Search

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

Globular clusters contain multiple stellar populations, with some previous generation of stars polluting the current stars with heavier elements. Understanding the history of globular clusters is helpful in understanding how galaxies merged and evolved and therefore constraining the site or sites of this historic pollution is a priority. The acceptable temperature and density conditions of these polluting sites depend on critical reaction rates. In this paper, three experimental studies helping to constrain astrophysically important reaction rates are briefly discussed., Comment: Submission to conference proceedings of INPC2022 in Cape Town

The $^{22}$Mg($\alpha$,$p$)$^{25}$Al reaction rate has been identified as a major source of uncertainty for understanding the nucleosynthesis flow in Type-I x-ray bursts (XRBs). We report a direct measurement of the energy- and angle-integrated cross sections of this reaction in a 3.3-6.9 MeV center-of-mass energy range using the MUlti-Sampling Ionization Chamber (MUSIC). The new $^{22}$Mg($\alpha$,$p$)$^{25}$Al reaction rate is a factor of $\sim$4 higher than the previous direct measurement of this reaction within temperatures relevant for XRBs, resulting in the $^{22}$Mg waiting point of x-ray burst nucleosynthesis flow to be significantly bypassed via the ($\alpha,p$) reaction, Comment: 6 pages, 3 figures, 1 table

A possible mechanism to explain the origin of the light $p$-nuclei in the Galaxy is the nucleosynthesis in the proton-rich neutrino-driven wind ejecta of core-collapse supernovae via the $\nu p$-process. However this production scenario is very sensitive to the underlying supernova dynamics and the nuclear physics input. As far as the nuclear uncertainties are concerned, the breakout from the $pp$-chains via the $^7$Be($\alpha,\gamma$)$^{11}$C reaction has been identified as an important link which can influence the nuclear flow and therefore the efficiency of the $\nu p$-process. However its reaction rate is poorly known over the relevant temperature range, T = 1.5-3 GK. We report on the first direct measurement of two resonances of the $^7$Be($\alpha,\gamma$)$^{11}$C reaction with previously unknown strengths using an intense radioactive $^7$Be beam from the ISAC facility and the DRAGON recoil separator in inverse kinematics. We have decreased the $^7$Be($\alpha,\gamma$)$^{11}$C reaction rate uncertainty to $\sim$ 9.4-10.7% over the relevant temperature region., Comment: 15 pages, 12 figures. Accepted for publication in Phys. Rev. C

We have performed the first direct measurement of two resonances of the $^7$Be($\alpha,\gamma$)$^{11}$C reaction with unknown strengths using an intense radioactive $^7$Be beam and the DRAGON recoil separator. We report on the first measurement of the 1155 and 1110 keV resonance strengths of $1.73 \pm 0.25(stat.) \pm 0.40(syst.)$ eV and $125 ^{+27}_{-25}(stat.) \pm 15(syst.)$ meV, respectively. The present results have reduced the uncertainty in the $^7$Be($\alpha,\gamma$)$^{11}$C reaction rate to $\sim$ 9.4-10.7% over T = 1.5-3 GK, which is relevant for nucleosynthesis in the neutrino-driven outflows of core-collapse supernovae ($\nu p$-process). We find no effect of the new, constrained reaction rate on $\nu p$-process nucleosynthesis., Comment: 5 pages, 3 figures. Accepted for publication in Phys. Rev. Lett

Modern machine learning models use an ever-increasing number of parameters to train (175 billion parameters for GPT-3) with large datasets to obtain better performance. Bigger is better has been the norm. Optical computing has been reawakened as a potential solution to large-scale computing through optical accelerators that carry out linear operations while reducing electrical power. However, to achieve efficient computing with light, creating and controlling nonlinearity optically rather than electronically remains a challenge. This study explores a reservoir computing (RC) approach whereby a 14 mm long few-mode waveguide in LiNbO3 on insulator is used as a complex nonlinear optical processor. A dataset is encoded digitally on the spectrum of a femtosecond pulse which is then launched in the waveguide. The output spectrum depends nonlinearly on the input. We experimentally show that a simple digital linear classifier with 784 parameters using the output spectrum from the waveguide as input increased the classification accuracy of several databases compared to non-transformed data, approximately 10$\%$. In comparison, a deep digital neural network (NN) with 40000 parameters was necessary to achieve the same accuracy. Reducing the number of parameters by a factor of $\sim$50 illustrates that a compact optical RC approach can perform on par with a deep digital NN., Comment: 13 pages, 3 figures and 1 table

Deep neural networks trained on physical losses are emerging as promising surrogates of nonlinear numerical solvers. These tools can predict solutions of Maxwell's equations and compute gradients of output fields with respect to the material and geometrical properties in millisecond times which makes them attractive for inverse design or inverse scattering applications. Here we develop a tunable version of MaxwellNet, a physics driven neural network able to compute light scattering from inhomogenous media with a size comparable with the incident wavelength in the presence of the optical Kerr effect. The weights of the network are dynamically adjusted to take into account the intensity-dependent refractive index of the material.

