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The behavior of coronal holes (CHs) as open magnetic field lines and a substrate for the high-speed exit of solar winds is an essential aspect of solar activity. This paper studies the statistical properties of CHs around the maximum activity of cycle 24 (13 May 2010 to 31 October 2017) observed by the Atmospheric Imaging Assembly at 193 Å images. We showed that the size of CHs follows the power-law distributions indicating the scale-free behavior of CHs. The butterfly diagram of CHs demonstrates that the overall number of CHs shows a similar distribution in the two hemispheres. Large CHs with sizes ⩾ 2.57 × 10 5 arcsec2 (top 3% of observed CHs) more commonly appear at latitudes above 30 ∘ closer to the poles. We found that the largest CHs with sizes ⩾ 7.71 × 10 5 arcsec2 (top 0.03% of observed CHs) occur in the northern hemisphere. The appearance and disappearance of large CHs exhibit the asymmetrical behavior between the two hemispheres. Also, it is observed that CHs tend to have a latitudinal drift towards the poles in each hemisphere. A significant correlation between the number of sunspots and the eccentricity of equatorial CHs appeared near the solar disk center revealed that most of the CHs have elongated shapes at the maximum solar activity. In contrast, they have less abnormality during solar minimum. Applying detrended fluctuation and rescaled range analyses to the time series of CHs, we realized that the CHs have a long-term correlation and form a self-organized system. [ABSTRACT FROM AUTHOR]

Numerical methods have become a very important type of tool for celestial mechanics, especially in the study of planetary ephemerides. The errors generated during the computation are hard to know beforehand when applying a certain numerical integrator to solve a certain orbit. In that case, it is not easy to design a certain integrator for a certain celestial case when the requirement of accuracy were extremely high or the time-span of the integration were extremely large. Especially when a fixed-step method is applied, the caution and effort it takes would always be tremendous in finding a suitable time-step, because it is about whether the accuracy and time-cost of the final result are acceptable. Thus, finding the best balance between efficiency and accuracy with the least time cost appeared to be a major obstruction in the face of both numerical integrator designers and their users. To solve this problem, we investigate the variation pattern of truncation error and the pattern of rounding error distributions with time-step and time-span of the integration. According to those patterns, we promote an error estimation method that could predict the distribution of rounding errors and the total truncation errors with any time-step at any time-spot with little experimental cost, and test it with the Adams-Cowell method in the calculation of circular periodic orbits. This error estimation method is expected to be applied to the comparison of the performance of different numerical integrators, and also it can be of great help for finding the best solution to certain cases of complex celestial orbits calculations. [ABSTRACT FROM AUTHOR]

Based on comet observations from JPL (Jet Propulsion Laboratory) and MPC (Minor Planet Center), the orbits of long period comets (LPCs) were reverse-evolved to obtain their original orbits. The dynamic characteristics of LPC observation orbit and original orbit are analyzed. The results show that the distribution of reciprocal of the semi-major axis of LPC observation orbit ((1 / a) obs) is significantly different from that of the original orbit ((1 / a) ori), and the peak values are at (1 / a) obs ∼ 2 × 10 − 5 au − 1 and (1 / a) ori ∼ 6 × 10 − 5 au − 1 , respectively; large perihelion LPC (perihelion distance q > 3.1 au) and small perihelion LPC (perihelion distance q ≤ 3.1 au) have the same peak position of original orbit ((1 / a) ori), both located at 1 × 10 − 4 au − 1 ; the peak positions of original reciprocal semi-major axis of LPCs between JPL and MPC are the same, both at 1 × 10 − 4 au − 1 . Many Oort cloud comets have large perihelion distances (more than 50% of comets with q > 3.1 au). It will provide information to understand the dynamical characteristics of long period comets and Oort cloud comets, and will also provide a research basis for future space missions targeting long period comets. [ABSTRACT FROM AUTHOR]

The local Cosmic Ray (CR) energy spectrum exhibits a spectral softening at energies around 3 PeV. Sources which are capable of accelerating hadrons to such energies are called hadronic PeVatrons. However, hadronic PeVatrons have not yet been firmly identified within the Galaxy. Several source classes, including Galactic Supernova Remnants (SNRs), have been proposed as PeVatron candidates. The potential to search for hadronic PeVatrons with the Cherenkov Telescope Array (CTA) is assessed. The focus is on the usage of very high energy γ -ray spectral signatures for the identification of PeVatrons. Assuming that SNRs can accelerate CRs up to knee energies, the number of Galactic SNRs which can be identified as PeVatrons with CTA is estimated within a model for the evolution of SNRs. Additionally, the potential of a follow-up observation strategy under moonlight conditions for PeVatron searches is investigated. Statistical methods for the identification of PeVatrons are introduced, and realistic Monte-Carlo simulations of the response of the CTA observatory to the emission spectra from hadronic PeVatrons are performed. Based on simulations of a simplified model for the evolution for SNRs, the detection of a γ -ray signal from in average 9 Galactic PeVatron SNRs is expected to result from the scan of the Galactic plane with CTA after 10 h of exposure. CTA is also shown to have excellent potential to confirm these sources as PeVatrons in deep observations with O (100) hours of exposure per source. [ABSTRACT FROM AUTHOR]

