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The first very long baseline interferometry (VLBI) detections at 870$\mu$m wavelength (345$\,$GHz frequency) are reported, achieving the highest diffraction-limited angular resolution yet obtained from the surface of the Earth, and the highest-frequency example of the VLBI technique to date. These include strong detections for multiple sources observed on inter-continental baselines between telescopes in Chile, Hawaii, and Spain, obtained during observations in October 2018. The longest-baseline detections approach 11$\,$G$\lambda$ corresponding to an angular resolution, or fringe spacing, of 19$\mu$as. The Allan deviation of the visibility phase at 870$\mu$m is comparable to that at 1.3$\,$mm on the relevant integration time scales between 2 and 100$\,$s. The detections confirm that the sensitivity and signal chain stability of stations in the Event Horizon Telescope (EHT) array are suitable for VLBI observations at 870$\mu$m. Operation at this short wavelength, combined with anticipated enhancements of the EHT, will lead to a unique high angular resolution instrument for black hole studies, capable of resolving the event horizons of supermassive black holes in both space and time., Comment: Corresponding author: S. Doeleman

A new image-reconstruction algorithm, PRIMO, applied to the interferometric data of the M87 black hole collected with the Event Horizon Telescope (EHT), resulted in an image that reached the native resolution of the telescope array. PRIMO is based on learning a compact set of image building blocks obtained from a large library of high-fidelity, physics-based simulations of black hole images. It uses these building blocks to fill the sparse Fourier coverage of the data that results from the small number of telescopes in the array. In this paper, we show that this approach is readily justified. Since the angular extent of the image of the black hole and of its inner accretion flow is finite, the Fourier space domain is heavily smoothed, with a correlation scale that is at most comparable to the sizes of the data gaps in the coverage of Fourier space with the EHT. Consequently, PRIMO or other machine-learning algorithms can faithfully reconstruct the images without the need to generate information that is unconstrained by the data within the resolution of the array. We also address the completeness of the eigenimages and the compactness of the resulting representation. We show that PRIMO provides a compact set of eigenimages that have sufficient complexity to recreate a broad set of images well beyond those in the training set., Comment: Submitted to the Astrophysical Journal

A fundamental challenge in endoscopy is how to fabricate a small fiber-optic probe that can achieve comparable function to probes with large, complicated optics (e.g., high resolution and extended depth of focus). To achieve high resolution over an extended depth of focus (DOF), the application of needle-like beams has been proposed. However, existing methods using miniaturized needle beam designs fail to adequately correct astigmatism and other monochromatic aberrations, limiting the resolution of at least one axis. Here, we describe a novel approach to realize freeform beam-shaping endoscopic probes via two-photon direct laser writing, also known as micro 3D-printing. We present a design achieving approximately 8-micron resolution with a DOF of >0.8 mm at a central wavelength of 1310 nm. The probe has a diameter of 0.25 mm (without the catheter sheaths) and is fabricated using a single printing step directly on the optical fiber. We demonstrate our device in intravascular imaging of living atherosclerotic pigs at multiple time points, as well as human arteries with plaques ex vivo. This is the first step to enable beam-tailoring endoscopic probes which achieve diffraction-limited resolution over a large DOF.

Diffusion models generate new samples by progressively decreasing the noise from the initially provided random distribution. This inference procedure generally utilizes a trained neural network numerous times to obtain the final output, creating significant latency and energy consumption on digital electronic hardware such as GPUs. In this study, we demonstrate that the propagation of a light beam through a semi-transparent medium can be programmed to implement a denoising diffusion model on image samples. This framework projects noisy image patterns through passive diffractive optical layers, which collectively only transmit the predicted noise term in the image. The optical transparent layers, which are trained with an online training approach, backpropagating the error to the analytical model of the system, are passive and kept the same across different steps of denoising. Hence this method enables high-speed image generation with minimal power consumption, benefiting from the bandwidth and energy efficiency of optical information processing., Comment: 17 pages, 10 figures

