Combining timing characteristics with physical broad-band spectral modelling of black hole X-ray binary GX 339–4

GX 339–4 is a black hole X-ray binary that is a key focus of accretion studies, since it goes into outburst roughly every 2–3 yr. Tracking of its radio, infrared (IR), and X-ray flux during multiple outbursts reveals tight broad-band correlations. The radio emission originates in a compact, self-absorbed jet; however, the origin of the X-ray emission is still debated: jet base or corona? We fit 20 quasi-simultaneous radio, IR, optical, and X-ray observations of GX 339–4 covering three separate outbursts in 2005, 2007, 2010–2011, with a composite corona+jet model, where inverse Compton emission from both regions contributes to the X-ray emission. Using a recently proposed identifier of the X-ray variability properties known as power-spectral hue, we attempt to explain both the spectral and evolving timing characteristics, with the model. We find the X-ray spectra are best fit by inverse Compton scattering in a dominant hot corona (kT_e ∼ hundreds of keV). However, radio and IR-optical constraints imply a non-negligible contribution from inverse Compton scattering off hotter electrons (kT_e ≥ 511 keV) in the base of the jets, ranging from a few up to ∼50 per cent of the integrated 3–100 keV flux. We also find that the physical properties of the jet show interesting correlations with the shape of the broad-band X-ray variability of the source, posing intriguing suggestions for the connection between the jet and corona

Combining timing characteristics with physical broad-band spectral modelling of black hole X-ray binary GX 339-4

GX 339-4 is a black hole X-ray binary that is a key focus of accretion studies, since it goes into outburst roughly every 2-3 yr. Tracking of its radio, infrared (IR), and X-ray flux during multiple outbursts reveals tight broad-band correlations. The radio emission originates in a compact, self-absorbed jet; however, the origin of the X-ray emission is still debated: jet base or corona? We fit 20 quasi-simultaneous radio, IR, optical, and X-ray observations of GX 339-4 covering three separate outbursts in 2005, 2007, 2010-2011, with a composite corona+jet model, where inverse Compton emission from both regions contributes to the X-ray emission. Using a recently proposed identifier of the X-ray variability properties known as power-spectral hue, we attempt to explain both the spectral and evolving timing characteristics, with the model. We find the X-ray spectra are best fit by inverse Compton scattering in a dominant hot corona (kT(e) similar to hundreds of keV). However, radio and IR-optical constraints imply a non-negligible contribution from inverse Compton scattering off hotter electrons (kT(e) >= 511 keV) in the base of the jets, ranging from a few up to similar to 50 per cent of the integrated 3-100 keV flux. We also find that the physical properties of the jet show interesting correlations with the shape of the broad-band X-ray variability of the source, posing intriguing suggestions for the connection between the jet and corona

H-AMR: A New GPU-accelerated GRMHD Code for Exascale Computing With 3D Adaptive Mesh Refinement and Local Adaptive Time-stepping

General-relativistic magnetohydrodynamic (GRMHD) simulations have revolutionized our understanding of black-hole accretion. Here, we present a GPU-accelerated GRMHD code H-AMR with multi-faceted optimizations that, collectively, accelerate computation by 2-5 orders of magnitude for a wide range of applications. Firstly, it involves a novel implementation of a spherical-polar grid with 3D adaptive mesh refinement that operates in each of the 3 dimensions independently. This allows us to circumvent the Courant condition near the polar singularity, which otherwise cripples high-res computational performance. Secondly, we demonstrate that local adaptive time-stepping (LAT) on a logarithmic spherical-polar grid accelerates computation by a factor of $\lesssim10$ compared to traditional hierarchical time-stepping approaches. Jointly, these unique features lead to an effective speed of $\sim10^9$ zone-cycles-per-second-per-node on 5,400 NVIDIA V100 GPUs (i.e., 900 nodes of the OLCF Summit supercomputer). We demonstrate its computational performance by presenting the first GRMHD simulation of a tilted thin accretion disk threaded by a toroidal magnetic field around a rapidly spinning black hole. With an effective resolution of $13$,$440\times4$,$608\times8$,$092$ cells, and a total of $\lesssim22$ billion cells and $\sim0.65\times10^8$ timesteps, it is among the largest astrophysical simulations ever performed. We find that frame-dragging by the black hole tears up the disk into two independently precessing sub-disks. The innermost sub-disk rotation axis intermittently aligns with the black hole spin, demonstrating for the first time that such long-sought alignment is possible in the absence of large-scale poloidal magnetic fields.Comment: 10 pages, 5 figures, submitted to MNRAS, for the YouTube playlist, see https://youtu.be/rIOjKUfzcv

Combining timing characteristics with physical broad-band spectral modelling of black hole X-ray binary GX 339–4