The ever-increasing demand for processing data with larger machine learning models requires more efficient hardware solutions due to limitations such as power dissipation and scalability. Optics is a promising contender for providing lower power computation since light propagation through a non-absorbing medium is a lossless operation. However, to carry out useful and efficient computations with light, generating and controlling nonlinearity optically is a necessity that is still elusive. Multimode fibers (MMF) have been shown that they can provide nonlinear effects with microwatts of average power while maintaining parallelism and low loss. In this work, we propose an optical neural network architecture, which performs nonlinear optical computation by controlling the propagation of ultrashort pulses in MMF by wavefront shaping. With a surrogate model, optimal sets of parameters are found to program this optical computer for different tasks with minimal utilization of an electronic computer. We show a remarkable decrease of 97% in the number of model parameters, which leads to an overall 99% digital operation reduction compared to an equivalently performing digital neural network. We further demonstrate that a fully optical implementation can also be performed with competitive accuracies., Comment: 32 pages, 11 figures

The sparse interferometric coverage of the Event Horizon Telescope (EHT) poses a significant challenge for both reconstruction and model fitting of black-hole images. PRIMO is a new principal components analysis-based algorithm for image reconstruction that uses the results of high-fidelity general relativistic, magnetohydrodynamic simulations of low-luminosity accretion flows as a training set. This allows the reconstruction of images that are both consistent with the interferometric data and that live in the space of images that is spanned by the simulations. PRIMO follows Monte Carlo Markov Chains to fit a linear combination of principal components derived from an ensemble of simulated images to interferometric data. We show that PRIMO can efficiently and accurately reconstruct synthetic EHT data sets for several simulated images, even when the simulation parameters are significantly different from those of the image ensemble that was used to generate the principal components. The resulting reconstructions achieve resolution that is consistent with the performance of the array and do not introduce significant biases in image features such as the diameter of the ring of emission., Comment: Accepted to ApJ on Dec. 12, 2022, 23 pages, 19 figures, replaced with accepted version

Observations of the S-stars, the cluster of young stars in the inner 0.1 pc of the Galactic Center, have been crucial in providing conclusive evidence for a supermassive black hole at the center of our galaxy. Since some of the stars have orbits less than that of a typical human lifetime, it is possible to observe multiple orbits and test the weak-field regime of general relativity. Current calculations of S-star orbits require slow and expensive computations in order to numerically solve geodesic equations for many small time steps. In this paper, we present a computationally efficient, first-order post-Newtonian model for the astrometric and spectroscopic data gathered for the S-stars. We find that future, 30-m class telescopes -- and potentially even current large telescopes with very high spectroscopic resolution -- may be able to detect the Shapiro effect for an S-star in the next decade or so., Comment: 20 pages, 8 figures, 3 tables. v2: edited to match published ApJ version

We propose a physics-informed neural network as the forward model for tomographic reconstructions of biological samples. We demonstrate that by training this network with the Helmholtz equation as a physical loss, we can predict the scattered field accurately. It will be shown that a pretrained network can be fine-tuned for different samples and used for solving the scattering problem much faster than other numerical solutions. We evaluate our methodology with numerical and experimental results. Our physics-informed neural networks can be generalized for any forward and inverse scattering problem.

