The dust-eliminated shape of quasar spectra in the near-infrared: a hidden part of the big blue bump

The near-infrared shape of the big blue bump component in quasar spectra has been essentially unknown. It usually cannot be observed directly, due to the strong hot dust emission which dominates quasar spectra longward of ~1micron. However this is quite an important part of the spectrum theoretically. At least bare disk models provide quite a robust prediction for the overall continuum shape in the near-infrared. Self-gravity should become important in the outer, near-infrared emitting regions of the putative disk, possibly leaving a signature of disk truncation in the near-infrared. We propose here that this important part of the spectrum can be revealed for the first time by observing polarized flux from normal quasars. At least in some polarized quasars, the emission lines are all unpolarized and so the polarized flux should originate interior to the broad line region, and therefore also interior to the dust emitting region. This can then be used to eliminate the dust emission. We present the results of near-infrared polarimetry for such three quasars (Ton202, 4C37.43, B2 1208+32). The data for Ton202 have the highest S/N, and the near-infrared polarized flux in this case is measured to have quite a blue shape, nu^+0.42+-0.29 in F_nu, intriguingly consistent with the simple multi-temperature black body, bare disk prediction of nu^+1/3. All these data, although still with quite low S/N for the other two objects, demonstrate the unique potential of the technique with future better data. We also present similar data for other quasars and radio galaxies, and briefly discuss the nature of the polarization.Comment: MNRAS in pres

RANdom SAmple Consensus (RANSAC) algorithm for material-informatics: application to photovoltaic solar cells

Abstract An important aspect of chemoinformatics and material-informatics is the usage of machine learning algorithms to build Quantitative Structure Activity Relationship (QSAR) models. The RANdom SAmple Consensus (RANSAC) algorithm is a predictive modeling tool widely used in the image processing field for cleaning datasets from noise. RANSAC could be used as a “one stop shop” algorithm for developing and validating QSAR models, performing outlier removal, descriptors selection, model development and predictions for test set samples using applicability domain. For “future” predictions (i.e., for samples not included in the original test set) RANSAC provides a statistical estimate for the probability of obtaining reliable predictions, i.e., predictions within a pre-defined number of standard deviations from the true values. In this work we describe the first application of RNASAC in material informatics, focusing on the analysis of solar cells. We demonstrate that for three datasets representing different metal oxide (MO) based solar cell libraries RANSAC-derived models select descriptors previously shown to correlate with key photovoltaic properties and lead to good predictive statistics for these properties. These models were subsequently used to predict the properties of virtual solar cells libraries highlighting interesting dependencies of PV properties on MO compositions

Toward Developing Techniques─Agnostic Machine Learning Classification Models for Forensically Relevant Glass Fragments

Glass fragments found in crime scenes may constitute important forensic evidence when properly analyzed, for example, to determine their origin. This analysis could be greatly helped by having a large and diverse database of glass fragments and by using it for constructing reliable machine learning (ML)-based glass classification models. Ideally, the samples that make up this database should be analyzed by a single accurate and standardized analytical technique. However, due to differences in equipment across laboratories, this is not feasible. With this in mind, in this work, we investigated if and how measurement performed at different laboratories on the same set of glass fragments could be combined in the context of ML. First, we demonstrated that elemental analysis methods such as particle-induced X-ray emission (PIXE), laser ablation induct i v e l y coupled plasma mass spectrometry (LA-ICP-MS), scanning electron microscopy with energy-dispersive X-ray spectrometry (SEM-EDS), particle-induced Gamma-ray emission (PIGE), instrumental neutron activation analysis (INAA), and prompt Gamma-ray neutron activation analysis (PGAA) could each produce lab-specific ML-based classification models. Next, we determined rules for the successf u l combinations of data from different laboratories and techniques and demonstrated that when followed, they give rise to improved models, and conversely, poor combinations wi l l lead to poor-performing models. Thus, the combination of PIXE and LA-ICP-MS improves the performances by similar to 10-15%, while combining PGAA with other techniques provides poorer performances in comparison with the lab-specific models. Finally, we demonstrated that the poor performances of the SEM-EDS technique, sti l l in use by law enforcement agencies, could be greatly improved by replacing SEM-EDS measurements for Fe and Ca by PIX E measurements for these elements. These findings suggest a process whereby forensic laboratories using different elemental analysis techniques could upload their data into a unified database and get reliable classification based on lab-agnostic models. This in tur n brings us closer to a more exhaustive extraction of information from glass fragment evidence and furthermore may form the basis for international-wide collaboration between law enforcement agencies.Peer reviewe