Image restoration in astronomy has been considered a vital step in many ground-based observational programs that often suffer from sub-optimal seeing due to atmospheric turbulence, distortion of stellar shapes due to instrumental aberrations, trailing, and other issues. It holds importance for various tasks: improved astrometry, deblending of overlapping sources, faint source detection, and identification of point sources near bright extended objects, such as galaxies, to name a few. We conduct an empirical study by applying the Scaled Gradient Projection (SGP) iterative image deconvolution algorithm to restore distorted stellar shapes in our observed data. We investigate using a more flexible divergence measure, the β -divergence, which contains the commonly-used Kullback–Leibler (KL) divergence as a special case and allows automatic adaptation of the parameter β to the data. An extensive set of experiments comparing the performance of SGP and its β -divergence variant (β -SGP) is carried out on extracted star stamps and on images containing multiple stars (both crowded and relatively sparser fields). We show a consistent enhancement in the flux conservation across all considered scenarios using β -SGP compared to SGP. Using a few quantifiable metrics such as the Full-Width-at-Half-Maximum (FWHM) and ellipticity of stars, we observe that β -SGP improves restoration quality, compared to the SGP, in many cases and still preserves restoration quality in others. We conclude that generalized versions of image restoration algorithms are more robust due to their enhanced flexibility and could be a promising modification for astronomical image restoration. [ABSTRACT FROM AUTHOR]

Atmospheric seeing is one of the most important parameters for evaluating and monitoring an astronomical site. Moreover, being able to predict the seeing in advance can guide observing decisions and significantly improve the efficiency of telescopes. However, it is not always easy to obtain long-term and continuous seeing measurements from a standard instrument such as differential image motion monitor (DIMM), especially for those unattended observatories with challenging environments such as Dome A, Antarctica. In this paper, we present a novel machine learning-based framework for estimating and predicting seeing at a height of 8 m at Dome A, Antarctica, using only the data from a multi-layer automated weather station (AWS). In comparison with DIMM data, our estimate has a root mean square error (RMSE) of 0.18 arcsec, and the RMSE of predictions 20 min in the future is 0.12 arcsec for the seeing range from 0 to 2.2 arcsec. Compared with the persistence, where the forecast is the same as the last data point, our framework reduces the RMSE by 37 per cent. Our method predicts the seeing within a second of computing time, making it suitable for real-time telescope scheduling. [ABSTRACT FROM AUTHOR]

The increase in data volume is challenging the suitability of non-distributed and non-scalable algorithms, despite advancements in hardware. An example of this challenge is clustering. Considering that optimal clustering algorithms scale poorly with increased data volume or are intrinsically non-distributed, accurate clustering of large datasets is increasingly resource-heavy, relying on substantial and expensive compute nodes. This scenario forces users to choose between accuracy and scalability. In this work, we introduce HiErArchical Data Splitting and Stitching (HEADSS), a Python package designed to facilitate clustering at scale. By automating the splitting and stitching, it allows repeatable handling, and removal, of edge effects. We implement HEADSS in conjunction with HDBSCAN, where we achieve orders of magnitude reduction in single node memory requirements for both non-distributed and distributed implementations, with the latter offering similar order of magnitude reductions in total run times while recovering analogous accuracy. Furthermore, our method establishes a hierarchy of features by using a subset of clustering features to split the data. 1 1 Source code and examples are available at https://github.com/D-Crake/HEADSS. [ABSTRACT FROM AUTHOR]

The increasingly large amount of cosmological data coming from ground-based and space-borne telescopes requires highly efficient and fast enough data analysis techniques to maximise the scientific exploitation. In this work, we explore the capabilities of supervised machine learning algorithms to learn the properties of the large-scale structure of the Universe, aiming at constraining the matter density parameter, Ω m. We implement a new Artificial Neural Network for a regression data analysis, and train it on a large set of galaxy two-point correlation functions in standard cosmologies with different values of Ω m. The training set is constructed from log-normal mock catalogues which reproduce the clustering of the Baryon Oscillation Spectroscopic Survey (BOSS) galaxies. The presented statistical method requires no specific analytical model to construct the likelihood function, and runs with negligible computational cost, after training. We test this new Artificial Neural Network on real BOSS data, finding Ω m = 0. 309 ± 0. 008 , which is consistent with standard analysis results. [ABSTRACT FROM AUTHOR]