Aims: The presence of coronary tandem lesions poses a significant challenge for the accurate diagnosis and management of coronary artery diseases. This study set out to provide a deeper understanding of the haemodynamic interactions between tandem obstructive coronary lesions and their impact on different haemodynamic diagnostic parameters. Methods and Results: Using a computational fluid dynamic model, validated against in vitro laboratory experiments, we investigated the various combinations of moderate and severe stenoses interchangeably located in the proximal and distal segments of the artery. The investigation was conducted using two diagnostic parameters: one hyperaemic-based, i.e., FFR, and one rest-based, i.e., iFR, technique, both of which are commonly used to assess the physiological significance of coronary stenoses. The three main findings of this work are: (a) the recovery distance (the immediate local distance affected by the presence of stenosis) is much shorter for the rest-measured diagnostic parameter compared with the hyperaemic-measured diagnostic parameter; (b) pressure drop measurements immediately downstream of the stenotic sections overestimate the significance of stenoses, and (c) the presence of a moderate stenosis downstream of a severe stenosis increases FFR value (faster FFR recovery). Conclusion: These findings enhance our understanding of the diagnostic accuracy of hyperaemic-based and rest-based physiological diagnostic coronary assessments and the nuances of using these different techniques when assessing tandem coronary stenoses. This understanding can help inform tailored therapeutic approaches for the management of coronary artery disease.

The ring-like images of the two supermassive black holes captured by the Event Horizon Telescope (EHT) provide powerful probes of the physics of accretion flows at horizon scales. Specifically, the brightness asymmetry in the images carries information about the angular velocity profile of the inner accretion flow and the inclination of the observer, owing to the Doppler boosts photons experience at their site of emission. In this paper, we develop a method for quantifying the brightness asymmetry of black-hole images in the Fourier domain, which can be measured directly from interferometric data. We apply this method to current EHT data and find that the image of Sagittarius A* (Sgr A*) has an unusually low degree of asymmetry that is even lower than that inferred for M87. We then use a covariant semi-analytic model to obtain constraints on the inclinations and velocity profiles of the inner accretion flow for Sgr A*. We find that the lack of significant brightness asymmetry forces the observer inclination to uncomfortably small values ($6-10^\circ$), if the plasma velocity follows Keplerian profiles. Alternatively, larger inclination angles can be accommodated if the plasma velocities are significantly sub-Keplerian and the black hole is not spinning rapidly., Comment: 12 pages, 5 figures. Submitted to ApJ

Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern AI. Could we train AI models 1000x larger than current ones? Could we do this and also have them perform inference locally and privately on edge devices, such as smartphones or sensors? Research over the past few years has shown that the answer to all these questions is likely "yes, with enough research": PNNs could one day radically change what is possible and practical for AI systems. To do this will however require rethinking both how AI models work, and how they are trained - primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs at large scale, many methods including backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs, and so far no method has been shown to scale to the same scale and performance as the backpropagation algorithm widely used in deep learning today. However, this is rapidly changing, and a diverse ecosystem of training techniques provides clues for how PNNs may one day be utilized to create both more efficient realizations of current-scale AI models, and to enable unprecedented-scale models., Comment: 29 pages, 4 figures

Optical diffraction tomography (ODT) has emerged as an important label-free tool in biomedicine to measure the three-dimensional (3D) structure of a biological sample. In this paper, we describe ODT using second-harmonic generation (SHG) which is a coherent nonlinear optical process with a strict symmetry selectivity and has several advantages over traditional fluorescence methods. We report the tomographic retrieval of the 3D second-order nonlinear optical susceptibility using two-dimensional holographic measurements of the SHG fields at different illumination angles and polarization states. The method is a generalization of the conventional linear ODT to the nonlinear scenario. We demonstrate the method with a numerically simulated nanoparticle distribution and an experiment with muscle tissue fibers. Our results show that SHG ODT does not only provide an effective contrast mechanism for label-free imaging but also due to the symmetry requirement enables the visualization of properties that are not otherwise accessible.

