The Luminosity Dependence of Quasar Clustering

We investigate the luminosity dependence of quasar clustering, inspired by numerical simulations of galaxy mergers that incorporate black hole growth. These simulations have motivated a new interpretation of the quasar luminosity function. In this picture, the bright end of the quasar luminosity function consists of quasars radiating nearly at their peak luminosities, while the faint end consists mainly of very similar sources, but at dimmer phases in their evolution. We combine this model with the statistics of dark matter halos that host quasar activity. We find that, since bright and faint quasars are mostly similar sources seen in different evolutionary stages, a broad range in quasar luminosities corresponds to only a narrow range in the masses of quasar host halos. On average, bright and faint quasars reside in similar host halos. Consequently, we argue that quasar clustering should depend only weakly on luminosity. This prediction is in qualitative agreement with recent measurements of the luminosity dependence of the quasar correlation function (Croom et al. 2005) and the galaxy-quasar cross-correlation function (Adelberger & Steidel 2005). Future precision clustering measurements from SDSS and 2dF, spanning a large range in luminosity, should provide a strong test of our model.Comment: 9 pages, 4 figures, submitted to Ap

Studying light propagation in a locally homogeneous universe through an extended Dyer-Roeder approach

Light is affected by local inhomogeneities in its propagation, which may alter distances and so cosmological parameter estimation. In the era of precision cosmology, the presence of inhomogeneities may induce systematic errors if not properly accounted. In this vein, a new interpretation of the conventional Dyer-Roeder (DR) approach by allowing light received from distant sources to travel in regions denser than average is proposed. It is argued that the existence of a distribution of small and moderate cosmic voids (or "black regions") implies that its matter content was redistributed to the homogeneous and clustered matter components with the former becoming denser than the cosmic average in the absence of voids. Phenomenologically, this means that the DR smoothness parameter (denoted here by $\alpha_E$) can be greater than unity, and, therefore, all previous analyses constraining it should be rediscussed with a free upper limit. Accordingly, by performing a statistical analysis involving 557 type Ia supernovae (SNe Ia) from Union2 compilation data in a flat $\Lambda$CDM model we obtain for the extended parameter, $\alpha_E=1.26^{+0.68}_{-0.54}$ ($1\sigma$). The effects of $\alpha_E$ are also analyzed for generic $\Lambda$CDM models and flat XCDM cosmologies. For both models, we find that a value of $\alpha_E$ greater than unity is able to harmonize SNe Ia and cosmic microwave background observations thereby alleviating the well-known tension between low and high redshift data. Finally, a simple toy model based on the existence of cosmic voids is proposed in order to justify why $\alpha_E$ can be greater than unity as required by supernovae data.Comment: 5 pages, 2 figures. Title modified, results unchanged. It matches version published as a Brief Report in Phys. Rev.

Measurement of Vegetation and Terrain Characteristics On Small Scale Vertical Aerial Photographs

Few events in recent years have stirred public imagination and interest to the degree occasioned by the uses made of aerial photographs in the Cuban affair. The average earth scientist, however, was not taken by surprise since the basic methods, materials and principles involved were not new to him. As a matter of fact, since World War II, aerial photography has become an everyday, virtually indispensable tool to most earth feature and natural resource analysts. In order better to understand the application of aerial photographs to such civil pursuits, it would be well at this point to differentiate the two basic levels of use: (1). Photogrammetry involves use of highly precise measurements and complicated instrument systems. Among the products of photogrammetry, to list a very few, are highway design, topographic maps, bridge and dam site surveys. (2). Photo Interpretation involves the extraction of both subjective information and the performance of measurements at a lower level of precision than that essential to the photogrammetrist. Photo interpretation work is usually done by the skilled professional ( e.g., archeologist, forester, geographer, geologist) who utilizes this information to formulate decisions pertinent to his professional activity. Admittedly, this a gross over-simplification of the distinction between the two levels of activity and precision since there is a certain degree of overlap between the two . As a matter of fact, there are some individuals who are fully qualified to perform both functions. Nevertheless, these basic categories must be recognized in order to indicate to the average subject matter specialist the photo interpretation applications which are available to him directly. Although the photo interpretation process is basically subjective, both in nature and by definition, a useful degree of quantification is possible. Crude though these measurements and controlled estimates may appear to be to the photogrammetric engineer, they are still suitable and often fully adequate for the purposes of the interpreter. It is doubtful, however, whether these techniques are being put to sufficient use since it is generally estimated that not more than 60% of the useful capabilities of currently-available photo interpretation systems are being realized. It is the purpose of this paper, therefore, to briefly describe some of these measurement techniques, give a few examples in current use and suggest some possibilities for the future

Polarimetry of Water Ice Particles Providing Insights on Grain Size and Degree of Sintering on Icy Planetary Surfaces