Filamentary structures (one-dimensional manifolds) are ubiquitous in astronomical data sets. Be it in particle simulations or observations, filaments are always tracers of a perturbation in the equilibrium of the studied system and hold essential information on its history and future evolution. However, the recovery of such structures is often complicated by the presence of a large amount of background and transverse noise in the observation space. While the former is generally considered detrimental to the analysis, the latter can be attributed to measurement errors and it can hold essential information about the structure. To further complicate the scenario, one-dimensional manifolds (filaments) are generally non-linear and their geometry difficult to extract and model. Thus, in order to study hidden manifolds within the dataset, particular care has to be devoted to background noise removal and transverse noise modeling, while still maintaining accuracy in the recovery of their geometrical structure. We propose 1-DREAM: a toolbox composed of five main Machine Learning methodologies whose aim is to facilitate manifold extraction in such cases. Each methodology has been designed to address particular issues when dealing with complicated low-dimensional structures convoluted with noise and it has been extensively tested in previously published works. However, for the first time, in this work all methodologies are presented in detail, joint within a cohesive framework and demonstrated for three particularly interesting astronomical cases: a simulated jellyfish galaxy, a filament extracted from a simulated cosmic web and the stellar stream of Omega-Centauri as observed with the GAIA DR2. Two newly developed visualization techniques are also proposed, that take full advantage of the results obtained with 1-DREAM. This contribution presents the toolbox in all its details and the code is made publicly available to benefit the community. The controlled experiments on a purposefully built data set prove the accuracy of the pipeline in recovering the real underlying structures. [Display omitted] • Toolbox with five semi-automated methods for manifold extraction and modeling. • Background noise removal; • Transverse noise attenuation; • Point-dimensionality estimation; • Manifold's mean curve extraction and corresponding transverse noise modeling. • Two visualization approaches to represent information on extracted manifolds. • Global: Bi-dimensional profile. Information shown along main manifold's directions; • Local: coherent orthonormal coordinate frames. Orthogonal plane shifted along length. • Demonstration of the toolbox to three interesting astronomical cases. • Simulated dwarf galaxy with elongated arms. • Extracted simulated cosmic web filament. • Omega-Centauri stream as observed via the GAIA DR2. [ABSTRACT FROM AUTHOR]

The Boltzmann–Gibbs thermodynamic equilibrium state of charged particles pitch-angle scattered by weak plasma waves is discussed. Degrees of freedom of these waves play a fundamental role in constructing the grand canonical ensemble. Via the gyro-resonance condition, fast particles have an inverse break power-law spectrum for ɛ − μ ≪ T , where ɛ is the particle energy, μ is the chemical potential, T is the temperature. The break energies are the rest energy and − μ. For ɛ ≪ − μ ≪ T , the energy spectral index α is δ / 2 + 1 and δ + 1 for non- and ultra-relativistic particles, respectively, with δ an effective fractal dimension of background magnetic field lines. The spectral index for − μ ≪ ɛ ≪ T is α + 1. This thermal equilibrium scenario, combined with the leaky-box model and cosmic-ray observations, seems to suggest that the Galactic magnetic field is super-diffusive with δ ≈ 1. 4. [ABSTRACT FROM AUTHOR]

A Generative Adversarial Network (GAN) was used to investigate the statistics and properties of voids in a Λ CDM universe. The total number of voids and the distribution of void sizes is similar in both sets of images and, within the formal error bars, the mean void properties are consistent with each other. However, the generated images yield somewhat fewer small voids than do the simulated images. In addition, the generated images yield far fewer voids with central density contrast ∼ − 1. Because the generated images yield fewer of the emptiest voids, the distribution of the mean interior density contrast is systematically higher for the generated voids than it is for the simulated voids. The mean radial underdensity profiles of the largest voids are similar in both sets of images, but systematic differences are apparent. On small scales (r < 0. 5 r v ), the underdensity profiles of the voids in the generated images exceed those of the voids in the simulated images. On large scales (r > 0. 5 r v ), the underdensity profiles of the voids in the simulated images exceed those of the voids in the generated images. The discrepancies between the void properties in the two sets of images are attributable to the GAN struggling to capture absolute patterns in the data. In particular, the GAN produces too few pixels with density contrasts ∼ − 1 and too many pixels with density contrasts in the range ∼ − 0. 88 to ∼ − 0. 63. [ABSTRACT FROM AUTHOR]