Low density plasmas in curved spacetimes, such as those found in accretion flows around black holes, are challenging to model from first principles, owing to the large scale separation between the characteristic scales of the microscopic processes and large mean-free-paths comparable to the system sizes. Kinetic approaches become necessary to capture the relevant physics but lack the dynamic range to model the global characteristics of the systems. In this paper, we develop new covariant guiding center equations of motion for charges in general relativistic spacetimes that are computationally tractable. We decompose the particle motion into a fast gyration, which we integrate analytically and a slow drift of the guiding center, which can be solved numerically. We derive covariant conservation laws for the motions of the guiding centers and show, through a number of limiting cases, that the equations contain all known drift mechanisms. Finally, we present the general relativistic expressions for the various drift velocities in Schwarzschild spacetimes., Comment: Submitted to ApJ

We present here an extended nuclear network, with 90 species, designed for being coupled with hydrodynamic simulations, which includes neutrons, protons, electrons, positrons, and the corresponding neutrino and anti-neutrino emission. This network is also coupled with temperature, making it extremely robust and, together with its size, unique of its kind. The inclusion of electron captures on free protons makes the network very appropriate for multidimensional studies of Type Ia supernova explosions, especially when the exploding object is a massive white dwarf. The results obtained with the proposed medium-sized network compare fairly well, to a few percent, with those computed with the extended network WinNet (> 2000 isotopes) in scenarios reproducing the gross physical conditions of current Type Ia supernova explosion models. In those cases where the carbon and oxygen fuel ignites at high density, the high-temperature plateau typical of the nuclear statistical equilibrium regime is well defined and stable, allowing large integration time steps. We show that the inclusion of electron captures on free protons substantially improves the estimation of the electron fraction of the mixture. Therefore, the pressure is better determined than in networks where electron captures are excluded, which will ultimately lead to more reliable hydrodynamic models. Explosive combustion of helium at low density, occurring near the surface layer of a white dwarf, is also better described with the proposed network, which gives nuclear energy generation rates much closer to WinNet than typical reduced alpha networks., Comment: 19 pages, 21 Figures and 5 Tables. Accepted for publication in A&A

Electronic computers have evolved drastically over the past years with an ever-growing demand for improved performance. However, the transfer of information from memory and high energy consumption have emerged as issues that require solutions. Optical techniques are considered promising solutions to these problems with higher speed than their electronic counterparts and with reduced energy consumption. Here, we use the optical reservoir computing framework we have previously described (Scalable Optical Learning Operator or SOLO) to program the spatial-spectral output of the light after nonlinear propagation in a multimode fiber. The novelty in the current paper is that the system is programmed through an output sampling scheme, similar to that used in hyperspectral imaging in astronomy. Linear and nonlinear computations are performed by light in the multimode fiber and the high dimensional spatial-spectral information at the fiber output is optically programmed before it reaches the camera. We then used a digital computer to classify the programmed output of the multi-mode fiber using a simple, single layer network. When combining front-end programming and the proposed spatial-spectral programming, we were able to achieve 89.9% classification accuracy on the dataset consisting of chest X-ray images from COVID-19 patients. At the same time, we obtained a decrease of 99% in the number of tunable parameters compared to an equivalently performing digital neural network. These results show that the performance of programmed SOLO is comparable with cutting-edge electronic computing platforms, albeit with a much-reduced number of electronic operations.

The partition of turbulent heating between ions and electrons in radiatively inefficient accretion flows plays a crucial role in determining the observational appearance of accreting black holes. Modeling this partition is, however, a challenging problem because of the large scale separation between the macroscopic scales at which energy is injected by turbulence and the microscopic ones at which it is dissipated into heat. Recent studies of particle heating from collisionless damping of turbulent energy have shown that the partition of energy between ions and electrons is dictated by the ratio of the energy injected into the slow and Alfv\'en wave cascades as well as the plasma $\beta$ parameter. In this paper, we study the mechanism of the injection of turbulent energy into slow- and Alfv\'en- wave cascades in magnetized shear flows. We show that this ratio depends on the particular ($r\phi$) components of the Maxwell and Reynolds stress tensors that cause the transport of angular momentum, the shearing rate, and the orientation of the mean magnetic field with respect to the shear. We then use numerical magnetohydrodynamic shearing-box simulations with background conditions relevant to black hole accretion disks to compute the magnitudes of the stress tensors for turbulence driven by the magneto-rotational instability and derive the injection power ratio between slow and Alfv\'en wave cascades. We use these results to formulate a local subgrid model for the ion-to-electron heating ratio that depends on the macroscopic characteristics of the accretion flow., Comment: Accepted for publication in ApJ

