systems (EMSs) with the Internet of Things (IoT) offers a promising way to improve energy use and distribution. However, challenges such as device compatibility and the difficulty of managing energy efficiently make it hard to implement these systems effectively. This study offers a significant advancement in energy management by using IoT for microgrid systems. An Optimized Multi‐scale Attention Convolutional Neural Network for microgrid EMS employing IoT (OMACNN‐MGEMS‐IoT) is proposed in this study, which enables efficient monitoring and control of energy resources. The proposed model's input data are gathered from the MQTT dataset. This research employs a Regularized Bias‐aware Ensemble Kalman Filter (RBAEKF) for pre‐processing input data, ensuring the removal of outliers and updating missing values. The MACNN is then used for effective fault detection within the microgrid. To enhance its performance, the Sheep Flock Optimization Algorithm (SFOA) is introduced to optimize the MACNN parameters, ensuring accurate fault detection. Implemented on the MATLAB platform, the performance of the OMACNN‐MGEMS‐IoT method is assessed through various performance metrics, demonstrating significant improvements. Notably, the proposed method achieves higher cost reductions of 25%, 22%, and 26% compared to existing approaches such as the IoT platform for energy management in multi‐micro grid systems (IoT‐PEM‐MMS), a micro‐grid system infrastructure implementing IoT for efficient energy management in buildings (MSII‐IoT‐EEM) and a hybrid deep learning‐based online energy management scheme for industrial microgrids (HDL‐OEM‐IM). The findings highlight the impact of the proposed OMACNN‐MGEMS‐IoT method in enhancing energy efficiency and cost‐effectiveness in microgrid systems.

H-AMR FORGE’d in FIRE. I. Magnetic State Transitions, Jet Launching, and Radiative Emission in Super-Eddington, Highly Magnetized Quasar Disks Formed from Cosmological Initial Conditions

Abstract Quasars are powered by supermassive black hole (SMBH) accretion disks, yet standard thin disk models are inconsistent with many observations. Recently, P. F. Hopkins et al. simulated the formation of a quasar disk feeding an SMBH of mass M = 1.3 × 107 M ⊙ in a galaxy. The disk had surprisingly strong toroidal magnetic fields that supported it vertically from gravity and powered rapid accretion. What feedback can such a system produce? To answer this, we must follow the gas to the event horizon. For this, we interpolated the quasar into the general-relativistic radiation magnetohydrodynamics code H-AMR and performed 3D simulations with BH spins a = 0 and a = 0.9375. This remapping generates magnetic monopoles, which we erase using a novel divergence cleaning approach. Despite the toroidal magnetic field's dominance at large radii, vertical magnetic flux builds up near the event horizon, leading to a magnetic state transition within the inner 200 gravitational radii of the disk. This powers strong winds and, for spinning BHs, relativistic jets that can spin down the BH within 5−10 Myr. Sometimes, vertical magnetic fields of opposite polarity reach the BH, causing a polarity inversion event that briefly destroys the jets and, possibly, the X-ray corona. These strong fields power accretion at rates 5× the Eddington limit, which can double the BH mass in 5–10 Myr. When a = 0.9375 (a = 0), the energy in mechanical outflows and radiation equals about 60% (10%) and 100% (3%) of the accreted rest mass energy, respectively. Much of the light escapes in cool, ≳1300 au photospheres, consistent with quasar microlensing and spectral energy distributions.

SCUBADive. I. JWST+ALMA Analysis of 289 Submillimeter Galaxies in COSMOS-web

Abstract JWST has enabled detecting and spatially resolving the heavily dust-attenuated stellar populations of submillimeter galaxies, revealing detail that was previously inaccessible. In this work, we construct a sample of 289 submillimeter galaxies with joint Atacama Large Millimeter/submillimeter Array (ALMA) and JWST constraints in the COSMOS field. Sources are originally selected using the SCUBA-2 instrument and have archival ALMA observations from various programs. Their JWST NIRCam imaging is from COSMOS-Web and PRIMER. We extract multiwavelength photometry in a manner that leverages the unprecedented near-infrared (NIR) spatial resolution of JWST, and we fit the data with spectral energy distribution models to derive photometric redshifts, stellar masses, star formation rates, and optical attenuation. The sample has an average 〈 z 〉 = 2 . 6 − 0.8 + 1.0 , 〈 A V 〉 = 2 . 5 − 1.0 + 1.5 , 〈 SFR 〉 = 30 0 − 200 + 400 M ⊙ yr − 1 , and 〈 log ( M * / M ⊙ ) 〉 = 11 . 1 − 0.5 + 0.3 . There are 81 (30%) galaxies that have no previous optical/NIR detections, including 75% of the z > 4 subsample (n = 28). The faintest observed NIR sources have the highest redshifts and largest A V = 4 ± 1. In a preliminary morphology analysis we find that ∼10% of the members of our sample exhibit spiral arms and 5% host stellar bars, with one candidate bar found at z > 3. Finally, we find that the clustering of JWST sources within 10″ of a submillimeter galaxy is a factor of 2 greater than what is expected based on either random clustering or the distribution of sources around any red galaxy irrespective of a submillimeter detection.