Lightcurve inversion for asteroid spins and shapes

Knowledge of the physical properties of asteroids is crucial in many branches of solar-system research. Knowledge of the spin states and shapes is needed, e.g., for accurate orbit determination and to study the history and evolution of the asteroids. In my thesis, I present new methods for using photometric lightcurves of asteroids in the determination of their spin states and shapes. The convex inversion method makes use of a general polyhedron shape model and provides us at best with an unambiguous spin solution and a convex shape solution that reproduces the main features of the original shape. Deriving information about the non-convex shape features is, in principle, also possible, but usually requires a priori information about the object. Alternatively, a distribution of non-convex solutions, describing the scale of the non-convexities, is also possible to be obtained. Due to insufficient number of absolute observations and inaccurately defined asteroid phase curves, the $c/b$-ratio, i.e., the flatness of the shape model is often somewhat ill-defined. However, especially in the case of elongated objects, the flatness seems to be quite well constrained, even in the case when only relative lightcurves are available. The results prove that it is, contrary to the earlier misbelief, possible to derive shape information from the lightcurve data if a sufficiently wide range of observing geometries is covered by the observations. Along with the more accurate shape models, also the rotational states, i.e., spin vectors and rotation periods, are defined with improved accuracy. The shape solutions obtained so far reveal a population of irregular objects whose most descriptive shape characteristics, however, can be expressed with only a few parameters. Preliminary statistical analyses for the shapes suggests that there are correlations between shape and other physical properties, such as the size, rotation period and taxonomic type of the asteroids. More shape data of, especially, the smallest and largest asteroids, as well as the fast and slow rotators is called for in order to be able to study the statistics more thoroughly.

Inverse Problem for Fractional Brownian Motion with Discrete Data

The problem of recovering information from measurement data has already been studied for a long time. In the beginning, the methods were mostly empirical, but already towards the end of the sixties Backus and Gilbert started the development of mathematical methods for the interpretation of geophysical data. The problem of recovering information about a physical phenomenon from measurement data is an inverse problem. Throughout this work, the statistical inversion method is used to obtain a solution. Assuming that the measurement vector is a realization of fractional Brownian motion, the goal is to retrieve the amplitude and the Hurst parameter. We prove that under some conditions, the solution of the discretized problem coincides with the solution of the corresponding continuous problem as the number of observations tends to infinity. The measurement data is usually noisy, and we assume the data to be the sum of two vectors: the trend and the noise. Both vectors are supposed to be realizations of fractional Brownian motions, and the goal is to retrieve their parameters using the statistical inversion method. We prove a partial uniqueness of the solution. Moreover, with the support of numerical simulations, we show that in certain cases the solution is reliable and the reconstruction of the trend vector is quite accurate.

On the mechanical behavior of compacted pack ice : A theoretical and numerical investigation

Pack ice is an aggregate of ice floes drifting on the sea surface. The forces controlling the motion and deformation of pack ice are air and water drag forces, sea surface tilt, Coriolis force and the internal force due to the interaction between ice floes. In this thesis, the mechanical behavior of compacted pack ice is investigated using theoretical and numerical methods, focusing on the three basic material properties: compressive strength, yield curve and flow rule. A high-resolution three-category sea ice model is applied to investigate the sea ice dynamics in two small basins, the whole Gulf Riga and the inside Pärnu Bay, focusing on the calibration of the compressive strength for thin ice. These two basins are on the scales of 100 km and 20 km, respectively, with typical ice thickness of 10-30 cm. The model is found capable of capturing the main characteristics of the ice dynamics. The compressive strength is calibrated to be about 30 kPa, consistent with the values from most large-scale sea ice dynamic studies. In addition, the numerical study in Pärnu Bay suggests that the shear strength drops significantly when the ice-floe size markedly decreases. A characteristic inversion method is developed to probe the yield curve of compacted pack ice. The basis of this method is the relationship between the intersection angle of linear kinematic features (LKFs) in sea ice and the slope of the yield curve. A summary of the observed LKFs shows that they can be basically divided into three groups: intersecting leads, uniaxial opening leads and uniaxial pressure ridges. Based on the available observed angles, the yield curve is determined to be a curved diamond. Comparisons of this yield curve with those from other methods show that it possesses almost all the advantages identified by the other methods. A new constitutive law is proposed, where the yield curve is a diamond and the flow rule is a combination of the normal and co-axial flow rule. The non-normal co-axial flow rule is necessary for the Coulombic yield constraint. This constitutive law not only captures the main features of forming LKFs but also takes the advantage of avoiding overestimating divergence during shear deformation. Moreover, this study provides a method for observing the flow rule for pack ice during deformation.