The Event Horizon Telescope (EHT) observed the compact radio source, Sagittarius A* (Sgr A*), in the Galactic Center on 2017 April 5-11 in the 1.3 millimeter wavelength band. At the same time, interferometric array data from the Atacama Large Millimeter/submillimeter Array and the Submillimeter Array were collected, providing Sgr A* light curves simultaneous with the EHT observations. These data sets, complementing the EHT very-long-baseline interferometry, are characterized by a cadence and signal-to-noise ratio previously unattainable for Sgr A* at millimeter wavelengths, and they allow for the investigation of source variability on timescales as short as a minute. While most of the light curves correspond to a low variability state of Sgr A*, the April 11 observations follow an X-ray flare, and exhibit strongly enhanced variability. All of the light curves are consistent with a red noise process, with a power spectral density (PSD) slope measured to be between -2 and -3 on timescales between 1 min and several hours. Our results indicate a steepening of the PSD slope for timescales shorter than 0.3 h. The spectral energy distribution is flat at 220 GHz and there are no time-lags between the 213 and 229 GHz frequency bands, suggesting low optical depth for the event horizon scale source. We characterize Sgr A*'s variability, highlighting the different behavior observed just after the X-ray flare, and use Gaussian process modeling to extract a decorrelation timescale and a PSD slope. We also investigate the systematic calibration uncertainties by analyzing data from independent data reduction pipelines.

Phase-contrast tomographic flow cytometry combines quantitative 3D analysis of unstained single cells and high-throughput. A crucial issue of this method is the storage and management of the huge amount of 3D tomographic data. Here we show an effective quasi lossless compression of tomograms data through 3D Zernike descriptors, unlocking data management tasks and computational pipelines that were unattainable until now.

Aims. The Chemical Evolution of R-process Elements in Stars (CERES) project aims to provide a homogeneous analysis of a sample of metal-poor stars ([Fe/H]<-1.5). We present the stellar parameters and the chemical abundances of elements up to Zr for a sample of 52 giant stars.Methods. We relied on a sample of high signal-to-noise UVES spectra. We determined stellar parameters from Gaia photometry and parallaxes. Chemical abundances were derived using spectrum synthesis and model atmospheres.Results. We determined chemical abundances of 26 species of 18 elements: Na, Mg, Al, Si, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Sr, Y, and Zr. For several stars, we were able to measure both neutral and ionised species, including Si, Sc, Mn, and Zr. We have roughly doubled the number of measurements of Cu for stars at [Fe/H] <= -2.5. The homogeneity of the sample made it possible to highlight the presence of two Zn-rich stars ([Zn/Fe]~+0.7), one r-rich and the other r-poor. We report the existence of two branches in the [Zn/Fe] versus [Ni/Fe] plane and suggest that the high [Zn/Fe] branch is the result of hypernova nucleosynthesis. We discovered two stars with peculiar light neutron-capture abundance patterns: CES1237+1922 (also known as BS 16085-0050), which is ~1 dex underabundant in Sr, Y, and Zr with respect to the other stars in the sample, and CES2250-4057 (also known as HE 2247-4113), which shows a ~1 dex overabundance of Sr with respect to Y and Zr.Conclusions. The high quality of our dataset allowed us to measure hardly detectable ions. This can provide guidance in the development of line formation computations that take deviations from local thermodynamic equilibrium and hydrodynamical effects into account.

Optical diffraction tomography (ODT) is an emerging 3D imaging technique that is used for the 3D reconstruction of the refractive index (RI) for semi-transparent samples. Various inverse models have been proposed to reconstruct the 3D RI based on the holographic detection of different samples such as the Born and the Rytov approximations. However, such approximations usually suffer from the so-called missing-cone problem that results in an elongation of the final reconstruction along the optical axis. Different iterative schemes have been proposed to solve the missing cone problem relying on physical forward models and an error function that aims at filling in the k-space and thus eliminating the missing-cone problem and reaching better reconstruction accuracy. In this paper, we propose a different approach where a 3D neural network (NN) is employed. The NN is trained with a cost function derived from a physical model based on the physics of optical wave propagation. The 3D NN starts with an initial guess for the 3D RI reconstruction (i.e. Born, or Rytov) and aims at reconstructing better 3D reconstruction based on an error function. With this technique, the NN can be trained without any examples of the relation between the ill-posed reconstruction (Born or Rytov) and the ground truth (true shape).

Recent advancements in observational techniques have led to new tests of the general relativistic predictions for black-hole spacetimes in the strong-field regime. One of the key ingredients for several tests is a metric that allows for deviations from the Kerr solution but remains free of pathologies outside its event horizon. Existing metrics that have been used in the literature often do not satisfy the null convergence condition that is necessary to apply the strong rigidity theorem and would have allowed us to calculate the location of the event horizon by identifying it with an appropriate Killing horizon. This has led earlier calculations of event horizons of parametrically deformed metrics to either follow numerical techniques or simply search heuristically for coordinate singularities. We show that several of these metrics, almost by construction, are circular. We can, therefore, use the weak rigidity and Carter's rotosurface theorem and calculate algebraically the locations of their event horizons, without relying on expansions or numerical techniques. We apply this approach to a number of parametrically deformed metrics, calculate the locations of their event horizons, and place constraints on the deviation parameters such that the metrics remain regular outside their horizons. We show that calculating the angular velocity of the horizon and the effective gravity there offers new insights into the observational signatures of deformed metrics, such as the sizes and shapes of the predicted black-hole shadows.

