Using a magnetic carpet as model for the near surface solar magnetic field we study its effects on the propagation of energy injectected by photospheric footpoint motions. Such a magnetic carpet structure is topologically highly non-trivial and with its magnetic nulls exhibits qualitatively different behavior than simpler magnetic fields. We show that the presence of magnetic fields connecting back to the photosphere inhibits the propagation of energy into higher layers of the solar atmosphere, like the solar corona. By applying certain types of footpoint motions the magnetic field topology is is greatly reduced through magnetic field reconnection which facilitates the propagation of energy and disturbances from the photosphere.

The Hong Kong/AAO/Strasbourg Hα (HASH) planetary nebula database is an online research platform providing free and easy access to the largest and most comprehensive catalogue of known Galactic PNe and a repository of observational data (imaging and spectroscopy) for these and related astronomical objects. The main motivation for creating this system is resolving some of long standing problems in the field e.g. problems with mimics and dubious and/or misidentifications, errors in observational data and consolidation of the widely scattered data-sets. This facility allows researchers quick and easy access to the archived and new observational data and creating and sharing of non-redundant PN samples and catalogues.

I have proposed that long Hα fibrils are caused by heating events of which the tracks are afterwards outlined by contrails of cooling gas with extraordinary Hα opacity and yet larger opacity at the ALMA wavelengths. Here I detail the radiative transfer background.

In the modern galaxy surveys photometric redshifts play a central role in a broad range of studies, from gravitational lensing and dark matter distribution to galaxy evolution. Using a dataset of ~ 25,000 galaxies from the second data release of the Kilo Degree Survey (KiDS) we obtain photometric redshifts with five different methods: (i) Random forest, (ii) Multi Layer Perceptron with Quasi Newton Algorithm, (iii) Multi Layer Perceptron with an optimization network based on the Levenberg-Marquardt learning rule, (iv) the Bayesian Photometric Redshift model (or BPZ) and (v) a classical SED template fitting procedure (Le Phare). We show how SED fitting techniques could provide useful information on the galaxy spectral type which can be used to improve the capability of machine learning methods constraining systematic errors and reduce the occurrence of catastrophic outliers. We use such classification to train specialized regression estimators, by demonstrating that such hybrid approach, involving SED fitting and machine learning in a single collaborative framework, is capable to improve the overall prediction accuracy of photometric redshifts.

The tens of millions of radio sources to be detected with next-generation surveys pose new challenges, quite apart from the obvious ones of processing speed and data volumes. For example, existing algorithms are inadequate for source extraction or cross-matching radio and optical/IR sources, and a new generation of algorithms are needed using machine learning and other techniques. The large numbers of sources enable new ways of testing astrophysical models, using a variety of “large-n astronomy” techniques such as statistical redshifts. Furthermore, while unexpected discoveries account for some of the most significant discoveries in astronomy, it will be difficult to discover the unexpected in large volumes of data, unless specific software is developed to mine the data for the unexpected.

A detailed examination of new high quality radio catalogues (e.g. Cornish) in combination with available mid-infrared (MIR) satellite imagery (e.g. Glimpse) has allowed us to find 70 new planetary nebula (PN) candidates based on existing knowledge of their typical colors and fluxes. To further examine the nature of these sources, multiple diagnostic tools have been applied to these candidates based on published data and on available imagery in the HASH (Hong Kong/ AAO/ Strasbourg Hα planetary nebula) research platform. Some candidates have previously-missed optical counterparts allowing for spectroscopic follow-up. Indeed, the single object spectroscopically observed so far has turned out to be a bona fide PN.

Using recent data from photometric monitoring and data from the photographic plate archives we aim to study, the long-term photometric behavior of FUors. The construction of the historical light curves of FUors could be very important for determining the beginning of the outburst, the time to reach the maximum light, the rate of increase and decrease in brightness, the pre-outburst variability of the star. Our CCD photometric observations were performed with the telescopes of the Rozhen (Bulgaria) and Skinakas (Crete, Greece) observatories. Most suitable for long-term photometric study are the plate archives of the big Schmidt telescopes, as the telescopes at Kiso Observatory, Asiago Observatory, Palomar Observatory and others. In comparing our results with light curves of the well-studied FUors, we conclude that every new FUor object shows different photometric behavior. Each known FUor has a different rate of increase and decrease in brightness and a different light curve shape.

The so-called Carrington Event on September 1, 1859, is clearly the solar outburst that brought the realization to the inhabitants of Earth that weather existed in space, and that space weather was important to the rapidly developing technological infrastructure on Earth. It is important to understand not only how space weather affects our technological systems, but like the case of atmospheric weather, the possible intensity of such weather, the frequency of extreme events, and how to predict them. This paper reviews what we know about one class of extreme space weather events, the superfast arrival events, how best to compare them given our limited diagnostics in past events and even at the current time, and suggests a direction for progress in this field.

I review the evolution of low-mass stars with outer convective zones over timescales of millions-to-billions of years, from the pre-main sequence to solar-age, ~4.6 Gyr (Bahcall et al. 1995; Amelin et al. 2010), and beyond. I discuss the evolution of high-energy coronal and chromospheric emission, the links with stellar rotation and magnetism, and the emergence of the rotation-activity relation for stars within young clusters.

Euclid is a Europe-led cosmology space mission dedicated to a visible and near infrared survey of the entire extra-galactic sky. Its purpose is to deepen our knowledge of the dark content of our Universe. After an overview of the Euclid mission and science, this contribution describes how the community is getting organized to face the data analysis challenges, both in software development and in operational data processing matters. It ends with a more specific account of some of the main contributions of the Swiss Science Data Center (SDC-CH).