GX 339–4 is a black hole X-ray binary that is a key focus of accretion studies, since it goes into outburst roughly every 2–3 yr. Tracking of its radio, infrared (IR), and X-ray flux during multiple outbursts reveals tight broad-band correlations. The radio emission originates in a compact, self-absorbed jet; however, the origin of the X-ray emission is still debated: jet base or corona? We fit 20 quasi-simultaneous radio, IR, optical, and X-ray observations of GX 339–4 covering three separate outbursts in 2005, 2007, 2010–2011, with a composite corona+jet model, where inverse Compton emission from both regions contributes to the X-ray emission. Using a recently proposed identifier of the X-ray variability properties known as power-spectral hue, we attempt to explain both the spectral and evolving timing characteristics, with the model. We find the X-ray spectra are best fit by inverse Compton scattering in a dominant hot corona (kT_e ∼ hundreds of keV). However, radio and IR-optical constraints imply a non-negligible contribution from inverse Compton scattering off hotter electrons (kT_e ≥ 511 keV) in the base of the jets, ranging from a few up to ∼50 per cent of the integrated 3–100 keV flux. We also find that the physical properties of the jet show interesting correlations with the shape of the broad-band X-ray variability of the source, posing intriguing suggestions for the connection between the jet and corona

Deep-learning-based joint rigid and deformable contour propagation for magnetic resonance imaging-guided prostate radiotherapy

Background: Deep learning-based unsupervised image registration has recently been proposed, promising fast registration. However, it has yet to be adopted in the online adaptive magnetic resonance imaging-guided radiotherapy (MRgRT) workflow. Purpose: In this paper, we design an unsupervised, joint rigid, and deformable registration framework for contour propagation in MRgRT of prostate cancer. Methods: Three-dimensional pelvic T2-weighted MRIs of 143 prostate cancer patients undergoing radiotherapy were collected and divided into 110, 13, and 20 patients for training, validation, and testing. We designed a framework using convolutional neural networks (CNNs) for rigid and deformable registration. We selected the deformable registration network architecture among U-Net, MS-D Net, and LapIRN and optimized the training strategy (end-to-end vs. sequential). The framework was compared against an iterative baseline registration. We evaluated registration accuracy (the Dice and Hausdorff distance of the prostate and bladder contours), structural similarity index, and folding percentage to compare the methods. We also evaluated the framework's robustness to rigid and elastic deformations and bias field perturbations. Results: The end-to-end trained framework comprising LapIRN for the deformable component achieved the best median (interquartile range) prostate and bladder Dice of 0.89 (0.85–0.91) and 0.86 (0.80–0.91), respectively. This accuracy was comparable to the iterative baseline registration: prostate and bladder Dice of 0.91 (0.88–0.93) and 0.86 (0.80–0.92). The best models complete rigid and deformable registration in 0.002 (0.0005) and 0.74 (0.43) s (Nvidia Tesla V100-PCIe 32 GB GPU), respectively. We found that the models are robust to translations up to 52 mm, rotations up to 15 (Formula presented.), elastic deformations up to 40 mm, and bias fields. Conclusions: Our proposed unsupervised, deep learning-based registration framework can perform rigid and deformable registration in less than a second with contour propagation accuracy comparable with iterative registration

Rethinking Serious Games Design in the Age of COVID-19: Setting the Focus on Wicked Problems

We live in a complex world, in which our existence is defined by forces that we cannot fully comprehend, predict, nor control. This is the world of wicked problems, of which the situation triggered by the COVID-19 pandemic is a notable example. Wicked problems are complex scenarios defined by the interplay of multiple environmental, social and economic factors. They are everchanging, and largely unpredictable and uncontrollable. As a consequence, wicked problems cannot be definitively solved through traditional problem-solving approaches. Instead, they should be iteratively managed, recognizing and valuing our connectedness with each other and the environment, and engaging in joint thinking and action to identify and pursue the common good. Serious games can be key to foster wicked problem management abilities. To this end, they should engage players in collective activities set in contexts simulating real-world wicked problem scenarios. These should require the continuous interpretation of changing circumstances to identify and pursue shared goals, promoting the development of knowledge, attitudes and skill sets relevant to tackle real-world situations. In this paper we outline the nature, implications and challenges of wicked problems, highlighting why games should be leveraged to foster wicked problem management abilities. Then, we propose a theory-based framework to support the design of games for this purpose

Quantitative comparison of deep learning-based image reconstruction methods for low-dose and sparse-angle CT applications

The reconstruction of computed tomography (CT) images is an active area of research. Following the rise of deep learning methods, many data-driven models have been proposed in recent years. In this work, we present the results of a data challenge that we organized, bringing together algorithm experts from different institutes to jointly work on quantitative evaluation of several data-driven methods on two large, public datasets during a ten day sprint. We focus on two applications of CT, namely, low-dose CT and sparse-angle CT. This enables us to fairly compare different methods using standardized settings. As a general result, we observe that the deep learning-based methods are able to improve the reconstruction quality metrics in both CT applications while the top performing methods show only minor differences in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). We further discuss a number of other important criteria that should be taken into account when selecting a method, such as the availability of training data, the knowledge of the physical measurement model and the reconstruction speed