Nuclear Astrophysics is a field at the intersection of nuclear physics and astrophysics, which seeks to understand the nuclear engines of astronomical objects and the origin of the chemical elements. This white paper summarizes progress and status of the field, the new open questions that have emerged, and the tremendous scientific opportunities that have opened up with major advances in capabilities across an ever growing number of disciplines and subfields that need to be integrated. We take a holistic view of the field discussing the unique challenges and opportunities in nuclear astrophysics in regards to science, diversity, education, and the interdisciplinarity and breadth of the field. Clearly nuclear astrophysics is a dynamic field with a bright future that is entering a new era of discovery opportunities., Comment: 96 pages. Submitted to Journal of Physics G

A promising astrophysical site to produce the lighter heavy elements of the first $r$-process peak ($Z = 38-47$) is the moderately neutron rich ($0.4 < Y_e < 0.5$) neutrino-driven ejecta of explosive environments, such as core-collapse supernovae and neutron star mergers, where the weak $r$-process operates. This nucleosynthesis exhibits uncertainties from the absence of experimental data from $(\alpha,xn)$ reactions on neutron-rich nuclei, which are currently based on statistical model estimates. In this work, we report on a new study of the nuclear reaction impact using a Monte Carlo approach and improved $(\alpha,xn)$ rates based on the Atomki-V2 $\alpha$ Optical Model Potential ($\alpha$OMP). We compare our results with observations from an up-to-date list of metal-poor stars with [Fe/H] $<$ -1.5 to find conditions of the neutrino-driven wind where the lighter heavy elements can be synthesized. We identified a list of $(\alpha,xn)$ reaction rates that affect key elemental ratios in different astrophysical conditions. Our study aims on motivating more nuclear physics experiments on $(\alpha, xn)$ reactions using current and the new generation of radioactive beam facilities and also more observational studies of metal-poor stars., Comment: 16 pages, 8 figures, submitted to ApJ

The $^{30}$P$(p,\gamma)^{31}$S reaction plays an important role in understanding nucleosynthesis of $A\geq 30$ nuclides in oxygen-neon novae. The Gaseous Detector with Germanium Tagging was used to measure $^{31}$Cl $\beta$-delayed proton decay through the key $J^{\pi}=3/2^{+}$, 260-keV resonance. The intensity $I^{260}_{\beta p} = 8.3^{+1.2}_{-0.9} \times 10^{-6}$ represents the weakest $\beta$-delayed, charged-particle emission ever measured below 400 keV, resulting in a proton branching ratio of $\Gamma_p / \Gamma = 2.5^{+0.4}_{-0.3} \times 10^{-4}$. By combining this measurement with shell-model calculations for $\Gamma_{\gamma}$ and past work on other resonances, the total $^{30}$P$(p,\gamma)^{31}$S rate has been determined with reduced uncertainty. The new rate has been used in hydrodynamic simulations to model the composition of nova ejecta, leading to a concrete prediction of $^{30}$Si/$^{28}$Si excesses in presolar nova grains and the calibration of nuclear thermometers., Comment: 7 pages, 2 figures, accepted to Physical Review Letters on April 4, 2022

We present MeqSilhouette v2.0 (MeqSv2), a fully polarimetric, time-and frequency-resolved synthetic data generation software for simulating millimetre (mm) wavelength very long baseline interferometry (VLBI) observations with heterogeneous arrays. Synthetic data are a critical component in understanding real observations, testing calibration and imaging algorithms, and predicting performance metrics of existing or proposed sites. MeqSv2 applies physics-based instrumental and atmospheric signal corruptions constrained by empirically-derived site and station parameters to the data. The new version is capable of applying instrumental polarization effects and various other spectrally-resolved effects using the Radio Interferometry Measurement Equation (RIME) formalism and produces synthetic data compatible with calibration pipelines designed to process real data. We demonstrate the various corruption capabilities of MeqSv2 using different arrays, with a focus on the effect of complex bandpass gains on closure quantities for the EHT at 230 GHz. We validate the frequency-dependent polarization leakage implementation by performing polarization self-calibration of synthetic EHT data using PolSolve. We also note the potential applications for cm-wavelength VLBI array analysis and design and future directions., Comment: accepted for publication in MNRAS

