Link between the chromospheric network and magnetic structures of the corona

Recent work suggested that the traditional picture of the corona above the quiet Sun being rooted in the magnetic concentrations of the chromospheric network alone is strongly questionable. Building on that previous study we explore the impact of magnetic configurations in the photosphere and the low corona on the magnetic connectivity from the network to the corona. Observational studies of this connectivity are often utilizing magnetic field extrapolations. However, it is open to which extent such extrapolations really represent the connectivity found on the Sun, as observations are not able to resolve all fine scale magnetic structures. The present numerical experiments aim at contributing to this question. We investigated random salt-and-pepper-type distributions of kilo-Gauss internetwork flux elements carrying some $10^{15}$ to $10^{17}$ Mx, which are hardly distinguishable by current observational techniques. These photospheric distributions are then extrapolated into the corona using different sets of boundary conditions at the bottom and the top. This allows us to investigate the fraction of network flux which is connected to the corona, as well as the locations of those coronal regions which are connected to the network patches. We find that with current instrumentation one cannot really determine from observations, which regions on the quiet Sun surface, i.e. in the network and internetwork, are connected to which parts of the corona through extrapolation techniques. Future spectro-polarimetric instruments, such as with Solar B or GREGOR, will provide a higher sensitivity, and studies like the present one could help to estimate to which extent one can then pinpoint the connection from the chromosphere to the corona.Comment: 8 pages, 5 figures, acceped for publication in A&

Weak lensing cosmology with convolutional neural networks on noisy data

Weak gravitational lensing is one of the most promising cosmological probes of the late universe. Several large ongoing (DES, KiDS, HSC) and planned (LSST, EUCLID, WFIRST) astronomical surveys attempt to collect even deeper and larger scale data on weak lensing. Due to gravitational collapse, the distribution of dark matter is non-Gaussian on small scales. However, observations are typically evaluated through the two-point correlation function of galaxy shear, which does not capture non-Gaussian features of the lensing maps. Previous studies attempted to extract non-Gaussian information from weak lensing observations through several higher-order statistics such as the three-point correlation function, peak counts or Minkowski-functionals. Deep convolutional neural networks (CNN) emerged in the field of computer vision with tremendous success, and they offer a new and very promising framework to extract information from 2 or 3-dimensional astronomical data sets, confirmed by recent studies on weak lensing. We show that a CNN is able to yield significantly stricter constraints of ($\sigma_8, \Omega_m$) cosmological parameters than the power spectrum using convergence maps generated by full N-body simulations and ray-tracing, at angular scales and shape noise levels relevant for future observations. In a scenario mimicking LSST or Euclid, the CNN yields 2.4-2.8 times smaller credible contours than the power spectrum, and 3.5-4.2 times smaller at noise levels corresponding to a deep space survey such as WFIRST. We also show that at shape noise levels achievable in future space surveys the CNN yields 1.4-2.1 times smaller contours than peak counts, a higher-order statistic capable of extracting non-Gaussian information from weak lensing maps

Airborne chemical sensing with mobile robots

Airborne chemical sensing with mobile robots has been an active research areasince the beginning of the 1990s. This article presents a review of research work in this field,including gas distribution mapping, trail guidance, and the different subtasks of gas sourcelocalisation. Due to the difficulty of modelling gas distribution in a real world environmentwith currently available simulation techniques, we focus largely on experimental work and donot consider publications that are purely based on simulations

A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale

In this era of complete genomes, our knowledge of neuroanatomical circuitry remains surprisingly sparse. Such knowledge is however critical both for basic and clinical research into brain function. Here we advocate for a concerted effort to fill this gap, through systematic, experimental mapping of neural circuits at a mesoscopic scale of resolution suitable for comprehensive, brain-wide coverage, using injections of tracers or viral vectors. We detail the scientific and medical rationale and briefly review existing knowledge and experimental techniques. We define a set of desiderata, including brain-wide coverage; validated and extensible experimental techniques suitable for standardization and automation; centralized, open access data repository; compatibility with existing resources, and tractability with current informatics technology. We discuss a hypothetical but tractable plan for mouse, additional efforts for the macaque, and technique development for human. We estimate that the mouse connectivity project could be completed within five years with a comparatively modest budget.Comment: 41 page

EU accession and Poland's external trade policy

No description supplie

Voids in cosmological simulations over cosmic time

We study evolution of voids in cosmological simulations using a new method for tracing voids over cosmic time. The method is based on tracking watershed basins (contiguous regions around density minima) of well developed voids at low redshift, on a regular grid of density field. It enables us to construct a robust and continuous mapping between voids at different redshifts, from initial conditions to the present time. We discuss how the new approach eliminates strong spurious effects of numerical origin when voids evolution is traced by matching voids between successive snapshots (by analogy to halo merger trees). We apply the new method to a cosmological simulation of a standard LambdaCDM cosmological model and study evolution of basic properties of typical voids (with effective radii between 6Mpc/h and 20Mpc/h at redshift z=0) such as volumes, shapes, matter density distributions and relative alignments. The final voids at low redshifts appear to retain a significant part of the configuration acquired in initial conditions. Shapes of voids evolve in a collective way which barely modifies the overall distribution of the axial ratios. The evolution appears to have a weak impact on mutual alignments of voids implying that the present state is in large part set up by the primordial density field. We present evolution of dark matter density profiles computed on iso-density surfaces which comply with the actual shapes of voids. Unlike spherical density profiles, this approach enables us to demonstrate development of theoretically predicted bucket-like shape of the final density profiles indicating a wide flat core and a sharp transition to high-density void walls.Comment: 13 pages, 13 figures; accepted for publication in MNRA