3C84 is a nearby radio source with a complex total intensity structure, showing linear polarisation and spectral patterns. A detailed investigation of the central engine region necessitates the use of VLBI above the hitherto available maximum frequency of 86GHz. Using ultrahigh resolution VLBI observations at the highest available frequency of 228GHz, we aim to directly detect compact structures and understand the physical conditions in the compact region of 3C84. We used EHT 228GHz observations and, given the limited (u,v)-coverage, applied geometric model fitting to the data. We also employed quasi-simultaneously observed, multi-frequency VLBI data for the source in order to carry out a comprehensive analysis of the core structure. We report the detection of a highly ordered, strong magnetic field around the central, SMBH of 3C84. The brightness temperature analysis suggests that the system is in equipartition. We determined a turnover frequency of $\nu_m=(113\pm4)$GHz, a corresponding synchrotron self-absorbed magnetic field of $B_{SSA}=(2.9\pm1.6)$G, and an equipartition magnetic field of $B_{eq}=(5.2\pm0.6)$G. Three components are resolved with the highest fractional polarisation detected for this object ($m_\textrm{net}=(17.0\pm3.9)$%). The positions of the components are compatible with those seen in low-frequency VLBI observations since 2017-2018. We report a steeply negative slope of the spectrum at 228GHz. We used these findings to test models of jet formation, propagation, and Faraday rotation in 3C84. The findings of our investigation into different flow geometries and black hole spins support an advection-dominated accretion flow in a magnetically arrested state around a rapidly rotating supermassive black hole as a model of the jet-launching system in the core of 3C84. However, systematic uncertainties due to the limited (u,v)-coverage, however, cannot be ignored., Comment: 15 pages, 6 figures, published in A&A

Artificial Intelligence (AI) demands large data flows within datacenters, heavily relying on multicasting data transfers. As AI models scale, the requirement for high-bandwidth and low-latency networking compounds. The common use of electrical packet switching faces limitations due to optical-electrical-optical conversion bottlenecks. Optical switches, while bandwidth-agnostic and low-latency, suffer from having only unicast or non-scalable multicasting capability. This paper introduces an optical switching technique addressing this challenge. Our approach enables arbitrarily programmable simultaneous unicast and multicast connectivity, eliminating the need for optical splitters that hinder scalability due to optical power loss. We use phase modulation in multiple layers, tailored to implement any multicast connectivity map. Phase modulation also enables wavelength selectivity on top of spatial selectivity, resulting in an optical switch that implements space-wavelength routing. We conducted simulations and experiments to validate our approach. Our results affirm the concept's feasibility, effectiveness, and scalability, as a multicasting switch by experimentally demonstrating 16 spatial ports using 2 wavelength channels. Numerically, 64 spatial ports with 4 wavelength channels each were simulated, with approximately constant efficiency (< 3 dB) as ports and wavelength channels scale., Comment: 17 pages, 4 figures, article