Two ex situ fungal technologies to treat contaminated soil

Wood-degrading fungi are able to degrade a large range of recalcitrant pollutants which resemble the lignin biopolymer. This ability is attributed to the production of lignin-modifying enzymes, which are extracellular and non-specific. Despite the potential of fungi in bioremediation, there is still an understanding gap in terms of the technology. In this thesis, the feasibility of two ex situ fungal bioremediation methods to treat contaminated soil was evaluated. Treatment of polycyclic aromatic hydrocarbons (PAHs)-contaminated marsh soil was studied in a stirred slurry-phase reactor. Due to the salt content in marsh soil, fungi were screened for their halotolerance, and the white-rot fungi Lentinus tigrinus, Irpex lacteus and Bjerkandera adusta were selected for further studies. These fungi degraded 40 - 60% of a PAH mixture (phenanthrene, fluoranthene, pyrene and chrysene) in a slurry-phase reactor (100 ml) during 30 days of incubation. Thereafter, B. adusta was selected to scale-up and optimize the process in a 5 L reactor. Maximum degradation of dibenzothiophene (93%), fluoranthene (82%), pyrene (81%) and chrysene (83%) was achieved with the free mycelium inoculum of the highest initial biomass (2.2 g/l). In autoclaved soil, MnP was the most important enzyme involved in PAH degradation. In non-sterile soil, endogenous soil microbes together with B. adusta also degraded the PAHs extensively, suggesting a synergic action between soil microbes and the fungus. A fungal solid-phase cultivation method to pretreat contaminated sawmill soil with high organic matter content was developed to enhance the effectiveness of the subsequent soil combustion. In a preliminary screening of 146 fungal strains, 28 out of 52 fungi, which extensively colonized non-sterile contaminated soil, were litter-decomposing fungi. The 18 strains further selected were characterized by their production of lignin-modifying and hydrolytic enzymes, of which MnP and endo-1,4-β-glucanase were the main enzymes during cultivation on Scots pine (Pinus sylvestris) bark. Of the six fungi selected for further tests, Gymnopilus luteofolius, Phanerochaete velutina, and Stropharia rugosoannulata were the most active soil organic matter degraders. The results showed that a six-month pretreatment of sawmill soil would result in a 3.5 - 9.5% loss of organic matter, depending on the fungus applied. The pretreatment process was scaled-up for a 0.56 m3 reactor, in which perforated plastic tubes filled with S. rugosoannulata growing on pine bark were introduced into the soil. The fungal pretreatment resulted in a soil mass loss of 30.5 kg, which represents 10% of the original soil mass (308 kg). Despite the fact that Scots pine bark contains several antimicrobial compounds, it was a suitable substrate for fungal growth and promoter of the production of oxidative enzymes, as well as an excellent and cheap natural carrier of fungal mycelium. This thesis successfully developed two novel fungal ex situ bioremediation technologies and introduce new insights for their further full-scale application. Ex situ slurry-phase fungal reactors might be applied in cases when the soil has a high water content or when the contaminant bioavailability is low; for example, in wastewater treatment plants to remove pharmaceutical residues. Fungal solid-phase bioremediation is a promising remediation technology to ex situ or in situ treat contaminated soil.

