The Three-Dimensional Structure of Interior Ejecta in Cassiopeia A at High Spectral Resolution

We used the Spitzer Space Telescope's Infrared Spectrograph to create a high resolution spectral map of the central region of the Cassiopeia A supernova remnant, allowing us to make a Doppler reconstruction of its 3D structure. The ejecta responsible for this emission have not yet encountered the remnant's reverse shock or the circumstellar medium, making it an ideal laboratory for exploring the dynamics of the supernova explosion itself. We observe that the O, Si, and S ejecta can form both sheet-like structures as well as filaments. Si and O, which come from different nucleosynthetic layers of the star, are observed to be coincident in velocity space in some regions, and separated by 500 km/s or more in others. Ejecta traveling toward us are, on average, ~900 km/s slower than the material traveling away from us. We compare our observations to recent supernova explosion models and find that no single model can simultaneously reproduce all the observed features. However, models of different supernova explosions can collectively produce the observed geometries and structures of the interior emission. We use the results from the models to address the conditions during the supernova explosion, concentrating on asymmetries in the shock structure. We also predict that the back surface of Cassiopeia A will begin brightening in ~30 years, and the front surface in ~100 years.Comment: 35 pages, 16 figures, accepted to Ap

The Three-Dimensional Structure of Cassiopeia A

We used the Spitzer Space Telescope's Infrared Spectrograph to map nearly the entire extent of Cassiopeia A between 5-40 micron. Using infrared and Chandra X-ray Doppler velocity measurements, along with the locations of optical ejecta beyond the forward shock, we constructed a 3-D model of the remnant. The structure of Cas A can be characterized into a spherical component, a tilted thick disk, and multiple ejecta jets/pistons and optical fast-moving knots all populating the thick disk plane. The Bright Ring in Cas A identifies the intersection between the thick plane/pistons and a roughly spherical reverse shock. The ejecta pistons indicate a radial velocity gradient in the explosion. Some ejecta pistons are bipolar with oppositely-directed flows about the expansion center while some ejecta pistons show no such symmetry. Some ejecta pistons appear to maintain the integrity of the nuclear burning layers while others appear to have punched through the outer layers. The ejecta pistons indicate a radial velocity gradient in the explosion. In 3-D, the Fe jet in the southeast occupies a "hole" in the Si-group emission and does not represent "overturning", as previously thought. Although interaction with the circumstellar medium affects the detailed appearance of the remnant and may affect the visibility of the southeast Fe jet, the bulk of the symmetries and asymmetries in Cas A are intrinsic to the explosion.Comment: Accepted to ApJ. 54 pages, 21 figures. For high resolution figures and associated mpeg movie and 3D PDF files, see http://homepages.spa.umn.edu/~tdelaney/pape

Nucleosynthetic Layers in the Shocked Ejecta of Cassiopeia A

We present a three-dimensional analysis of the supernova remnant Cassiopeia A using high-resolution spectra from the Spitzer Space Telescope. We observe supernova ejecta both immediately before and during the shock-ejecta interaction. We determine that the reverse shock of the remnant is spherical to within 7%, although the center of this sphere is offset from the geometric center of the remnant by 810 km s^(–1). We determine that the velocity width of the nucleosynthetic layers is ~1000 km s^(–1) over 4000 arcsec^2 regions, although the velocity width of a layer along any individual line of sight is <250 km s^(–1). Si and O, which come from different nucleosynthetic layers in the progenitor star, are observed to be coincident in velocity space in some directions, but segregated by up to ~500 km s^(–1) in other directions. We compare these observations of the nucleosynthetic layers to predictions from supernova explosion models in an attempt to constrain such models. Finally, we observe small-scale, corrugated velocity structures that are likely caused during the supernova explosion itself, rather than hundreds of years later by dynamical instabilities at the remnant's reverse shock

Spitzer Spectral Mapping of Supernova Remnant Cassiopeia A

We present the global distribution of fine structure infrared line emission in the Cassiopeia A supernova remnant using data from the Spitzer Space Telescope's Infrared Spectrograph. We identify emission from ejecta materials in the interior, prior to their encounter with the reverse shock, as well as from the post-shock bright ring. The global electron density increases by >~100 at the shock to ~10^4 cm^-3, providing evidence for strong radiative cooling. There is also a dramatic change in ionization state at the shock, with the fading of emission from low ionization interior species like [SiII], giving way to [SIV] and, at even further distances, high-energy X-rays from hydrogenic silicon. Two compact, crescent-shaped clumps with highly enhanced neon abundance are arranged symmetrically around the central neutron star. These neon crescents are very closely aligned with the "kick" direction of the compact object from the remnant's expansion center, tracing a new axis of explosion asymmetry. They indicate that much of the apparent macroscopic elemental mixing may arise from different compositional layers of ejecta now passing through the reverse shock along different directions.Comment: 9 pages, 8 figures, accepted by Ap

Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI

© Springer Nature Switzerland AG 2020. Accurate segmentation of the cardiac boundaries in late gadolinium enhancement magnetic resonance images (LGE-MRI) is a fundamental step for accurate quantification of scar tissue. However, while there are many solutions for automatic cardiac segmentation of cine images, the presence of scar tissue can make the correct delineation of the myocardium in LGE-MRI challenging even for human experts. As part of the Multi-Sequence Cardiac MR Segmentation Challenge, we propose a solution for LGE-MRI segmentation based on two components. First, a generative adversarial network is trained for the task of modality-to-modality translation between cine and LGE-MRI sequences to obtain extra synthetic images for both modalities. Second, a deep learning model is trained for segmentation with different combinations of original, augmented and synthetic sequences. Our results based on three magnetic resonance sequences (LGE, bSSFP and T2) from 45 different patients show that the multi-sequence model training integrating synthetic images and data augmentation improves in the segmentation over conventional training with real datasets. In conclusion, the accuracy of the segmentation of LGE-MRI images can be improved by using complementary information provided by non-contrast MRI sequences

A persistent homology-based topological loss function for multi-class CNN segmentation of cardiac MRI

With respect to spatial overlap, CNN-based segmentation of short axis cardiovascular magnetic resonance (CMR) images has achieved a level of performance consistent with inter observer variation. However, conventional training procedures frequently depend on pixel-wise loss functions, limiting optimisation with respect to extended or global features. As a result, inferred segmentations can lack spatial coherence, including spurious connected components or holes. Such results are implausible, violating the anticipated topology of image segments, which is frequently known a priori. Addressing this challenge, published work has employed persistent homology, constructing topological loss functions for the evaluation of image segments against an explicit prior. Building a richer description of segmentation topology by considering all possible labels and label pairs, we extend these losses to the task of multi-class segmentation. These topological priors allow us to resolve all topological errors in a subset of 150 examples from the ACDC short axis CMR training data set, without sacrificing overlap performance.Comment: To be presented at the STACOM workshop at MICCAI 202

LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentation

Deep Learning (DL) models are becoming larger, because the increase in model size might offer significant accuracy gain. To enable the training of large deep networks, data parallelism and model parallelism are two well-known approaches for parallel training. However, data parallelism does not help reduce memory footprint per device. In this work, we introduce Large deep 3D ConvNets with Automated Model Parallelism (LAMP) and investigate the impact of both input's and deep 3D ConvNets' size on segmentation accuracy. Through automated model parallelism, it is feasible to train large deep 3D ConvNets with a large input patch, even the whole image. Extensive experiments demonstrate that, facilitated by the automated model parallelism, the segmentation accuracy can be improved through increasing model size and input context size, and large input yields significant inference speedup compared with sliding window of small patches in the inference. Code is available\footnote{https://monai.io/research/lamp-automated-model-parallelism}.Comment: MICCAI 2020 Early Accepted paper. Code is available\footnote{https://monai.io/research/lamp-automated-model-parallelism

New light for time series: international collaboration in ship-based ecosystem monitoring.

Ship-based biogeochemical and ecological time series are one of the most valuable tools to characterize and quantify ocean ecosystems. These programs continuously provided major breakthroughs in understanding ecosystem variability, allow quantification of the ocean carbon cycle, and help understand the processes that link biodiversity, food webs, and changes in services that benefit human societies. A quantum jump in regional and global ocean ecosystem science can be gained by aggregating observations from individual time series that are distributed across different oceans and which are managed by different countries. The collective value of these data is greater than that provided by each time series individually. However, maintaining time series requires a commitment by the science community and sponsor agencies.. Based on the success of existing initiatives, e.g. ICES and SCOR working groups, IOC-UNESCO launched the International Group for Marine Ecological Time Series (IGMETS, http://igmets.net) to promote collaborations across different individual projects, and jointly look at holistic changes within different ocean regions. The effort explores the reasons and connections for changes in phytoplankton and zooplankton at a global level and identifies locations where particularly large changes may be ocurring. This compilation will facilitate better coordination, communication, and data intercomparability among time series.IEO (RADIALES) IOC-UNESC