Phase imaging is widely used in biomedical imaging, sensing, and material characterization, among other fields. However, direct imaging of phase objects with subwavelength resolution remains a challenge. Here, we demonstrate subwavelength imaging of phase and amplitude objects based on all-optical diffractive encoding and decoding. To resolve subwavelength features of an object, the diffractive imager uses a thin, high-index solid-immersion layer to transmit high-frequency information of the object to a spatially-optimized diffractive encoder, which converts/encodes high-frequency information of the input into low-frequency spatial modes for transmission through air. The subsequent diffractive decoder layers (in air) are jointly designed with the encoder using deep-learning-based optimization, and communicate with the encoder layer to create magnified images of input objects at its output, revealing subwavelength features that would otherwise be washed away due to diffraction limit. We demonstrate that this all-optical collaboration between a diffractive solid-immersion encoder and the following decoder layers in air can resolve subwavelength phase and amplitude features of input objects in a highly compact design. To experimentally demonstrate its proof-of-concept, we used terahertz radiation and developed a fabrication method for creating monolithic multi-layer diffractive processors. Through these monolithically fabricated diffractive encoder-decoder pairs, we demonstrated phase-to-intensity transformations and all-optically reconstructed subwavelength phase features of input objects by directly transforming them into magnified intensity features at the output. This solid-immersion-based diffractive imager, with its compact and cost-effective design, can find wide-ranging applications in bioimaging, endoscopy, sensing and materials characterization., Comment: 32 Pages, 9 Figures

The elemental abundances between strontium and silver ($Z = 38-47$) observed in the atmospheres of very metal-poor stars (VMP) in the Galaxy may contain the fingerprint of the weak $r$-process and $\nu p$-process occurring in early core-collapse supernovae explosions. In this work, we combine various astrophysical conditions based on a steady-state model to cover the richness of the supernova ejecta in terms of entropy, expansion timescale, and electron fraction. The calculated abundances based on different combinations of conditions are compared with stellar observations with the aim of constraining supernova ejecta conditions. We find that some conditions of the neutrino-driven outflows consistently reproduce the observed abundances of our sample. In addition, from the successful combinations, the neutron-rich trajectories better reproduce the observed abundances of Sr-Zr ($Z= 38-40$), while the proton-rich ones, Mo-Pd ($Z= 42-47$)., Comment: 18 pages, 11 figures, submitted to ApJ

We demonstrate the fabrication of volume holograms using 2-photon polymerization with dynamic control of light exposure. We refer to our method as (3+1)D printing. Volume holograms that are recorded by interfering reference and signal beams have a diffraction efficiency relation that is inversely proportional with the square of the number of superimposed holograms. By using (3+1)D printing for fabrication, the refractive index of each voxel is created independently and thus by, digitally filtering the undesired interference terms, the diffraction efficiency is now inversely proportional to the number of multiplexed gratings. We experimentally demonstrated this linear dependence by recording M=50 volume gratings. To the best of our knowledge, this is the first experimental demonstration of distributed volume holograms that overcome the 1/M^2 limit., Comment: 8 pages, 9 figures

The low-lying structure of $^{13}$Be has remained an enigma for decades. Despite numerous experimental and theoretical studies, large inconsistencies remain. Being both unbound, and one neutron away from $^{14}$Be, the heaviest bound beryllium nucleus, $^{13}$Be is difficult to study through simple reactions with weak radioactive ion beams or more complex reactions with stable-ion beams. Here, we present the results of a study using the $^{12}$Be(d,p)$^{13}$Be reaction in inverse kinematics using a 9.5~MeV per nucleon $^{12}$Be beam from the ISAC-II facility. The solid deuteron target of IRIS was used to achieve an increased areal thickness compared to conventional deuterated polyethylene targets. The Q-value spectrum below -4.4~MeV was analyzed using a Bayesian method with GEANT4 simulations. A three-point angular distribution with the same Q-value gate was fit with a mixture of $s$- and $p$-wave, $s$- and $d$-wave, or pure $p$-wave transfer. The Q-value spectrum was also compared with GEANT simulations obtained using the energies and widths of states reported in four previous works. It was found that our results are incompatible with works that revealed a wide $5/2^+$ resonance but shows better agreement with ones that reported a narrower width., Comment: 10 pages, 5 figures

