A variable near-infrared counterpart to the neutron-star low-mass X-ray binary 4U 1705-440

We report the discovery of a near-infrared (nIR) counterpart to the persistent neutron-star low-mass X-ray binary 4U 1705-440, at a location consistent with its recently determined Chandra X-ray position. The nIR source is highly variable, with K_s-band magnitudes varying between 15.2 and 17.3 and additional J- and H-band observations revealing color variations. A comparison with contemporaneous X-ray monitoring observations shows that the nIR brightness correlates well with X-ray flux and X-ray spectral state. We also find possible indications for a change in the slope of the nIR/X-ray flux relation between different X-ray states. We discuss and test various proposed mechanisms for the nIR emission from neutron-star low-mass X-ray binaries and conclude that the nIR emission in 4U 1705-440 is most likely dominated by X-ray heating of the outer accretion disk and the secondary star.Comment: Accepted for publication in Ap

Multivariate NIR studies of seed-water interaction in Scots Pine Seeds (Pinus sylvestris L.)

This thesis describes seed-water interaction using near infrared (NIR) spectroscopy, multivariate regression models and Scots pine seeds. The presented research covers classification of seed viability, prediction of seed moisture content, selection of NIR wavelengths and interpretation of seed-water interaction modelled and analysed by principal component analysis, ordinary least squares (OLS), partial least squares (PLS), bi-orthogonal least squares (BPLS) and genetic algorithms. The potential of using multivariate NIR calibration models for seed classification was demonstrated using filled viable and non-viable seeds that could be separated with an accuracy of 98-99%. It was also shown that multivariate NIR calibration models gave low errors (0.7% and 1.9%) in prediction of seed moisture content for bulk seed and single seeds, respectively, using either NIR reflectance or transmittance spectroscopy. Genetic algorithms selected three to eight wavelength bands in the NIR region and these narrow bands gave about the same prediction of seed moisture content (0.6% and 1.7%) as using the whole NIR interval in the PLS regression models. The selected regions were simulated as NIR filters in OLS regression resulting in predictions of the same quality (0.7 % and 2.1%). This finding opens possibilities to apply NIR sensors in fast and simple spectrometers for the determination of seed moisture content. Near infrared (NIR) radiation interacts with overtones of vibrating bonds in polar molecules. The resulting spectra contain chemical and physical information. This offers good possibilities to measure seed-water interactions, but also to interpret processes within seeds. It is shown that seed-water interaction involves both transitions and changes mainly in covalent bonds of O-H, C-H, C=O and N-H emanating from ongoing physiological processes like seed respiration and protein metabolism. I propose that BPLS analysis that has orthonormal loadings and orthogonal scores giving the same predictions as using conventional PLS regression, should be used as a standard to harmonise the interpretation of NIR spectra