Features such as photon rings, jets, or hot. spots can leave particular topological signatures in a black hole image. As such, topological data analysis can be used to characterize images resulting from high resolution observations (synthetic or real) of black holes in the electromagnetic sector. We demonstrate that persistent homology allows for this characterization to be made automatically by counting the number of connected components and one-dimensional holes. Further, persistent homology also allows for the distance between connected components or diameter of holes to be extracted from the image. In order to apply persistent homology on synthetic black hole images, we also introduce metronization, a new algorithm to prepare black hole images into a form that is suitable for topological analysis., Comment: PRD accepted -- matches accepted version

Monte Carlo simulations are widely used in nuclear physics to model experimental systems. In cases where there are significant unknown quantities, such as energies of states, an iterative process of simulating and fitting is often required to describe experimental data. We describe a Bayesian approach to fitting experimental data, designed for data from a $^{12}$Be(d,p) reaction measurement, using simulations made with GEANT4. Q-values from the $^{12}$C(d,p) reaction to well-known states in $^{13}$C are compared with simulations using BayesOpt. The energies of the states were not included in the simulation to reproduce the situation for $^{13}$Be where the states are poorly known. Both cases had low statistics and significant resolution broadening owing to large proton energy losses in the solid deuterium target. Excitation energies of the lowest three excited states in $^{13}$C were extracted to better than 90 keV, paving a way for extracting information on $^{13}$Be., Comment: 8 pages, 6 figures

The last decade has seen the development of a wide set of tools, such as wavefront shaping, computational or fundamental methods, that allow to understand and control light propagation in a complex medium, such as biological tissues or multimode fibers. A vibrant and diverse community is now working on this field, that has revolutionized the prospect of diffraction-limited imaging at depth in tissues. This roadmap highlights several key aspects of this fast developing field, and some of the challenges and opportunities ahead., Comment: submitted to J.Phys Photonics (IOP), 116 pages, 23 sections

Very-long-baseline interferometry (VLBI) observations of active galactic nuclei at millimeter wavelengths have the power to reveal the launching and initial collimation region of extragalactic radio jets, down to $10-100$ gravitational radii ($r_g=GM/c^2$) scales in nearby sources. Centaurus A is the closest radio-loud source to Earth. It bridges the gap in mass and accretion rate between the supermassive black holes (SMBHs) in Messier 87 and our galactic center. A large southern declination of $-43^{\circ}$ has however prevented VLBI imaging of Centaurus A below ${\lambda}1$cm thus far. Here, we show the millimeter VLBI image of the source, which we obtained with the Event Horizon Telescope at $228$GHz. Compared to previous observations, we image Centaurus A's jet at a tenfold higher frequency and sixteen times sharper resolution and thereby probe sub-lightday structures. We reveal a highly-collimated, asymmetrically edge-brightened jet as well as the fainter counterjet. We find that Centaurus A's source structure resembles the jet in Messier 87 on ${\sim}500r_g$ scales remarkably well. Furthermore, we identify the location of Centaurus A's SMBH with respect to its resolved jet core at ${\lambda}1.3$mm and conclude that the source's event horizon shadow should be visible at THz frequencies. This location further supports the universal scale invariance of black holes over a wide range of masses., Comment: 27 pages, 9 figures. This is a post-peer-review, pre-copyedit version of an article published in Nature Astronomy. The final authenticated version is available online at: https://doi.org/10.1038/s41550-021-01417-w