Low density plasmas are characterized by a large scale separation between the gyromotion of particles around local magnetic fields and the macroscopic scales of the system, often making global kinetic simulations computationally intractable. The guiding center formalism has been proposed as a powerful tool to bridge the gap between these scales. Despite its usefulness, the guiding center approach has been formulated successfully only in flat spacetimes, limiting its applicability in astrophysical settings. Here, we present a new covariant formalism that leads to kinetic equations in the guiding center limit that are valid in arbitrary spacetimes. Through a variety of experiments, we demonstrate that our equations capture all known gyro-center drifts while overcoming one severe limitation imposed on numerical algorithms by the fast timescales of the particle gyromotion. This formalism will enable explorations of a variety of global plasma kinetic phenomena in the curved spacetimes around black holes and neutron stars., Comment: Accepted for publication in ApJL

The Event Horizon Telescope (EHT) observed in 2017 the supermassive black hole at the center of the Milky Way, Sagittarius A* (Sgr A*), at a frequency of 228.1 GHz ($\lambda$=1.3 mm). The fundamental physics tests that even a single pulsar orbiting Sgr A* would enable motivate searching for pulsars in EHT datasets. The high observing frequency means that pulsars - which typically exhibit steep emission spectra - are expected to be very faint. However, it also negates pulse scattering, an effect that could hinder pulsar detections in the Galactic Center. Additionally, magnetars or a secondary inverse Compton emission could be stronger at millimeter wavelengths than at lower frequencies. We present a search for pulsars close to Sgr A* using the data from the three most-sensitive stations in the EHT 2017 campaign: the Atacama Large Millimeter/submillimeter Array, the Large Millimeter Telescope and the IRAM 30 m Telescope. We apply three detection methods based on Fourier-domain analysis, the Fast-Folding-Algorithm and single pulse search targeting both pulsars and burst-like transient emission; using the simultaneity of the observations to confirm potential candidates. No new pulsars or significant bursts were found. Being the first pulsar search ever carried out at such high radio frequencies, we detail our analysis methods and give a detailed estimation of the sensitivity of the search. We conclude that the EHT 2017 observations are only sensitive to a small fraction ($\lesssim$2.2%) of the pulsars that may exist close to Sgr A*, motivating further searches for fainter pulsars in the region., Comment: 33 pages, 7 figures, 6 Tables. Accepted for publication in ApJ

Deep neural networks have achieved remarkable breakthroughs by leveraging multiple layers of data processing to extract hidden representations, albeit at the cost of large electronic computing power. To enhance energy efficiency and speed, the optical implementation of neural networks aims to harness the advantages of optical bandwidth and the energy efficiency of optical interconnections. In the absence of low-power optical nonlinearities, the challenge in the implementation of multilayer optical networks lies in realizing multiple optical layers without resorting to electronic components. In this study, we present a novel framework that uses multiple scattering that is capable of synthesizing programmable linear and nonlinear transformations concurrently at low optical power by leveraging the nonlinear relationship between the scattering potential, represented by data, and the scattered field. Theoretical and experimental investigations show that repeating the data by multiple scattering enables non-linear optical computing at low power continuous wave light. Moreover, we empirically found that scaling of this optical framework follows the power law as in state-of-the-art deep digital networks., Comment: 15 pages and 5 figures

Among the existing modalities for 3D action recognition, 3D flow has been poorly examined, although conveying rich motion information cues for human actions. Presumably, its susceptibility to noise renders it intractable, thus challenging the learning process within deep models. This work demonstrates the use of 3D flow sequence by a deep spatiotemporal model and further proposes an incremental two-level spatial attention mechanism, guided from skeleton domain, for emphasizing motion features close to the body joint areas and according to their informativeness. Towards this end, an extended deep skeleton model is also introduced to learn the most discriminant action motion dynamics, so as to estimate an informativeness score for each joint. Subsequently, a late fusion scheme is adopted between the two models for learning the high level cross-modal correlations. Experimental results on the currently largest and most challenging dataset NTU RGB+D, demonstrate the effectiveness of the proposed approach, achieving state-of-the-art results., Comment: 18 pages, 3 figures, 3 tables, conference