The polarimetry of the light scattered by planetary surfaces is a powerful tool to provide constraints on their microstructure. To improve the interpretation of polarimetric data from icy surfaces, we have developed the POLarimeter for ICE Samples (POLICES) complementing the measurement facilities of the Ice Laboratory at the University of Bern. The new setup uses a high precision Stokes polarimeter to measure the degree of polarization in the visible light scattered by surfaces at moderate phase angles (from 1.5 to 30{\deg}). We present the photometric and polarimetric phase curves measured on various surfaces made of pure water ice particles having well-controlled size and shape (spherical, crushed, frost). The results show how the amplitude and the shape of the negative polarization branch change with the particles sizes and the degree of metamorphism of the ice. We found that fresh frost formed by water condensation on cold surfaces has a phase curve characterized by resonances (Mie oscillations) indicating that frost embryos are transparent micrometer-sized particles with a narrow size distribution and spherical shape. Comparisons of these measurements with polarimetric observations of the icy satellites of the Solar System suggest that Europa is possibly covered by relatively coarser (~40-400 {\mu}m) and more sintered grains than Enceladus and Rhea, more likely covered by frost-like particles of few micrometers in average. The great sensitivity of polarization to grain size and degree of sintering makes it an ideal tool to detect hints of ongoing processes on icy planetary surfaces, such as cryovolcanism.Comment: 36 pages, 1 table, 11 figures, 2 data sets, accepted in Journal of Geophysical Research: Planet

Pavement Performance Evaluation Using Connected Vehicles

Roads deteriorate at different rates from weathering and use. Hence, transportation agencies must assess the ride quality of a facility regularly to determine its maintenance needs. Existing models to characterize ride quality produce the International Roughness Index (IRI), the prevailing summary of roughness. Nearly all state agencies use Inertial Profilers to produce the IRI. Such heavily instrumented vehicles require trained personnel for their operation and data interpretation. Resource constraints prevent the scaling of these existing methods beyond 4% of the network. This dissertation developed an alternative method to characterize ride quality that uses regular passenger vehicles. Smartphones or connected vehicles provide the onboard sensor data needed to enable the new technique. The new method provides a single index summary of ride quality for all paved and unpaved roads. The new index is directly proportional to the IRI. A new transform integrates sensor data streams from connected vehicles to produce a linear energy density representation of roughness. The ensemble average of indices from different speed ranges converges to a repeatable characterization of roughness. The currently used IRI is undefined at speeds other than 80 km/h. This constraint mischaracterizes roughness experienced at other speeds. The newly proposed transform integrates the average roughness indices from all speed ranges to produce a speed-independent characterization of ride quality. This property avoids spatial wavelength bias, which is a critical deficiency of the IRI. The new method leverages the emergence of connected vehicles to provide continuous characterizations of ride quality for the entire roadway network. This dissertation derived precision bounds of deterioration forecasting for models that could utilize the new index. The results demonstrated continuous performance improvements with additional vehicle participation. With practical traversal volumes, the achievable precision of forecast is within a few days. This work also quantified capabilities of the new transform to localize roadway anomalies that could pose travel hazards. The methods included derivations of the best sensor settings to achieve the desired performances. Several case studies validated the findings. These new techniques have the potential to save agencies millions of dollars annually by enabling predictive maintenance practices for all roadways, worldwide.Mountain Plains Consortium (MPC

Validation of Immunoassay-Based Tools for the Comprehensive Quantification of Aß40 and Aß42 Peptides in Plasma

Recent advances in neuroimaging and cerebrospinal fluid (CSF) biomarker assays have provided evidence of a long preclinical stage of Alzheimer''s disease (AD). This period is being increasingly targeted for secondary prevention trials of new therapies. In this context, the interest of a noninvasive, cost-effective amyloid-ß (Aß) blood-based test does not need to be overstated. Nevertheless, a thorough validation of these bioanalytical methods should be performed as a prerequisite for confident interpretation of clinical results. The aim of this study was to validate ELISA sandwich colorimetric ABtest40 and ABtest42 for the quantification of Aß40 and Aß42 in human plasma. The validation parameters assessed included precision, accuracy, sensitivity, specificity, recovery, and dilution linearity. ABtest40 and ABtest42 proved to be specific for their target peptide using Aß peptides with sequence similar to the target. Mean relative error in the quantification was found to be below 7.5 for both assays, with high intra-assay, inter-assay, and inter-batch precision (CV <9.0 on average). Sensitivity was assessed by determination of the limit of quantification fulfilling precision and accuracy criteria; it was established at 7.60 pg/ml and 3.60 pg/ml for ABtest40 and ABtest42, respectively. Plasma dilution linearity was demonstrated in PBS; however, dilution in a proprietary formulated buffer significantly increased the recovery of both Aß40 and Aß42 masked by matrix interactions, allowing a more comprehensive assessment of the free and total peptide levels in the plasma. In conclusion, both assays were successfully validated as tools for the quantification Aß40 and Aß42 in plasma

Deep ROC Analysis and AUC as Balanced Average Accuracy to Improve Model Selection, Understanding and Interpretation

Optimal performance is critical for decision-making tasks from medicine to autonomous driving, however common performance measures may be too general or too specific. For binary classifiers, diagnostic tests or prognosis at a timepoint, measures such as the area under the receiver operating characteristic curve, or the area under the precision recall curve, are too general because they include unrealistic decision thresholds. On the other hand, measures such as accuracy, sensitivity or the F1 score are measures at a single threshold that reflect an individual single probability or predicted risk, rather than a range of individuals or risk. We propose a method in between, deep ROC analysis, that examines groups of probabilities or predicted risks for more insightful analysis. We translate esoteric measures into familiar terms: AUC and the normalized concordant partial AUC are balanced average accuracy (a new finding); the normalized partial AUC is average sensitivity; and the normalized horizontal partial AUC is average specificity. Along with post-test measures, we provide a method that can improve model selection in some cases and provide interpretation and assurance for patients in each risk group. We demonstrate deep ROC analysis in two case studies and provide a toolkit in Python.Comment: 14 pages, 6 Figures, submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), currently under revie