The Large Synoptic Survey Telescope (LSST) surveys have initially been optimized to omit the inner part of the Milky Way disk/bar from deep and cadence observations. However it is now clear that the LSST will be powerful for Galactic astronomy and may play a crucial role in continuing to extend the Gaia astrometric catalog until a future satellite, either optical or IR, carries on. LSST will provide metallicities and kinematics for the bulge, and will map halo structures to as distant as 450 kpc, nearly half the distance to the Andromeda galaxy. Thanks to the unprecedented calibration effort for its photometric system, and surprisingly good astrometry (transverse velocity measurements of 0.2 mas/yr at r=21; 1 mas/yr at r=24) LSST will provide photometric abundances and distance constraints for a billion or more Milky Way stars to distances of 450 kpc, and kinematics from proper motions to ~100 kpc. Single observation depths reach ~24 in the ugrizy bands, while depths at end of mission reach ~27. Although halo structures such as streams and dwarf galaxies are initially identified by the RR Lyrae and giants, their structure will be fleshed out by the 100× more abundant dwarfs that will be detected to 100 kpc (single observation) and ~300 kpc by end of mission. More complete mapping of stream structures may constrain the mass distribution of dark matter and perhaps confirm the interaction of dark matter halos and streams. I also describe the Blanco DECam Bulge Survey, a 200 deg2 LSST pathfinder survey of the bulge in ugrizy using the Dark Energy Camera on the Blanco 4m telescope. The purpose of this article is to encourage active workers on the Milky Way and Local Volume to participate in the LSST project, in particular to urge that the Galactic Plane receive the same cadence and depth coverage as the rest of the extragalactic sky.

We use the Sloan Digital Sky Survey Data Release 12, which is the largest available white dwarf catalogue to date, to study the evolution of the kinematical properties of the population of white dwarfs of the Galactic disk. We derive masses, ages, photometric distances and radial velocities for all white dwarfs with hydrogen-rich atmospheres. For those stars for which proper motions from the USNO-B1 catalogue are available, the three-dimensional components of the velocity are obtained. This subset of the original sample comprises 20,247 stars, making it the largest sample of white dwarfs with measured three-dimensional velocities. The volume probed by our sample is large, allowing us to obtain relevant kinematical information. In particular, our sample extends from a Galactocentric radial distance RG = 7.8 to 9.3 kpc, and vertical distances from the Galactic plane ranging from Z = +0.5 to –0.5 kpc.

The longitudinal distribution of solar active regions shows non-homogeneous spatial behaviour, which is often referred to as Active Longitude (AL). Evidence for a significant statistical relationships between the AL and the longitudinal distribution of flare and coronal mass ejections (CME) occurrences is found in Gyenge et al. 2017 (ApJ, 838, 18). The present work forecasts the spatial position of AL, hence the most flare/CME capable active regions are also predictable. Our forecast method applies Autoregressive Integrated Moving Average model for the next 2 years time period. We estimated the dates when the solar flare/CME-capable longitudinal belts face towards Earth.

Young objects (e.g. OB-associations and HII regions) in the Galaxy outline a 4-armed spiral structure whereas the tangent points of arms observed in the near-infrared indicate a 2-armed pattern. The more important issue is whether the spiral potential in the Galaxy is 2- or 4-armed i.e. if all arms traced by young objects also have a significant mass perturbation associated to them. This can be tested by studying the mean radial velocity of a well defined stellar population across the spiral arms and thereby estimating the surface density change.

The current paper presents a preliminary analysis of the radial velocities of a sample of 736 early-type stars toward the Galactic center observed with FLAMES/VLT. A comparison with N-body models in a fixed spiral potential with 2 or 4 arms suggests that no significant mass is associated to the Sagittarius arm. The data are consistent with 2-armed models with pattern speeds in the range of 15-30 km s−1 kpc−1 and relative radial forces of less than 4%.

The opportunity of development the short-term forecasting technique of geoeffective solar flares is presented in this study. This technique is based on the effect of growth the fluctuations of horizontal component of geomagnetic field before the solar proton flares, that is considered as a prognostic parameter of solar proton flares.

In the current paper, we investigate topological invariants, calculated by HMI LOS magnetograms as complexity descriptors of solar magnetic fields. We compared them with the physical parameters provided by the Space-weather HMI Active Region Patches (SHARP). We have repeated forecasting experiment of Stanford Solar Observatories Group with the same positive and negative active region patches database, but replace SHARP parameters with topological invariants of corresponding LOS magnetograms. The classification results turned out practically identical to those obtained by the Stanford Solar Observatory group. This means that using LOS magnetograms retains enough complexity for magnetic field description.