The $\gamma$-process nucleosynthesis in core-collapse supernovae is generally accepted as a feasible process for the synthesis of neutron-deficient isotopes beyond iron. However, crucial discrepancies between theory and observations still exist: the average production of $\gamma$-process yields from massive stars are too low to reproduce the solar distribution in galactic chemical evolution calculations, and the yields of the Mo and Ru isotopes are by a further factor of 10 lower than the yields of the other $\gamma$-process nuclei. We investigate the $\gamma$-process in 5 sets of core-collapse supernova models published in literature with initial masses 15, 20, and 25 M$_{\odot}$ at solar metallicity. We compared the $\gamma$-process overproduction factors from the different models. To highlight the possible effect of nuclear physics input, we also considered 23 ratios of two isotopes close to each other in mass, relative to their solar values. Further, we investigated the contribution of C-O shell mergers in the supernova progenitors as an additional site of the $\gamma$-process. Our analysis shows that a large scatter among the different models exists for both the $\gamma$-process integrated yields and the isotopic ratios. We found only 10 ratios that agree with their solar values, all the others differ by at least a factor of 3 from the solar values in all the considered sets of models. The $\gamma$-process within C-O shell mergers mostly influence the isotopic ratios that involve intermediate and heavy proton-rich isotopes with $\rm A>100$., Comment: Accepted for publication in A&A. 20 pages, 17 figures

Neural networks (NN) have demonstrated remarkable capabilities in various tasks, but their computation-intensive nature demands faster and more energy-efficient hardware implementations. Optics-based platforms, using technologies such as silicon photonics and spatial light modulators, offer promising avenues for achieving this goal. However, training multiple trainable layers in tandem with these physical systems poses challenges, as they are difficult to fully characterize and describe with differentiable functions, hindering the use of error backpropagation algorithm. The recently introduced Forward-Forward Algorithm (FFA) eliminates the need for perfect characterization of the learning system and shows promise for efficient training with large numbers of programmable parameters. The FFA does not require backpropagating an error signal to update the weights, rather the weights are updated by only sending information in one direction. The local loss function for each set of trainable weights enables low-power analog hardware implementations without resorting to metaheuristic algorithms or reinforcement learning. In this paper, we present an experiment utilizing multimode nonlinear wave propagation in an optical fiber demonstrating the feasibility of the FFA approach using an optical system. The results show that incorporating optical transforms in multilayer NN architectures trained with the FFA, can lead to performance improvements, even with a relatively small number of trainable weights. The proposed method offers a new path to the challenge of training optical NNs and provides insights into leveraging physical transformations for enhancing NN performance.

Low-energy investigations on rare ion beams are often limited by the available intensity and purity of the ion species in focus. Here, we present the first application of a technique that combines in-flight production at relativistic energies with subsequent secondary beam storage, accumulation and finally deceleration to the energy of interest. Using the FRS and ESR facilities at GSI, this scheme was pioneered to provide a secondary beam of $^{118}$Te$^{52+}$ for the measurement of nuclear proton-capture at energies of 6 and 7 MeV/u. The technique provided stored beam intensities of about $10^6$ ions at high purity and brilliance, representing a major step towards low-energy nuclear physics studies using rare ion beams.

In the collisionless plasmas of radiatively inefficient accretion flows, heating and acceleration of ions and electrons is not well understood. Recent studies in the gyrokinetic limit revealed the importance of incorporating both the compressive and Alfvenic cascades when calculating the partition of dissipated energy between the plasma species. In this paper, we use a covariant analytic model of the accretion flow to explore the impact of compressive and Alfvenic heating, Coulomb collisions, compressional heating, and radiative cooling on the radial temperature profiles of ions and electrons. We show that, independent of the partition of heat between the plasma species, even a small fraction of turbulent energy dissipated to the electrons makes their temperature scale with a virial profile and the ion-to-electron temperature ratio smaller than in the case of pure Coulomb heating. In contrast, the presence of compressive cascades makes this ratio larger because compressive turbulent energy is channeled primarily into the ions. We calculate the ion-to-electron temperature in the inner accretion flow for a broad range of plasma properties, mass accretion rates, and black hole spins and show that it ranges between $5 \lesssim T_i/T_e \lesssim 40$. We provide a physically motivated expression for this ratio that can be used to calculate observables from simulations of black hole accretion flows for a wide range of conditions., Comment: Accepted for publication in ApJ

