11,940 research outputs found
RFID and its impacts to the hospital supply chain
A radio frequency identification device (RFID) is a type of information technology used to improve supply chain management through an enhanced visualization of products. The RFID market in the U.S healthcare industry has been projected to be approximately 100 million had already deployed RFID technology. RFID is the latest technology to reduce costs by tracking both equipment and employees. This technology can also reduce medical error, thus creating a safer environment for patients
Swift UVOT grism observations of nearby Type Ia supernovae – II. Probing the progenitor metallicity of SNe Ia with ultraviolet spectra
Ultraviolet (UV) observations of Type Ia supernovae (SNe Ia) are crucial for constraining
the properties of their progenitor systems. Theoretical studies predicted that the UV spectra,
which probe the outermost layers of an SN, should be sensitive to the metal content of the
progenitor. Using the largest SN Ia UV (λ < 2900 Å) spectroscopic sample obtained from
Neil Gehrels Swift Observatory, we investigate the dependence of UV spectra on metallicity.
For the first time, our results reveal a correlation (∼2σ) between SN Ia UV flux and hostgalaxy metallicities, with SNe in more metal-rich galaxies (which are likely to have higher
progenitor metallicities) having lower UV flux level. We find that this metallicity effect is
only significant at short wavelengths (λ 2700 Å), which agrees well with the theoretical
predictions. We produce UV spectral templates for SNe Ia at peak brightness. With our sample,
we could disentangle the effect of light-curve shape and metallicity on the UV spectra. We
also examine the correlation between the UV spectra and SN luminosities as parametrized by
Hubble residuals. However, we do not see a significant trend with Hubble residuals. This is
probably due to the large uncertainties in SN distances, as the majority of our sample members
are extremely nearby (redshift z 0.01). Future work with SNe discovered in the Hubble flow
will be necessary to constrain a potential metallicity bias on SN Ia cosmology
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Type Ia Supernovae Are Excellent Standard Candles in the Near-infrared
Abstract
We analyze a set of 89 type Ia supernovae (SNe Ia) that have both optical and near-infrared (NIR) photometry to derive distances and construct low-redshift (z ≤ 0.04) Hubble diagrams. We construct mean light curve (LC) templates using a hierarchical Bayesian model. We explore both Gaussian process (GP) and template methods for fitting the LCs and estimating distances, while including peculiar-velocity and photometric uncertainties. For the 56 SNe Ia with both optical and NIR observations near maximum light, the GP method yields a NIR-only Hubble-diagram with a root mean square (rms) of
mag when referenced to the NIR maxima. For each NIR band, a comparable GP method rms is obtained when referencing to NIR-max or B-max. Using NIR LC templates referenced to B-max yields a larger rms value of
mag. Fitting the corresponding optical data using standard LC fitters that use LC shape and color corrections yields larger rms values of 0.179 ± 0.018 mag with SALT2 and
mag with SNooPy. Applying our GP method to subsets of SNe Ia NIR LCs at NIR maximum light, even without corrections for LC shape, color, or host-galaxy dust reddening, provides smaller rms in the inferred distances, at the ∼2.3–4.1σ level, than standard optical methods that correct for those effects. Our ongoing RAISIN program on the Hubble Space Telescope will exploit this promising infrared approach to limit systematic errors when measuring the expansion history of the universe in order to constrain dark energy.</jats:p
Photometric Redshifts for Cosmology: Improving Accuracy and Uncertainty Estimates Using Bayesian Neural Networks
We present results exploring the role that probabilistic deep learning models
play in cosmology from large-scale astronomical surveys through photometric
redshift (photo-z) estimation. Photo-z uncertainty estimates are critical for
the science goals of upcoming large-scale surveys such as LSST, however common
machine learning methods typically provide only point estimates and lack
uncertainties on predictions. We turn to Bayesian neural networks (BNNs) as a
promising way to provide accurate predictions of redshift values with
uncertainty estimates. We have compiled a new galaxy training data set from the
Hyper Suprime-Cam Survey with grizy photometry, which is designed to be a
smaller scale version of large surveys like LSST. We use this data set to
investigate the performance of a neural network (NN) and a probabilistic BNN
for photo-z estimation and evaluate their performance with respect to LSST
photo-z science requirements. We also examine the utility of photo-z
uncertainties as a means to reduce catastrophic outlier estimates. The BNN
model outputs the estimate in the form of a Gaussian probability distribution.
We use the mean and standard deviation as the redshift estimate and
uncertainty, respectively. We find that the BNN can produce accurate
uncertainties. Using a coverage test, we find excellent agreement with
expectation -- 67.2% of galaxies between 0 < 2.5 have 1- uncertainties
that cover the spectroscopic value. We find the BNN meets 2/3 of the LSST
photo-z science requirements in the range 0 < z < 2.5 and generally outperforms
the alternative photo-z methods considered here on the same data.Comment: 14 pages, 10 figures, 3 table
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