Sophisticated instrumentation dedicated to studying and monitoring our Sun’s activity has proliferated in the past few decades, together with the increasing demand of specialized space weather forecasts that address the needs of commercial and government systems. As a result, theoretical and empirical models and techniques of increasing complexity have been developed, aimed at forecasting the occurrence of solar disturbances, their evolution, and time of arrival to Earth. Here we will review groundbreaking and recent methods to predict the propagation and evolution of coronal mass ejections and their driven shocks. The methods rely on a wealth of data sets provided by ground- and space-based observatories, involving remote-sensing observations of the corona and the heliosphere, as well as detections of radio waves.

The thick disc is a major component of the Milky Way but its epoch of formation and characteristics are still not yet well constrained. The Besançon Galaxy Model (BGM, Robin et al. 2003) is a population synthesis model based on a scenario of formation and evolution of the Galaxy, a star formation history, and a set of stellar evolution models. Thanks to Lagarde et al. (2017), new evolutionary tracks have been introduced into the Besancon Galaxy Model (STAREVOL, Lagarde et al. 2012) to provide global asteroseismic and surface chemical properties along the evolutionary stages. This updated Galaxy model will allow us to constrain the thick disc structure and history using the Markov Chain Monte Carlo fitting method (MCMC). We show preliminary results applying this MCMC method on the 2MASS photometric survey.

We numerically investigate the interaction between a nanosatllite CubeSat and surrounding plasma. The present study aims to elucidate particular issues related to nanosatllite-plasma interaction which affects the on-board instruments by using particle-in-cell simulations. The numerical results of the present study demonstrate the importance of several key physical processes in nanosatellites-plasma interaction. In particular, it is shown that plasma flow, ion composition, and the geomagnetic field have a strong impact on the Langmuir probes.

Solar hard X-ray and gamma-ray emission was measured by BDRG instrument, the part of set of instruments operated on board the Russian satellite Lomonosov from April 2016 until now (solar-synchronous orbit with altitude 490 km, inclination of 97.6 degrees). Lomonosov measurements (11 flares with the X-ray energy more than 10 keV, and more than half of them have class in soft X-rays less than C2) were compared to the data obtained by RHESSI and Fermi space observatories as well as the Nobeyama Radioheliograph operating at the same time. The quasi-periodicity with different periods were found in some of them.

The potential-field source-surface (PFSS) model of the solar corona is a widely used tool in the space weather research and operations. In particular, the PFSS model is used in solar wind forecast models which empirically associate solar wind properties with the numerically derived coronal magnetic field. In the PFSS model, the spherical surface where magnetic field lines are forced to open is typically placed at 2.5 solar radii. However, the results presented here suggest that setting this surface (the source-surface) to lower heights can provide a better agreement between observed and modelled coronal holes during the current solar cycle. Furthermore, the lower heights of the source-surface provide a better match between observed and forecasted solar wind speed.

High-precision abundances of elements have been derived from HARPS-N spectra of F and G main-sequence stars having ages determined from oscillation frequencies delivered by the Kepler mission. The tight relations between abundance ratios of refractory elements, e.g., [Mg/Fe] and [Y/Mg], and stellar age previously found for solar twin stars are confirmed. These relations provide new information on nucleosynthesis and Galactic evolution. Abundance ratios between volatile and refractory elements, e.g., [C/Fe] and [O/Fe], show on the other hand a significant scatter at a given age, which may be related to planet-star interactions. This is a potential problem for chemical tagging studies.

The main aim of this work is to study the frequency of extreme Space Weather events, in particular to analyse the tails of the daily averaged electron fluxes distribution function for different channels of energy between 0.249–1.192 MeV measured at ~ 600 km of altitude with the particle detector ICARE-NG/CARMEN-1 on board argentinian polar satellite SAC-D. An extreme value theory was applied to estimate the maximum values of the electron flux in the outer radiation belt for different return levels. We found that the cumulative distribution function of the extreme electron fluxes presents a finite upper limit in (1) the core of the outer radiation belt for the lower energy channels and (2) in the inner edge of the outer radiation belt for energy channels larger than 0.653 keV. The results presented in this work are important to characterise Space Weather conditions.