Estimation of local forest attributes, utilizing two-phase sampling and auxiliary data

This thesis examines the feasibility of a forest inventory method based on two-phase sampling in estimating forest attributes at the stand or substand levels for forest management purposes. The method is based on multi-source forest inventory combining auxiliary data consisting of remote sensing imagery or other geographic information and field measurements. Auxiliary data are utilized as first-phase data for covering all inventory units. Various methods were examined for improving the accuracy of the forest estimates. Pre-processing of auxiliary data in the form of correcting the spectral properties of aerial imagery was examined (I), as was the selection of aerial image features for estimating forest attributes (II). Various spatial units were compared for extracting image features in a remote sensing aided forest inventory utilizing very high resolution imagery (III). A number of data sources were combined and different weighting procedures were tested in estimating forest attributes (IV, V). Correction of the spectral properties of aerial images proved to be a straightforward and advantageous method for improving the correlation between the image features and the measured forest attributes. Testing different image features that can be extracted from aerial photographs (and other very high resolution images) showed that the images contain a wealth of relevant information that can be extracted only by utilizing the spatial organization of the image pixel values. Furthermore, careful selection of image features for the inventory task generally gives better results than inputting all extractable features to the estimation procedure. When the spatial units for extracting very high resolution image features were examined, an approach based on image segmentation generally showed advantages compared with a traditional sample plot-based approach. Combining several data sources resulted in more accurate estimates than any of the individual data sources alone. The best combined estimate can be derived by weighting the estimates produced by the individual data sources by the inverse values of their mean square errors. Despite the fact that the plot-level estimation accuracy in two-phase sampling inventory can be improved in many ways, the accuracy of forest estimates based mainly on single-view satellite and aerial imagery is a relatively poor basis for making stand-level management decisions.

Computational studies on new chemical species in the gas-phase and the solid-state.

There is intense activity in the area of theoretical chemistry of gold. It is now possible to predict new molecular species, and more recently, solids by combining relativistic methodology with isoelectronic thinking. In this thesis we predict a series of solid sheet-type crystals for Group-11 cyanides, MCN (M=Cu, Ag, Au), and Group-2 and 12 carbides MC2 (M=Be-Ba, Zn-Hg). The idea of sheets is then extended to nanostrips which can be bent to nanorings. The bending energies and deformation frequencies can be systematized by treating these molecules as an elastic bodies. In these species Au atoms act as an 'intermolecular glue'. Further suggested molecular species are the new uncongested aurocarbons, and the neutral Au_nHg_m clusters. Many of the suggested species are expected to be stabilized by aurophilic interactions. We also estimate the MP2 basis-set limit of the aurophilicity for the model compounds [ClAuPH_3]_2 and [P(AuPH_3)_4]^+. Beside investigating the size of the basis-set applied, our research confirms that the 19-VE TZVP+2f level, used a decade ago, already produced 74 % of the present aurophilic attraction energy for the [ClAuPH_3]_2 dimer. Likewise we verify the preferred C4v structure for the [P(AuPH_3)_4]^+ cation at the MP2 level. We also perform the first calculation on model aurophilic systems using the SCS-MP2 method and compare the results to high-accuracy CCSD(T) ones. The recently obtained high-resolution microwave spectra on MCN molecules (M=Cu, Ag, Au) provide an excellent testing ground for quantum chemistry. MP2 or CCSD(T) calculations, correlating all 19 valence electrons of Au and including BSSE and SO corrections, are able to give bond lengths to 0.6 pm, or better. Our calculated vibrational frequencies are expected to be better than the currently available experimental estimates. Qualitative evidence for multiple Au-C bonding in triatomic AuCN is also found.

Computational Methods for Detecting Large-Scale Chromosome Rearrangements in SNP Data

Large-scale chromosome rearrangements such as copy number variants (CNVs) and inversions encompass a considerable proportion of the genetic variation between human individuals. In a number of cases, they have been closely linked with various inheritable diseases. Single-nucleotide polymorphisms (SNPs) are another large part of the genetic variance between individuals. They are also typically abundant and their measuring is straightforward and cheap. This thesis presents computational means of using SNPs to detect the presence of inversions and deletions, a particular variety of CNVs. Technically, the inversion-detection algorithm detects the suppressed recombination rate between inverted and non-inverted haplotype populations whereas the deletion-detection algorithm uses the EM-algorithm to estimate the haplotype frequencies of a window with and without a deletion haplotype. As a contribution to population biology, a coalescent simulator for simulating inversion polymorphisms has been developed. Coalescent simulation is a backward-in-time method of modelling population ancestry. Technically, the simulator also models multiple crossovers by using the Counting model as the chiasma interference model. Finally, this thesis includes an experimental section. The aforementioned methods were tested on synthetic data to evaluate their power and specificity. They were also applied to the HapMap Phase II and Phase III data sets, yielding a number of candidates for previously unknown inversions, deletions and also correctly detecting known such rearrangements.

