Dennis Edward Hyde, Letícia Pereira Alves, Daniel Rodrigues Costa Mello
et al.
This article presents the National Parks Astrotourism Potential Index (IASTRO), an innovative tool to evaluate and compare the potential for astro tourism in the 75 Brazilian national parks. IASTRO combines three crucial parameters: night sky quality, open sky probability, and tourism infrastructure, which encompasses the presence of guides and the availability of different experiences of overnight stays. The detailed methodology for data collection and IASTRO calculation is described, including the weighting of parameters, with night sky quality receiving greater weight for its fundamental importance for celestial observation. The results reveal a diverse distribution of IASTRO among the parks, highlighting those located in the Cerrado and Caatinga biomes, which present more favorable night sky and climatological conditions. The in-depth discussion explores the relationship between IASTRO and other factors, such as the number of visitors and the characteristics of the biomes, revealing an untapped potential for astro tourism in many national parks. The study also highlights the importance of tourism infrastructure and simulates how improvements in this aspect can boost astro tourism potential in various parks. The final considerations emphasize IASTRO as a valuable tool for protected area management and for the formulation of public policies, promoting the preservation of the starry sky and the development of astro tourism in a sustainable way. The article concludes that Brazil has enormous potential to become a world-class astro tourism destination, with its national parks offering exceptional conditions for celestial observation and connection with nature. This pioneering study contributes to the field of ecotourism by providing a comprehensive and innovative index to assess the astro tourism potential in national parks. IASTRO can be used by park managers, researchers, tourists, astronomy enthusiasts and policy makers to identify and prioritize parks with greater potential for this activity, assisting in the planning of actions and investments that promote the development of astro tourism in Brazil. In summary, the article presents an innovative index to assess the astro tourism potential in Brazilian national parks, highlighting the importance of night sky quality, open sky probability, and tourism infrastructure. The study reveals an untapped potential for astro tourism in many parks and highlights the importance of investments in tourism infrastructure to boost this activity. IASTRO is presented as a valuable tool for protected area management and for the formulation of public policies, aiming at the sustainable development of astro tourism in Brazil.
Model update (MU) and candidate evaluation (CE) are classical steps incorporated inside many stochastic trust-region (TR) algorithms. The sampling effort exerted within these steps, often decided with the aim of controlling model error, largely determines a stochastic TR algorithm's sample complexity. Given that MU and CE are amenable to variance reduction, we investigate the effect of incorporating common random numbers (CRN) within MU and CE on complexity. Using ASTRO and ASTRO-DF as prototype first-order and zeroth-order families of algorithms, we demonstrate that CRN's effectiveness leads to a range of complexities depending on sample-path regularity and the oracle order. For instance, we find that in first-order oracle settings with smooth sample paths, CRN's effect is pronounced -- ASTRO with CRN achieves $\tilde{O}(\epsilon^{-2})$ a.s. sample complexity compared to $\tilde{O}(\epsilon^{-6})$ a.s. in the generic no-CRN setting. By contrast, CRN's effect is muted when the sample paths are not Lipschitz, with the sample complexity improving from $\tilde{O}(\epsilon^{-6})$ a.s. to $\tilde{O}(\epsilon^{-5})$ and $\tilde{O}(\epsilon^{-4})$ a.s. in the zeroth- and first-order settings, respectively. Since our results imply that improvements in complexity are largely inherited from generic aspects of variance reduction, e.g., finite-differencing for zeroth-order settings and sample-path smoothness for first-order settings within MU, we anticipate similar trends in other contexts.
Astronaut photography, spanning six decades of human spaceflight, presents a unique Earth observations dataset with immense value for both scientific research and disaster response. Despite their significance, accurately localizing the geographical extent of these images, which is crucial for effective utilization, poses substantial challenges. Cur-rent, manual localization efforts are time-consuming, mo-tivating the need for automated solutions. We propose a novel approach - leveraging image retrieval - to address this challenge efficiently. We introduce innovative training techniques which contribute to the development of a high-performance model, EarthLoc. We develop six evaluation datasets and perform a comprehensive benchmark comparing EarthLoc to existing methods, showcasing its superior efficiency and accuracy. Our approach marks a signifi-cant advancement in automating the localization of astro-naut photography, which will help bridge a critical gap in Earth observations data. Code and datasets are available at https://github.com/gmberton/EarthLoc.
As recentes observações cosmológicas e astrofísicas indicam que o Universo está em expansão, com uma fase muito quente no passado, o que para muitos indica um início do Universo. Neste artigo, pretendo motivar e descrever modelos cosmológicos onde o Universo não teve um começo nem terá fim, contendo uma longa fase de contração seguida por um ricochete que o lança na fase de expansão observada atualmente. Por isso são chamados de Modelos de Ricochete. No contexto onde há infinitas fases de contração e expansão, com ricochetes entre elas, são também chamados de Modelos Cíclicos. Esse conjunto de modelos será motivado teoricamente, e confrontado com as mais recentes observações cosmológicas. Também será comparado com os modelos onde o Universo teve um começo sucedido por uma fase inflacionária, que será explicada em outros artigos deste número dos Cadernos de Astronomia.