With new catalogues arriving such as the Gaia DR1, containing more than a billion objects, new methods of handling and visualizing these data volumes are needed. We show that by calculating statistics on a regular (N-dimensional) grid, visualizations of a billion objects can be done within a second on a modern desktop computer. This is achieved using memory mapping of hdf5 files together with a simple binning algorithm, which are part of a Python library called vaex. This enables efficient exploration or large datasets interactively, making science exploration of large catalogues feasible. Vaex is a Python library and an application, which allows for interactive exploration and visualization. The motivation for developing vaex is the catalogue of the Gaia satellite, however, vaex can also be used on SPH or N-body simulations, any other (future) catalogues such as SDSS, Pan-STARRS, LSST, etc. or other tabular data. The homepage for vaex is http://vaex.astro.rug.nl.

We review recent advances and ongoing work in evolving the NASA/IPAC Extragalactic Database (NED) beyond an object reference database into a data mining discovery engine. Updates to the infrastructure and data integration techniques are enabling more than a 10-fold expansion; NED will soon contain over a billion objects with their fundamental attributes fused across the spectrum via cross-identifications among the largest sky surveys (e.g., GALEX, SDSS, 2MASS, AllWISE, EMU), and over 100,000 smaller but scientifically important catalogs and journal articles. The recent discovery of super-luminous spiral galaxies exemplifies the opportunities for data mining and science discovery directly from NED’s rich data synthesis. Enhancements to the user interface, including new APIs, VO protocols, and queries involving derived physical quantities, are opening new pathways for panchromatic studies of large galaxy samples. Examples are shown of graphics characterizing the content of NED, as well as initial steps in exploring the database via interactive statistical visualizations.

To understand mass loss history and mass loss and dust formation history of massive AGB stars, we carried out observations of three bipolar planetary nebulae (BPNe) in 30 micron bands from a ground-based telescope. All of our targets have a compact strong emission source in the mid-infrared (MIR) around the position of the central star of planetary nebula (CSPN). These detected emissions can be originated from cool dust. Our results show that the cool dust component is compactly distributed and much more massive than previous studies indicated. These findings suggest that they experienced a strong mass loss into the equatorial direction in past.

We explore photometric redshift estimation of quasars with the SDSS DR12 quasar sample. Firstly the quasar sample is separated into three parts according to different redshift ranges. Then three classifiers based on Extreme Learning Machine (ELM) are created in the three redshift ranges. Finally k-Nearest Neighbor (kNN) approach is applied on the three samples to predict photometric redshifts of quasars with multiwavelength photometric data. We compare the performance with different input patterns by ELM-KNN with that only by kNN. The experimental results show that ELM-KNN is feasible and superior to kNN (e.g. rms is 0.0751 vs. 0.2626 for SDSS sample), in other words, the ensemble method has the potential to increase regressor performance beyond the level reached by an individual regressor alone and will be a good choice when facing much more complex data.

We analysed the Spitzer 8μm and WISE 22μm images of the PN IRAS 18333-2357 using a 3D RT code. We describe its morphology with a H-poor disk and a spherical shell around it.

Some key physical processes that impact the evolution of Earth's atmosphere on time-scale from days to millennia, such as the EUV emissions, are determined by the solar magnetic field. However, observations of the solar spectral irradiance are restricted to the last few solar cycles and are subject to large uncertainties. We present a physics-based model to reconstruct short-term solar spectral irradiance (SSI) variability. The coronal magnetic field is estimated to employ the Potential Field Source Surface extrapolation (PFSS) based on observational synoptic charts and magnetic flux transport model. The emission is estimated to employ the CHIANTI atomic database 8.0. The performance of the model is compared to the emission observed by TIMED/SORCE.

This paper comments on the use Gaia in studying the internal dynamics and morphologies of Planetary Nebulae (PN). It is noted that the second and subsequent releases of Gaia data, will have significant potential in unravelling PN morphologies.

Compressive Sensing is an emerging technology for data compression and simultaneous data acquisition. This is an enabling technique for significant reduction in data bandwidth, and transmission power and hence, can greatly benefit space-flight instruments. We apply this process to detect exoplanets via gravitational microlensing. We experiment with various impact parameters that describe microlensing curves to determine the effectiveness and uncertainty caused by Compressive Sensing. Finally, we describe implications for space-flight missions.

Establishing accurate morphological measurements of galaxies in a reasonable amount of time for future big-data surveys such as EUCLID, the Large Synoptic Survey Telescope or the Wide Field Infrared Survey Telescope is a challenge. Because of its high level of abstraction with little human intervention, deep learning appears to be a promising approach. Deep learning is a rapidly growing discipline that models high-level patterns in data as complex multilayered networks. In this work we test the ability of deep convolutional networks to provide parametric properties of Hubble Space Telescope like galaxies (half-light radii, Sérsic indices, total flux etc..). We simulate a set of galaxies including point spread function and realistic noise from the CANDELS survey and try to recover the main galaxy parameters using deep-learning. We compare the results with the ones obtained with the commonly used profile fitting based software GALFIT. This way showing that with our method we obtain results at least equally good as the ones obtained with GALFIT but, once trained, with a factor 5 hundred time faster.

We present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method able to provide a reliable PDF for photometric galaxy redshifts estimated through empirical techniques. METAPHOR is a modular workflow, mainly based on the MLPQNA neural network as internal engine to derive photometric galaxy redshifts, but giving the possibility to easily replace MLPQNA with any other method to predict photo-z’s and their PDF. We present here the results about a validation test of the workflow on the galaxies from SDSS-DR9, showing also the universality of the method by replacing MLPQNA with KNN and Random Forest models. The validation test include also a comparison with the PDF’s derived from a traditional SED template fitting method (Le Phare).