Asteroid identification using statistical orbital inversion methods

An efficient and statistically robust solution for the identification of asteroids among numerous sets of astrometry is presented. In particular, numerical methods have been developed for the short-term identification of asteroids at discovery, and for the long-term identification of scarcely observed asteroids over apparitions, a task which has been lacking a robust method until now. The methods are based on the solid foundation of statistical orbital inversion properly taking into account the observational uncertainties, which allows for the detection of practically all correct identifications. Through the use of dimensionality-reduction techniques and efficient data structures, the exact methods have a loglinear, that is, O(nlog(n)), computational complexity, where n is the number of included observation sets. The methods developed are thus suitable for future large-scale surveys which anticipate a substantial increase in the astrometric data rate. Due to the discontinuous nature of asteroid astrometry, separate sets of astrometry must be linked to a common asteroid from the very first discovery detections onwards. The reason for the discontinuity in the observed positions is the rotation of the observer with the Earth as well as the motion of the asteroid and the observer about the Sun. Therefore, the aim of identification is to find a set of orbital elements that reproduce the observed positions with residuals similar to the inevitable observational uncertainty. Unless the astrometric observation sets are linked, the corresponding asteroid is eventually lost as the uncertainty of the predicted positions grows too large to allow successful follow-up. Whereas the presented identification theory and the numerical comparison algorithm are generally applicable, that is, also in fields other than astronomy (e.g., in the identification of space debris), the numerical methods developed for asteroid identification can immediately be applied to all objects on heliocentric orbits with negligible effects due to non-gravitational forces in the time frame of the analysis. The methods developed have been successfully applied to various identification problems. Simulations have shown that the methods developed are able to find virtually all correct linkages despite challenges such as numerous scarce observation sets, astrometric uncertainty, numerous objects confined to a limited region on the celestial sphere, long linking intervals, and substantial parallaxes. Tens of previously unknown main-belt asteroids have been identified with the short-term method in a preliminary study to locate asteroids among numerous unidentified sets of single-night astrometry of moving objects, and scarce astrometry obtained nearly simultaneously with Earth-based and space-based telescopes has been successfully linked despite a substantial parallax. Using the long-term method, thousands of realistic 3-linkages typically spanning several apparitions have so far been found among designated observation sets each spanning less than 48 hours.

Measurement of the top quark mass at CDF using the “neutrino ϕ weighting” template method on a lepton plus isolated track sample

We present a measurement of the top quark mass with t-tbar dilepton events produced in p-pbar collisions at the Fermilab Tevatron $\sqrt{s}$=1.96 TeV and collected by the CDF II detector. A sample of 328 events with a charged electron or muon and an isolated track, corresponding to an integrated luminosity of 2.9 fb$^{-1}$, are selected as t-tbar candidates. To account for the unconstrained event kinematics, we scan over the phase space of the azimuthal angles ($\phi_{\nu_1},\phi_{\nu_2}$) of neutrinos and reconstruct the top quark mass for each $\phi_{\nu_1},\phi_{\nu_2}$ pair by minimizing a $\chi^2$ function in the t-tbar dilepton hypothesis. We assign $\chi^2$-dependent weights to the solutions in order to build a preferred mass for each event. Preferred mass distributions (templates) are built from simulated t-tbar and background events, and parameterized in order to provide continuous probability density functions. A likelihood fit to the mass distribution in data as a weighted sum of signal and background probability density functions gives a top quark mass of $165.5^{+{3.4}}_{-{3.3}}$(stat.)$\pm 3.1$(syst.) GeV/$c^2$.