The Kerr-Newman metric is the unique vacuum solution of the General Relativistic field equations, in which any singularities or spacetime pathologies are hidden behind horizons. They are believed to describe the spacetimes of massive astrophysical objects with no surfaces, which we call black holes. This spacetime, which is defined entirely by the mass, spin, and charge of the black hole, gives rise to a variety of phenomena in the motion of particles and photons outside the horizons that have no Newtonian counterparts. Moreover, the Kerr-Newman spacetime remains remarkably resilient to many attempts in modifying the underlying theory of gravity. The monitoring of stellar orbits around supermassive black holes, the detection of gravitational waves from the coalescence of stellar-mass black holes, and the observation of black-hole shadows in images with horizon-scale resolution, all of which have become possible during the last decade, are offering valuable tools in testing quantitatively the predictions of this remarkable solution to Einstein's equations., Comment: This chapter is the pre-print of the version currently in production. Please cite this chapter as the following: D. Psaltis. "Black holes in classical general relativity and beyond" in The Encyclopedia of Cosmology (Set 2): Black Holes, edited by Z. Haiman (World Scientific, New Jersey, 2023)

We present a new reconstruction of the Event Horizon Telescope (EHT) image of the M87 black hole from the 2017 data set. We use PRIMO, a novel dictionary-learning based algorithm that uses high-fidelity simulations of accreting black holes as a training set. By learning the correlations between the different regions of the space of interferometric data, this approach allows us to recover high-fidelity images even in the presence of sparse coverage and reach the nominal resolution of the EHT array. The black hole image comprises a thin bright ring with a diameter of $41.5\pm0.6\,\mu$as and a fractional width that is at least a factor of two smaller than previously reported. This improvement has important implications for measuring the mass of the central black hole in M87 based on the EHT images., Comment: 7 pages, 5 figures

Interpretation of resolved polarized images of black holes by the Event Horizon Telescope (EHT) requires predictions of the polarized emission observable by an Earth-based instrument for a particular model of the black hole accretion system. Such predictions are generated by general relativistic radiative transfer (GRRT) codes, which integrate the equations of polarized radiative transfer in curved spacetime. A selection of ray-tracing GRRT codes used within the EHT collaboration is evaluated for accuracy and consistency in producing a selection of test images, demonstrating that the various methods and implementations of radiative transfer calculations are highly consistent. When imaging an analytic accretion model, we find that all codes produce images similar within a pixel-wise normalized mean squared error (NMSE) of 0.012 in the worst case. When imaging a snapshot from a cell-based magnetohydrodynamic simulation, we find all test images to be similar within NMSEs of 0.02, 0.04, 0.04, and 0.12 in Stokes I, Q, U , and V respectively. We additionally find the values of several image metrics relevant to published EHT results to be in agreement to much better precision than measurement uncertainties., Comment: Accepted for publication in ApJ

We have measured the cross section of the $^{83}$Rb(p,$\gamma)^{84}$Sr radiative capture reaction in inverse kinematics using a radioactive beam of $^{83}$Rb at incident energies of 2.4 and $2.7 A$ MeV. Prior to the radioactive beam measurement, the $^{84}$Kr(p,$\gamma)^{85}$Rb radiative capture reaction was measured in inverse kinematics using a stable beam of $^{84}$Kr at an incident energy of $2.7 A$ MeV. The effective relative kinetic energies of these measurements lie within the relevant energy window for the $\gamma$ process in supernovae. The central values of the measured partial cross sections of both reactions were found to be $0.17-0.42$ times the predictions of statistical model calculations. Assuming the predicted cross section at other energies is reduced by the same factor leads to a slightly higher calculated abundance of the $p$ nucleus $^{84}$Sr, caused by the reduced rate of the $^{84}$Sr($\gamma$,p)$^{83}$Rb reaction derived from the present measurement., Comment: 11 pages, 11 figures. arXiv admin note: text overlap with arXiv:2109.06775