Plasma diagnostics of active-region evolution and implications for coronal heating

A detailed study is presented of the decaying solar-active region NOAA 10103 observed with the Coronal Diagnostic Spectrometer (CDS), the Michelson Doppler Imager (MDI) and the Extreme-ultraviolet Imaging Telescope (EIT) onboard the Solar and Heliospheric Observatory (SOHO). Electron-density maps formed using Si x (356.03 angstrom/347.41 angstrom) show that the density varies from similar to 10(10) cm(-3) in the active-region core to similar to 7 x 108 cm-3 at the region boundaries. Over the 5 d of observations, the average electron density fell by similar to 30 per cent. Temperature maps formed using Fe XVI (335.41 angstrom)/Fe XIV (334.18 angstrom) show electron temperatures of similar to 2.34 x 10(6) K in the active-region core and similar to 2.10 x 10(6) K at the region boundaries. Similarly to the electron density, there was a small decrease in the average electron temperature over the 5-d period. The radiative, conductive and mass-flow losses were calculated and used to determine the resultant heating rate (P-H). Radiative losses were found to dominate the active-region cooling process. As the region decayed, the heating rate decreased by almost a factor of 5 between the first and last day of observations. The heating rate was then compared to the total unsigned magnetic flux (Phi(tot) = integral dA vertical bar B-z vertical bar), yielding a power law of the form P-H similar to Phi(0.81 +/- 0.32)(tot) This result suggests that waves rather than nanoflares may be the dominant heating mechanism in this active region.

Compositional data-driven alternatives to single geochemical component maps

Conventional practice in Regional Geochemistry includes as a final step of any geochemical campaign the generation of a series of maps, to show the spatial distribution of each of the components considered. Such maps, though necessary, do not comply with the compositional, relative nature of the data, which unfortunately make any conclusion based on them sensitive
to spurious correlation problems. This is one of the reasons why these maps are never interpreted isolated. This contribution aims at gathering a series of statistical methods to produce individual maps of multiplicative combinations of components (logcontrasts), much in the flavor of equilibrium constants, which are designed on purpose to capture certain aspects of the data.
We distinguish between supervised and unsupervised methods, where the first require an external, non-compositional variable (besides the compositional geochemical information) available in an analogous training set. This external variable can be a quantity (soil density, collocated magnetics, collocated ratio of Th/U spectral gamma counts, proportion of clay particle fraction, etc) or a category (rock type, land use type, etc). In the supervised methods, a regression-like model between the external variable and the geochemical composition is derived in the training set, and then this model is mapped on the whole region. This case is illustrated with the Tellus dataset, covering Northern Ireland at a density of 1 soil sample per 2 square km, where we map the presence of blanket peat and the underlying geology. The unsupervised methods considered include principal components and principal balances
(Pawlowsky-Glahn et al., CoDaWork2013), i.e. logcontrasts of the data that are devised to capture very large variability or else be quasi-constant. Using the Tellus dataset again, it is found that geological features are highlighted by the quasi-constant ratios Hf/Nb and their ratio against SiO2; Rb/K2O and Zr/Na2O and the balance between these two groups of two variables; the balance of Al2O3 and TiO2 vs. MgO; or the balance of Cr, Ni and Co vs. V and Fe2O3. The largest variability appears to be related to the presence/absence of peat.