The radio continuum spectrum of Mira A and Mira B up to submillimeter wavelengths

We present new measurements of the flux densities at submillimeter wavelengths based on ALMA band 7 (338 GHz) and band 9 (679 GHz) observations to better constrain the origin of the continuum emission of the Mira AB binary system and to check its orbit. We have measured the Mira A and Mira B continuum in ALMA band 7, with a resolution of ~0"31, and for the first time in ALMA band 9, with a resolution of ~0"18. We resolved the binary system at both bands, and derived the continuum spectral index of the stars and their relative position. We also analyzed ALMA SciVer data obtained in bands 6 and 3. Measurements at centimeter wavelengths obtained by other authors have been included in our study of the spectral energy distribution of the Mira components. The Mira A continuum emission has a spectral index of 1.98+-0.04 extending from submillimeter down to centimeter wavelengths. The spectral index of the Mira B continuum emission is 1.93+-0.06 at wavelengths ranging from submillimeter to ~3.1 mm, and a shallower spectral index of 1.22+-0.09 at longer wavelengths. The Mira A continuum emission up to submillimeter wavelengths is consistent with that of a radio photosphere surrounding the evolved star for which models predict a spectral index close to 2. The Mira B continuum emission cannot be described with a single ionized component. An extremely compact and dense region around the star can produce the nearly thermal continuum measured in the 0.4-3.1 mm wavelength range, and an inhomogeneous, less dense, and slightly larger ionized envelope could be responsible for the emission at longer wavelengths. Our results illustrate the potential of ALMA for high precision astrometry of binary systems. We have found a significant discrepancy of ~14 milliarcsec between the ALMA measurements and the predicted orbit positions.Comment: 6 pages, 3 figures, 2 tables, accepted for publication in Astronomy and Astrophysic

Bayesian neural networks to analyze hyperspectral datasets using uncertainty metrics

Machine learning techniques, and specifically neural networks, have proved to be very useful tools for image classification tasks. Nevertheless, measuring the reliability of these networks and calibrating them accurately are very complex. This is even more complex in a field like hyperspectral imaging, where labeled data are scarce and difficult to generate. Bayesian neural networks (BNNs) allow to obtain uncertainty metrics related to the data processed (aleatoric), and to the uncertainty generated by the model selected (epistemic). On this work, we will demonstrate the utility of BNNs by analyzing the uncertainty metrics obtained by a BNN over five of the most used hyperspectral images datasets. In addition, we will illustrate how these metrics can be used for several practical applications such as identifying predictions that do not reach the required level of accuracy, detecting mislabeling in the dataset, or identifying when the predictions are affected by the increase of the level of noise in the input data

An Efficient Hardware Accelerator to Handle Compressed Filters and Avoid Useless Operations in CNNs

Due to sparsity, a significant percentage of the operations carried out in Convolutional Neural Networks (CNNs) contains a zero in at least one of their operands. Different approaches try to take advantage of sparsity in two different ways. On the one hand, sparse matrices can be easily compressed, saving space and memory bandwidth. On the other hand, multiplications with zero in their operands can be avoided. We propose the implementation in an FPGA of an architecture for CNNs capable of taking advantage of both, sparsity and filter compression.    Due to sparsity, a significant percentage of the operations carried out in Convolutional Neural Networks (CNNs) contains a zero in at least one of their operands. Different approaches try to take advantage of sparsity in two different ways. On the one hand, sparse matrices can be easily compressed, saving space and memory bandwidth. On the other hand, multiplications with zero in their operands can be avoided. We propose the implementation in an FPGA of an architecture for CNNs capable of taking advantage of both, sparsity and filter compression.     &nbsp

A New Radio Molecular Line Survey of Planetary Nebulae: HNC/HCN as a Diagnostic of Ultraviolet Irradiation

Certain planetary nebulae contain shells, filaments, or globules of cold gas and dust whose heating and chemistry are likely driven by UV and X-ray emission from their central stars and from wind-collision-generated shocks. We present the results of a survey of molecular line emission in the 88-236 GHz range from nine nearby (<1.5 kpc) planetary nebulae spanning a range of UV and X-ray luminosities, using the 30 m telescope of the Institut de Radioastronomie Millimetrique. Rotational transitions of thirteen molecules, including CO isotopologues and chemically important trace species, were observed and the results compared with and augmented by previous studies of molecular gas in PNe. Lines of the molecules HCO+, HNC, HCN, and CN, which were detected in most objects, represent new detections for five planetary nebulae in our study. Specifically, we present the first detections of 13CO (1-0, 2-1), HCO+, CN, HCN, and HNC in NGC 6445; HCO+ in BD+303639; 13CO (2-1), CN, HCN, and HNC in NGC 6853; and 13CO (2-1) and CN in NGC 6772. Flux ratios were analyzed to identify correlations between the central star and/or nebular UV and X-ray luminosities and the molecular chemistries of the nebulae. This analysis reveals a surprisingly robust dependence of the HNC/HCN line ratio on PN central star UV luminosity. There exists no such clear correlation between PN X-rays and various diagnostics of PN molecular chemistry. The correlation between HNC/HCN ratio and central star UV luminosity demonstrates the potential of molecular emission line studies of PNe for improving our understanding of the role that high-energy radiation plays in the heating and chemistry of photodissociation regions.Comment: 17 pages, 17 figures, 6 tables, accepted for publication in Astronomy & Astrophysic

