Prototype selection for parameter estimation in complex models

Parameter estimation in astrophysics often requires the use of complex physical models. In this paper we study the problem of estimating the parameters that describe star formation history (SFH) in galaxies. Here, high-dimensional spectral data from galaxies are appropriately modeled as linear combinations of physical components, called simple stellar populations (SSPs), plus some nonlinear distortions. Theoretical data for each SSP is produced for a fixed parameter vector via computer modeling. Though the parameters that define each SSP are continuous, optimizing the signal model over a large set of SSPs on a fine parameter grid is computationally infeasible and inefficient. The goal of this study is to estimate the set of parameters that describes the SFH of each galaxy. These target parameters, such as the average ages and chemical compositions of the galaxy's stellar populations, are derived from the SSP parameters and the component weights in the signal model. Here, we introduce a principled approach of choosing a small basis of SSP prototypes for SFH parameter estimation. The basic idea is to quantize the vector space and effective support of the model components. In addition to greater computational efficiency, we achieve better estimates of the SFH target parameters. In simulations, our proposed quantization method obtains a substantial improvement in estimating the target parameters over the common method of employing a parameter grid. Sparse coding techniques are not appropriate for this problem without proper constraints, while constrained sparse coding methods perform poorly for parameter estimation because their objective is signal reconstruction, not estimation of the target parameters.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS500 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Perceptions of Job Competencies and Mentoring Program Development for Extension Administrative Assistants: A Focus Group Study of Multiple Extension Employee Groups

Extension workplace mentoring programs may produce increased Extension programming competence, organizational commitment, job satisfaction (Denny, 2016), and leadership effectiveness (Kutilek & Earnest, 2001). The study described in this article aimed to gather information for a proposed mentoring program for Extension administrative assistants. A total of 12 focus groups were conducted with 78 participants representing five employee groups: administrative assistants, Extension agents, county directors, state specialists, and department heads. Employee groups were separately interviewed. Findings indicated that respondents perceive the role of an administrative assistant as critically important, and major competencies required by the administrative assistant role are people skills/customer service, bookkeeping, technology, and a willingness to learn/adaptability to change. Respondents perceived that workplace mentoring is important, and it should be required of all newly-hired administrative assistants. Regarding incentives, administrative assistants felt that counting mentoring time as part of their University’s annual professional learning requirement of 32 hours would encourage participation. Major recommendations include the establishment of an e-mentoring program that incorporates the administrative assistants’ academic, career, and personal goals in addition to organizational policies and procedures

Semi-supervised Learning for Photometric Supernova Classification

We present a semi-supervised method for photometric supernova typing. Our approach is to first use the nonlinear dimension reduction technique diffusion map to detect structure in a database of supernova light curves and subsequently employ random forest classification on a spectroscopically confirmed training set to learn a model that can predict the type of each newly observed supernova. We demonstrate that this is an effective method for supernova typing. As supernova numbers increase, our semi-supervised method efficiently utilizes this information to improve classification, a property not enjoyed by template based methods. Applied to supernova data simulated by Kessler et al. (2010b) to mimic those of the Dark Energy Survey, our methods achieve (cross-validated) 95% Type Ia purity and 87% Type Ia efficiency on the spectroscopic sample, but only 50% Type Ia purity and 50% efficiency on the photometric sample due to their spectroscopic follow-up strategy. To improve the performance on the photometric sample, we search for better spectroscopic follow-up procedures by studying the sensitivity of our machine learned supernova classification on the specific strategy used to obtain training sets. With a fixed amount of spectroscopic follow-up time, we find that deeper magnitude-limited spectroscopic surveys are better for producing training sets. For supernova Ia (II-P) typing, we obtain a 44% (1%) increase in purity to 72% (87%) and 30% (162%) increase in efficiency to 65% (84%) of the sample using a 25th (24.5th) magnitude-limited survey instead of the shallower spectroscopic sample used in the original simulations. When redshift information is available, we incorporate it into our analysis using a novel method of altering the diffusion map representation of the supernovae. Incorporating host redshifts leads to a 5% improvement in Type Ia purity and 13% improvement in Type Ia efficiency.Comment: 16 pages, 11 figures, accepted for publication in MNRA

Mid-infrared Period-Luminosity Relations of RR Lyrae Stars Derived from the WISE Preliminary Data Release

Interstellar dust presents a significant challenge to extending parallax-determined distances of optically observed pulsational variables to larger volumes. Distance ladder work at mid-infrared wavebands, where dust effects are negligible and metallicity correlations are minimized, have been largely focused on few-epoch Cepheid studies. Here we present the first determination of mid-infrared period-luminosity (PL) relations of RR Lyrae stars from phase-resolved imaging using the preliminary data release of the Wide-Field Infrared Survey Explorer (WISE). We present a novel statistical framework to predict posterior distances of 76 well-observed RR Lyrae that uses the optically constructed prior distance moduli while simultaneously imposing a power-law PL relation to WISE-determined mean magnitudes. We find that the absolute magnitude in the bluest WISE filter is M_W1 = (-0.421+-0.014) - (1.681+-0.147)*log(P/0.50118 day), with no evidence for a correlation with metallicity. Combining the results from the three bluest WISE filters, we find that a typical star in our sample has a distance measurement uncertainty of 0.97% (statistical) plus 1.17% (systematic). We do not fundamentalize the periods of RRc stars to improve their fit to the relations. Taking the Hipparcos-derived mean V-band magnitudes, we use the distance posteriors to determine a new optical metallicity-luminosity relation which we present in Section 5. The results of this analysis will soon be tested by HST parallax measurements and, eventually, with the Gaia astrometric mission.Comment: 33 pages, 12 figures, 2 tables. Accepted for publication in ApJ, June 27th, 201