The images of supermassive black holes surrounded by optically-thin, radiatively-inefficient accretion flows, like those observed with the Event Horizon Telescope, are characterized by a bright ring of emission surrounding the black-hole shadow. In the Kerr spacetime this bright ring, when narrow, closely traces the boundary of the shadow and can, with appropriate calibration, serve as its proxy. The present paper expands the validity of this statement by considering two particular spacetime geometries: a solution to the field equations of a modified gravity theory and another that parametrically deviates from Kerr but recovers the Kerr spacetime when its deviation parameters vanish. A covariant, axisymmetric analytic model of the accretion flow based on conservation laws and spanning a broad range of plasma conditions is utilized to calculate synthetic non-Kerr black-hole images, which are then analysed and characterized. We find that in all spacetimes: (i) it is the gravitationally-lensed unstable photon orbit that plays the critical role in establishing the diameter of the rings observed in black-hole images, not the event horizon or the innermost stable circular orbit, (ii) bright rings in these images scale in size with, and encompass, the boundaries of the black-hole shadows, even when deviating significantly from Kerr, and (iii) uncertainties in the physical properties of the accreting plasma introduce subdominant corrections to the relation between the diameter of the image and the diameter of the black-hole shadow. These results provide important new theoretical justification for using black-hole images to probe and test the spacetimes of supermassive black holes., Comment: 30 pages, 18 figures, 2 tables. Accepted for publication in ApJ

The black-hole images obtained with the Event Horizon Telescope (EHT) are expected to be variable at the dynamical timescale near their horizons. For the black hole at the center of the M87 galaxy, this timescale (5-61 days) is comparable to the 6-day extent of the 2017 EHT observations. Closure phases along baseline triangles are robust interferometric observables that are sensitive to the expected structural changes of the images but are free of station-based atmospheric and instrumental errors. We explored the day-to-day variability in closure phase measurements on all six linearly independent non-trivial baseline triangles that can be formed from the 2017 observations. We showed that three triangles exhibit very low day-to-day variability, with a dispersion of $\sim3-5^\circ$. The only triangles that exhibit substantially higher variability ($\sim90-180^\circ$) are the ones with baselines that cross visibility amplitude minima on the $u-v$ plane, as expected from theoretical modeling. We used two sets of General Relativistic magnetohydrodynamic simulations to explore the dependence of the predicted variability on various black-hole and accretion-flow parameters. We found that changing the magnetic field configuration, electron temperature model, or black-hole spin has a marginal effect on the model consistency with the observed level of variability. On the other hand, the most discriminating image characteristic of models is the fractional width of the bright ring of emission. Models that best reproduce the observed small level of variability are characterized by thin ring-like images with structures dominated by gravitational lensing effects and thus least affected by turbulence in the accreting plasmas., Comment: Accepted for Publication in ApJ

The horizon-scale images of black holes obtained with the Event Horizon Telescope have provided new probes of their metrics and tests of General Relativity. The images are characterized by a bright, near circular ring from the gravitationally lensed emission from the hot plasma and a deep central depression cast by the black hole. The metric tests rely on fact that the bright ring closely traces the boundary of the black hole shadow with a small displacement that has been quantified using simulations. In this paper we develop a self-consistent covariant analytic model of the accretion flow that spans a broad range of plasma conditions and black-hole properties to explore the general validity of this result. We show that, for any physical model of the accretion flow, the ring always encompasses the outline of the shadow and is not displaced by it by more than half the ring width. This result is a consequence of conservation laws and basic thermodynamic considerations and does not depend on the microphysics of the plasma or the details of the numerical simulations. We also present a quantitative measurement of the bias between the bright ring and the shadow radius based on the analytical models., Comment: To appear in ApJ

To characterize a physical system to behave as desired, either its underlying governing rules must be known a priori or the system itself be accurately measured. The complexity of full measurements of the system scales with its size. When exposed to real-world conditions, such as perturbations or time-varying settings, the system calibrated for a fixed working condition might require non-trivial re-calibration, a process that could be prohibitively expensive, inefficient and impractical for real-world use cases. In this work, we propose a learning procedure to obtain a desired target output from a physical system. We use Variational Auto-Encoders (VAE) to provide a generative model of the system function and use this model to obtain the required input of the system that produces the target output. We showcase the applicability of our method for two datasets in optical physics and neuroscience.

A measurement of proton inelastic scattering of $^8$He at $8.25A$~MeV at TRIUMF shows a resonance at 3.54(6)~MeV with a width of 0.89(11)~MeV. The energy of the state is in good agreement with coupled cluster and no-core shell model with continuum calculations, with the latter successfully describing the measured resonance width as well. Its differential cross section analyzed with phenomenological collective excitation form factor and microscopic coupled reaction channels framework consistently reveals a large deformation parameter $\beta_2$ = 0.40(3), consistent with no-core shell model predictions of a large neutron deformation. This deformed double-closed shell at the neutron drip-line opens a new paradigm., Comment: 9 pages, 5 figures. Accepted for publication in Physics Letters B