New Approaches To Photometric Redshift Prediction Via Gaussian Process Regression In The Sloan Digital Sky Survey

Expanding upon the work of Way and Srivastava 2006 we demonstrate how the use of training sets of comparable size continue to make Gaussian process regression (GPR) a competitive approach to that of neural networks and other least-squares fitting methods. This is possible via new large size matrix inversion techniques developed for Gaussian processes (GPs) that do not require that the kernel matrix be sparse. This development, combined with a neural-network kernel function appears to give superior results for this problem. Our best fit results for the Sloan Digital Sky Survey (SDSS) Main Galaxy Sample using u,g,r,i,z filters gives an rms error of 0.0201 while our results for the same filters in the luminous red galaxy sample yield 0.0220. We also demonstrate that there appears to be a minimum number of training-set galaxies needed to obtain the optimal fit when using our GPR rank-reduction methods. We find that morphological information included with many photometric surveys appears, for the most part, to make the photometric redshift evaluation slightly worse rather than better. This would indicate that most morphological information simply adds noise from the GP point of view in the data used herein. In addition, we show that cross-match catalog results involving combinations of the Two Micron All Sky Survey, SDSS, and Galaxy Evolution Explorer have to be evaluated in the context of the resulting cross-match magnitude and redshift distribution. Otherwise one may be misled into overly optimistic conclusions.Comment: 32 pages, ApJ in Press, 2 new figures, 1 new table of comparison methods, updated discussion, references and typos to reflect version in Pres

Novel Methods for Predicting Photometric Redshifts from Broad Band Photometry using Virtual Sensors

We calculate photometric redshifts from the Sloan Digital Sky Survey Main Galaxy Sample, The Galaxy Evolution Explorer All Sky Survey, and The Two Micron All Sky Survey using two new training-set methods. We utilize the broad-band photometry from the three surveys alongside Sloan Digital Sky Survey measures of photometric quality and galaxy morphology. Our first training-set method draws from the theory of ensemble learning while the second employs Gaussian process regression both of which allow for the estimation of redshift along with a measure of uncertainty in the estimation. The Gaussian process models the data very effectively with small training samples of approximately 1000 points or less. These two methods are compared to a well known Artificial Neural Network training-set method and to simple linear and quadratic regression. Our results show that robust photometric redshift errors as low as 0.02 RMS can regularly be obtained. We also demonstrate the need to provide confidence bands on the error estimation made by both classes of models. Our results indicate that variations due to the optimization procedure used for almost all neural networks, combined with the variations due to the data sample, can produce models with variations in accuracy that span an order of magnitude. A key contribution of this paper is to quantify the variability in the quality of results as a function of model and training sample. We show how simply choosing the "best" model given a data set and model class can produce misleading results.Comment: 36 pages, 12 figures, ApJ in Press, modified to reflect published version and color figure

Pediatric Emergency Medicine Physicians’ Use of Point‐of‐care Ultrasound and Barriers to Implementation: A Regional Pilot Study

ObjectivesPoint‐of‐care ultrasound (POCUS) has been identified as a critical skill for pediatric emergency medicine (PEM) physicians. The purpose of this study was to profile the current status of PEM POCUS in pediatric emergency departments (EDs).MethodsAn electronic survey was distributed to PEM fellows and attending physicians at four major pediatric academic health centers. The 24‐item questionnaire covered professional demographics, POCUS experience and proficiency, and barriers to the use of POCUS in pediatric EDs. We used descriptive and inferential statistics to profile respondent’s PEM POCUS experience and proficiency and Rasch analysis to evaluate barriers to implementation.ResultsOur return rate was 92.8% (128/138). Respondents were attending physicians (68%) and fellows (28%). Most completed pediatric residencies prior to PEM fellowship (83.6%). Almost all had some form of ultrasound education (113/128, 88.3%). Approximately half (46.9%) completed a formal ultrasound curriculum. More than half (53.2%) said their ultrasound education was pediatric‐specific. Most participants (67%) rated their POCUS proficiency low (Levels 1–2), while rating proficiency in other professional competencies (procedures 52%, emergency stabilization 70%) high (Levels 4–5). There were statistically significant differences in POCUS proficiency between those with formal versus informal ultrasound education (p < 0.001) and those from pediatric versus emergency medicine residencies (p < 0.05). Participants identified both personal barriers discomfort with POCUS skills (76.7%), insufficient educational time to learn POCUS (65%), and negative impact of POCUS on efficiency (58.5%)—and institutional barriers to the use of ultrasound‐consultants will not use ultrasound findings from the ED (60%); insufficient mentoring (64.7%), and POCUS not being a departmental priority (57%).ConclusionsWhile POCUS utilization continues to grow in PEM, significant barriers to full implementation still persist. One significant barrier relates to the need for dedicated time to learn and practice POCUS to achieve sufficient levels of proficiency for use in practice.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138938/1/aet210049_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138938/2/aet210049-sup-0001-SupInfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138938/3/aet210049.pd

Components of Antineutrino Emission in Nuclear Reactor

New ${\bar{\nu}_e},e$ scattering experiments aimed for sensitive searches of the ${\nu}_e$ magnetic moment and projects to explore small mixing angle oscillations at reactors call for a better understanding of the reactor antineutrino spectrum. Here we consider six components, which contribute to the total ${\bar{\nu}_e}$ spectrum generated in nuclear reactor. They are: beta decay of the fission fragments of $^{235}$U, $^{239}$Pu, $^{238}$U and $^{241}$Pu, decay of beta-emitters produced as a result of neutron capture in $^{238}$U and also due to neutron capture in accumulated fission fragments which perturbs the spectrum. For antineutrino energies less than 3.5 MeV we tabulate evolution of ${\bar{\nu}_e}$ spectra corresponding to each of the four fissile isotopes vs fuel irradiation time and their decay after the irradiation is stopped and also estimate relevant uncertainties. Small corrections to the ILL spectra are considered.Comment: LaTex 8 pages, 2 ps figure

Differential expression of secreted factors SOSTDC1 and ADAMTS8 cause pro-fibrotic changes in linear morphoea fibroblasts

This is the peer reviewed version of the following article: Badshah, I. I., et al. "Differential expression of secreted factors SOSTDC1 and ADAMTS8 cause pro-fibrotic changes in linear morphoea fibroblasts." British Journal of Dermatology 0(ja)., which has been published in final form at https://doi.org/10.1111/bjd.17352. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived VersionsFunding: RO, IB and SB are funded by the Great Ormond Street Children's Charity. This research was supported by the NIHR Great Ormond Street Hospital Biomedical Research Centr

Two novel approaches for photometric redshift estimation based on SDSS and 2MASS databases

We investigate two training-set methods: support vector machines (SVMs) and Kernel Regression (KR) for photometric redshift estimation with the data from the Sloan Digital Sky Survey Data Release 5 and Two Micron All Sky Survey databases. We probe the performances of SVMs and KR for different input patterns. Our experiments show that the more parameters considered, the accuracy doesn't always increase, and only when appropriate parameters chosen, the accuracy can improve. Moreover for different approaches, the best input pattern is different. With different parameters as input, the optimal bandwidth is dissimilar for KR. The rms errors of photometric redshifts based on SVM and KR methods are less than 0.03 and 0.02, respectively. Finally the strengths and weaknesses of the two approaches are summarized. Compared to other methods of estimating photometric redshifts, they show their superiorities, especially KR, in terms of accuracy.Comment: accepted for publication in ChJA