Spectroscopy of Broad Line Blazars from 1LAC

We report on optical spectroscopy of 165 Flat Spectrum Radio Quasars (FSRQs) in the Fermi 1LAC sample, which have helped allow a nearly complete study of this population. Fermi FSRQ show significant evidence for non-thermal emission even in the optical; the degree depends on the gamma-ray hardness. They also have smaller virial estimates of hole mass than the optical quasar sample. This appears to be largely due to a preferred (axial) view of the gamma-ray FSRQ and non-isotropic (H/R ~ 0.4) distribution of broad-line velocities. Even after correction for this bias, the Fermi FSRQ show higher mean Eddington ratios than the optical population. A comparison of optical spectral properties with Owens Valley Radio Observatory radio flare activity shows no strong correlation.Comment: Accepted for publication in Ap

Photometric redshifts and quasar probabilities from a single, data-driven generative model

We describe a technique for simultaneously classifying and estimating the redshift of quasars. It can separate quasars from stars in arbitrary redshift ranges, estimate full posterior distribution functions for the redshift, and naturally incorporate flux uncertainties, missing data, and multi-wavelength photometry. We build models of quasars in flux-redshift space by applying the extreme deconvolution technique to estimate the underlying density. By integrating this density over redshift one can obtain quasar flux-densities in different redshift ranges. This approach allows for efficient, consistent, and fast classification and photometric redshift estimation. This is achieved by combining the speed obtained by choosing simple analytical forms as the basis of our density model with the flexibility of non-parametric models through the use of many simple components with many parameters. We show that this technique is competitive with the best photometric quasar classification techniques---which are limited to fixed, broad redshift ranges and high signal-to-noise ratio data---and with the best photometric redshift techniques when applied to broadband optical data. We demonstrate that the inclusion of UV and NIR data significantly improves photometric quasar--star separation and essentially resolves all of the redshift degeneracies for quasars inherent to the ugriz filter system, even when included data have a low signal-to-noise ratio. For quasars spectroscopically confirmed by the SDSS 84 and 97 percent of the objects with GALEX UV and UKIDSS NIR data have photometric redshifts within 0.1 and 0.3, respectively, of the spectroscopic redshift; this amounts to about a factor of three improvement over ugriz-only photometric redshifts. Our code to calculate quasar probabilities and redshift probability distributions is publicly available

New Insights into the Genus Lithophyllum (Lithophylloideae, Corallinaceae, Corallinales) from Deepwater Rhodolith Beds Offshore the NW Gulf of Mexico

Hard bank rhodolith beds at 45–80 m depth offshore Louisiana in the Northwestern Gulf of Mexico harbor a diverse community of non-geniculate coralline algae spanning multiple lineages including both rhodolith-forming (biogenic) taxa and others encrusting autogenic rhodoliths. Identifying these members of the Corallinales to the correct genus and species is an ongoing process because many available names need to be validated by comparison to historical type specimens. A phylogenetic analysis of concatenated plastid (psbA), nuclear (LSU rDNA), and mitochondrial (COI) DNA sequences of non-geniculate corallines belonging to the subfamily Lithophylloideae (Corallinaceae), including newly generated sequences from recently collected specimens dredged at Ewing and Sackett Banks following the April 2010 Deepwater Horizon oil spill, reveals at least two distinct species of Lithophyllum sensu lato for the region. Scanning Electron Microscopy confirmed the presence of vegetative characters congruent with those for both Lithophyllum and Titanoderma. Lithophyllum is a newly reported genus for the northern Gulf of Mexico. The generic boundaries within the Lithophylloideae are addressed in light of possible evolutionary progenetic heterochrony that may have occurred within this subfamil

Think Outside the Color Box: Probabilistic Target Selection and the SDSS-XDQSO Quasar Targeting Catalog

We present the SDSS-XDQSO quasar targeting catalog for efficient flux-based quasar target selection down to the faint limit of the Sloan Digital Sky Survey (SDSS) catalog, even at medium redshifts (2.5 <~ z <~ 3) where the stellar contamination is significant. We build models of the distributions of stars and quasars in flux space down to the flux limit by applying the extreme-deconvolution method to estimate the underlying density. We convolve this density with the flux uncertainties when evaluating the probability that an object is a quasar. This approach results in a targeting algorithm that is more principled, more efficient, and faster than other similar methods. We apply the algorithm to derive low-redshift (z < 2.2), medium-redshift (2.2 <= z 3.5) quasar probabilities for all 160,904,060 point sources with dereddened i-band magnitude between 17.75 and 22.45 mag in the 14,555 deg^2 of imaging from SDSS Data Release 8. The catalog can be used to define a uniformly selected and efficient low- or medium-redshift quasar survey, such as that needed for the SDSS-III's Baryon Oscillation Spectroscopic Survey project. We show that the XDQSO technique performs as well as the current best photometric quasar-selection technique at low redshift, and outperforms all other flux-based methods for selecting the medium-redshift quasars of our primary interest. We make code to reproduce the XDQSO quasar target selection publicly available