Outflows From Evolved Stars: The Rapidly Changing Fingers Of CRL 618

Our ultimate goal is to probe the nature of the collimator of the outflows in the pre-planetary nebula CRL 618. CRL 618 is uniquely suited for this purpose owing to its multiple, bright, and carefully studied finger-shaped outflows east and west of its nucleus. We compare new Hubble Space Telescope images to images in the same filters observed as much as 11 yr ago to uncover large proper motions and surface brightness changes in its multiple finger-shaped outflows. The expansion age of the ensemble of fingers is close to 100 yr. We find strong brightness variations at the fingertips during the past decade. Deep IR images reveal a multiple ring-like structure of the surrounding medium into which the outflows propagate and interact. Tightly constrained three-dimensional hydrodynamic models link the properties of the fingers to their possible formation histories. We incorporate previously published complementary information to discern whether each of the fingers of CRL 618 are the results of steady, collimated outflows or a brief ejection event that launched a set of bullets about a century ago. Finally, we argue on various physical grounds that fingers of CRL 618 are likely to be the result of a spray of clumps ejected at the nucleus of CRL 618 since any mechanism that form a sustained set of unaligned jets is unprecedented.HST GO 11580NASA through Space Telescope Science Institute GO11580NASA NAS5-26555Boeing ScholarshipOffice of Undergraduate Academic Affairs at the University of WashingtonSpanish MICINN CSD2009-00038NASA Office of Space Science NAG5-7584Astronom

Arte paleolítico de la meseta española.

Sin resume

GPU-friendly neural networks for remote sensing scene classification

Convolutional neural networks (CNNs) have proven to be very efficient for the analysis of remote sensing (RS) images. Due to the inherent complexity of extracting features from these images, along with the increasing amount of data to be processed (and the diversity of applications), there is a clear tendency to develop and employ increasingly deep and complex CNNs. In this regard, graphics processing units (GPUs) are frequently used to optimize their execution, both for the training and inference stages, optimizing the performance of neural models through their many-core architecture. Hence, the efficient use of the GPU resources should be at the core of optimizations. This letter analyzes the possibilities of using a new family of CNNs, denoted as TResNets, to provide an efficient solution to the RS scene classification problem. Moreover, the considered models have been combined with mixed precision to enhance their training performance. Our experimental results, conducted over three publicly available RS data sets, show that the proposed networks achieve better accuracy and more efficient use of GPU resources than other state-of-the-art networks. Source code is available at https://github.com/mhaut/GPUfriendlyRS

Lessons from the Ionised and Molecular Mass of Post-CE PNe

Close binary evolution is widely invoked to explain the formation of axisymmetric planetary nebulae after a brief common envelope phase. The evolution of the primary would be interrupted abruptly, its still quite massive envelope being fully ejected to form the PN, which should be more massive than a planetary nebula coming from the same star, were it single. We test this hypothesis by investigating the ionised and molecular masses of a sample consisting of 21 post-common-envelope planetary nebulae, roughly one-fifth of their known total population, and comparing them to a large sample of regular planetary nebulae (not known to host close-binaries). We find that post-common-envelope planetary nebulae arising from single-degenerate systems are, on average, neither more nor less massive than regular planetary nebulae, whereas post-common-envelope planetary nebulae arising from double-degenerate systems are considerably more massive and show substantially larger linear momenta and kinetic energy than the rest. The reconstruction of the common envelope of four objects further suggests that the mass of single-degenerate nebulae actually amounts to a very small fraction of the envelope of their progenitor stars. This leads to the uncomfortable question of where the rest of the envelope is, raising serious doubts on our understanding of these intriguing objects

Inference in supervised spectral classifiers for on-board hyperspectral imaging: An overview

Machine learning techniques are widely used for pixel-wise classification of hyperspectral images. These methods can achieve high accuracy, but most of them are computationally intensive models. This poses a problem for their implementation in low-power and embedded systems intended for on-board processing, in which energy consumption and model size are as important as accuracy. With a focus on embedded anci on-board systems (in which only the inference step is performed after an off-line training process), in this paper we provide a comprehensive overview of the inference properties of the most relevant techniques for hyperspectral image classification. For this purpose, we compare the size of the trained models and the operations required during the inference step (which are directly related to the hardware and energy requirements). Our goal is to search for appropriate trade-offs between on-board implementation (such as model size anci energy consumption) anci classification accuracy