Space weather processes, in general, are non-linear and time-varying. In such cases ‘data driven models’ such as Neural Network, Fuzzy Logic and Genetic Algorithm based models were proved promising to be used in parallel with the mathematical models based on first physical principles. In particular, with the recent developments in ‘big data’ systems, one of the urgent issues is the development of new signal processing techniques to extract manageable, representative data out of the ‘relevant big data’ to be employed in ‘training’, ‘testing’ and validation phases of model construction. Since 1990, under the EU Frame Work Program Actions, we have developed such models for nowcasting, forecasting, warning and also for filling the data gaps on space weather cases including prediction of orbital spacecraft parameters. In particular, some typical, illustrative examples include the forecasting of the ionospheric critical frequencies foF2, during disturbed conditions, such as solar storms and extreme events; GPS total electon content(TEC); solar flare index during solar maximum and the construction of solar EUV flux variations. The associated input data organisation and the typical errors which have been within the acceptable operational expectations are summarised in terms of absolute values, percent and RMS. The aim of the paper is to show that the data driven approaches are promising for the forecasting of space weather.

Open clusters are important objects to study the galactic structure and its dynamical behavior as well as the stellar formation and evolution. We carried out a spectroscopic analysis to derive atmospheric parameters and chemical composition for 52 giant stars within 9 galactic open clusters. We have used the high-resolution spectra from FEROS, HARPS and UVES in the ESO archive. The methodology used is based on LTE-hypothesis. Abundances of C, N, O, Na, Mg, Al, Si, Ca, Ti, Cr, Ni, YII, LaII, CeII, and NdII were calculated. Although most of these clusters present spectroscopic analysis in the literature, some CNO and s-process abundances were not previously estimated or were calculated with high uncertainties. Several lines of such elements were identified and used to calculate new abundances and improve some previous one.

We present a new technique to study joint observations of EUV spectral line intensities and in situ charge states of the fast solar wind. We solve the time-dependent equation for ionization and recombination for a chosen element and calculate the charge state evolution along the open magnetic fields for elements such as C, O, Ne, Mg, Si and Fe. Comparing predicted spectral lines intensities above the limb and in situ charge states to observations from SOHO/SUMER and Ulysses/SWICS, we test how well the modelled thermodynamic parameters of the solar wind reproduce observations. We outline the application of this method to Solar Orbiter data.

Very metal-poor stars ([Fe/H] < –2.0) inform our understanding of the formation and evolution of the Galaxy, and the physical conditions in the earliest star-forming environments of the Universe. They play an integral part in the paradigms of stellar populations, stellar archaeology, and near-field cosmology. We review the carbon-rich and carbon-normal sub-populations of the most iron-poor stars, providing insight into chemical enrichment at the earliest times in the Universe. We also discuss the role of very metal-poor stars in providing insight into the Galaxy’s halo, thick disk, and bulge, and the promise they hold for the future. A comparison between the abundances obtained for the nine most Fe-poor stars ([Fe/H] < –4.5) (all but one of which is C-rich) with abundances obtained from far-field cosmology suggests that the former are the most chemically primitive objects yet observed and probably older than the DLA- and sub-DLA systems for which data are currently available from far-field studies.

Magnetic activity information is concealed in the shape of stellar light curves owing to the process of rotational modulation. We developed approaches to extract magnetic activity characteristics from stellar light curves, and applied the method to a solar-type star observed with Kepler space telescope and also to the Sun for comparison. The result reveals distinct magnetic activity discrepancies between the solar-type star and the Sun. (1) The light-curve periodicity of the solar-type star is generally stronger than that of the Sun. (2) For the solar-type star, when the range of light-curve fluctuation is larger, the periodicity is also higher; while for the Sun, only during the solar minima with minimal range of fluctuation, the light curves show some periodicity. We propose that on the solar-type star, it is the large-scale magnetic field that leads to the light curves with both high periodicity and large range of fluctuation.

Dedicated wide-field surveys have uncovered a variety of debris features and stellar streams in the halos of the Milky Way and M31. I briefly compare these perspectives and discuss how observations of the peripheral regions of M31 can help shape our current understanding of the Milky Way. Much complexity resides in the outer halos of both systems in terms of overlapping structures, and I conclude by briefly highlighting some ongoing work to characterise a narrow tidal stream in the vicinity of the ultra-faint satellite Segue 1.

We developed a flare prediction model based on the supervised machine learning of solar observation data for 2010-2015. We used vector magnetograms, lower chromospheric brightening, and soft-X-ray data taken by Solar Dynamics Observatory and Geostationary Operational Environmental Satellite. We detected active regions and extracted 60 solar features such as magnetic neutral lines, current helicity, chromospheric brightening, and flare history. We fully shuffled the database and randomly divided it into two for training and testing. To predict the maximum size of flares occurring in the following 24 hours, we used three machine-learning algorithms independently: the support vector machine, the k nearest neighbors (kNN), and the extremely randomized trees. We achieved a skill score (TSS) of greater than 0.9 for kNN. Furthermore, we compared the prediction results in a more operational setting by shuffling and dividing the database with a week unit. It was found that the prediction score depends on the way the database is prepared.