The joint JAXA/NASA ASTRO-H mission is the sixth in a series of highly successful X-ray missions initiated by the Institute of Space and Astronautical Science (ISAS). ASTRO-H will investigate the physics of the highenergy universe via a suite of four instruments, covering a very wide energy range, from 0.3 keV to 600 keV. These instruments include a high-resolution, high-throughput spectrometer sensitive over 0.3–12 keV with high spectral resolution of ΔE ≦ 7 eV, enabled by a micro-calorimeter array located in the focal plane of thin-foil X-ray optics; hard X-ray imaging spectrometers covering 5–80 keV, located in the focal plane of multilayer-coated, focusing hard X-ray mirrors; a wide-field imaging spectrometer sensitive over 0.4–12 keV, with an X-ray CCD camera in the focal plane of a soft X-ray telescope; and a non-focusing Compton-camera type soft gamma-ray detector, sensitive in the 40–600 keV band. The simultaneous broad bandpass, coupled with high spectral resolution, will enable the pursuit of a wide variety of important science themes.
ASTRO-H White Papers are meant to provide useful information to scientists who plan observations from the satellite. This short paper introduces the 16 ASTRO-H White Papers in addition to general description of the satellite and its new features.
The sheer growth in data volume and Hadoop cluster size make it a significant challenge to diagnose and locate problems in a production-level cluster environment efficiently and within a short period of time. Often times, the distributed monitoring systems are not capable of detecting a problem well in advance when a large-scale Hadoop cluster starts to deteriorate i n performance or becomes unavailable. Thus, inc o m i n g workloads, scheduled between the time when cluster starts to deteriorate and the time when the problem is identified, suffer from longer execution times. As a result, both reliability and throughput of the cluster reduce significantly. In this paper, we address this problem by proposing a system called Astro, which consists of a predictive model and an extension to the Hadoop scheduler. The predictive model in Astro takes into account a rich set of cluster behavioral information that are collected by monitoring processes and model them using machine learning algorithms to predict future behavior of the cluster. The Astro predictive model detects anomalies in the cluster and also identifies a ranked set of metrics that have contributed the most towards the problem. The Astro scheduler uses the prediction outcome and the list of metrics to decide whether it needs to move and reduce workloads from the problematic cluster nodes or to prevent additional workload allocations to them, in order to improve both throughput and reliability of the cluster. The results demonstrate that the Astro scheduler improves usage of cluster compute resources significantly by 64.23% compared to traditional Hadoop. Furthermore, the runtime of the benchmark application reduced by 26.68% during the time of anomaly, thus improving the cluster throughput.
Tadayuki Takahashi, K. Mitsuda, Rich Kelley
et al.
In this white paper we describe the prospects for ASTRO-H for the study of outflows from active galactic nuclei. The most important breakthroughs in this field are expected to arise from the high spectral resolution and sensitivity in the Fe-K band, combined with broad-band sensitivity over the full X-ray band and spectral capabilities also at lower energies. The sensitivity in the Fe-K region allows to extend the absorption measure distribution of the outflow out to the highest ionisation states accessible, where observations with current X-ray missions indicate that most of the outflowing gas is to be found. Due to the high-resolution and sensitivity it will also be able to give the definitive proof for the existence of ultra-fast outflows, and if so, characterise their physical properties in great detail. These ultra-fast outflows carry very large amounts of energy and momentum, and are of fundamental importance for feedback studies. We show how the ASTRO-H observations in general can help to constrain numerical models for outflows. The link to reflection and emission processes is also discussed, as well as the possible relation between outflows and relativistic emission lines. Finally, we discuss the prospects for other related categories of objects like BAL quasars, partially covered sources and Compton thick outflows.
X-ray spectra from cores of galaxy clusters can be strongly distorted by resonant scattering of line photons, aecting metal abundance and gas velocity measurements. We introduce simulated spectral models that take into account the resonant scattering eect, radial variations of thermodynamic properties of the hot gas, projection eects and small-scale isotropic gas motions. The key feature of the models is that all these eects are treated self-consistently for the whole spectrum, rather than for individual lines. The model spectra are publicly available and can be used for direct comparison with observed projected spectra. Comparison with the existing XMM-Newton and Chandra data of the Perseus Cluster shows that even though there is no strong evidence for the resonant scattering in Perseus, the low energy resolution of the X-ray CCDs is not sucient to robustly distinguish spectral distortions due to the resonant scattering, dierent metal abundance proles and dierent levels of gas turbulence. Future AstroH data will resolve most of the problems we are facing with CCDs. With the help of our models, the resonant scattering analysis can be done self-consistently using the whole spectral information, constraining the level of gas turbulence already with a 100 ks observation with Astro-H.
In this paper we present the various concepts behind the Astro-WISE Information System. The concepts form a blueprint for general scientific information systems (WISE) which can satisfy a wide and challenging range of requirements for the data dissemination, storage and processing for various fields in science. We review the main features of the information system and its practical implementation.