Type Ia supernova Hubble diagram with near-infrared and optical observations

We main goal of this paper is to test whether the NIR peak magnitudes of SNe Ia could be accurately estimated with only a single observation obtained close to maximum light, provided the time of B band maximum and the optical stretch parameter are known. We obtained multi-epoch UBVRI and single-epoch J and H photometric observations of 16 SNe Ia in the redshift range z=0.037-0.183, doubling the leverage of the current SN Ia NIR Hubble diagram and the number of SNe beyond redshift 0.04. This sample was analyzed together with 102 NIR and 458 optical light curves (LCs) of normal SNe Ia from the literature. The analysis of 45 well-sampled NIR LCs shows that a single template accurately describes them if its time axis is stretched with the optical stretch parameter. This allows us to estimate the NIR peak magnitudes even with one observation obtained within 10 days from B-band maximum. We find that the NIR Hubble residuals show weak correlation with DM_15 and E(B-V), and for the first time we report a possible dependence on the J_max-H_max color. The intrinsic NIR luminosity scatter of SNe Ia is estimated to be around 0.10 mag, which is smaller than what can be derived for a similarly heterogeneous sample at optical wavelengths. In conclusion, we find that SNe Ia are at least as good standard candles in the NIR as in the optical. We showed that it is feasible to extended the NIR SN Ia Hubble diagram to z=0.2 with very modest sampling of the NIR LCs, if complemented by well-sampled optical LCs. Our results suggest that the most efficient way to extend the NIR Hubble diagram to high redshift would be to obtain a single observation close to the NIR maximum. (abridged)Comment: 39 pages, 15 figures, accepted by A&

Carnegie Supernova Project-II: The Near-infrared Spectroscopy Program

Shifting the focus of Type Ia supernova (SN Ia) cosmology to the near-infrared (NIR) is a promising way to significantly reduce the systematic errors, as the strategy minimizes our reliance on the empirical width-luminosity relation and uncertain dust laws. Observations in the NIR are also crucial for our understanding of the origins and evolution of these events, further improving their cosmological utility. Any future experiments in the rest-frame NIR will require knowledge of the SN Ia NIR spectroscopic diversity, which is currently based on a small sample of observed spectra. Along with the accompanying paper, Phillips et al. (2018), we introduce the Carnegie Supernova Project-II (CSP-II), to follow up nearby SNe Ia in both the optical and the NIR. In particular, this paper focuses on the CSP-II NIR spectroscopy program, describing the survey strategy, instrumental setups, data reduction, sample characteristics, and future analyses on the data set. In collaboration with the Harvard-Smithsonian Center for Astrophysics (CfA) Supernova Group, we obtained 661 NIR spectra of 157 SNe Ia. Within this sample, 451 NIR spectra of 90 SNe Ia have corresponding CSP-II follow-up light curves. Such a sample will allow detailed studies of the NIR spectroscopic properties of SNe Ia, providing a different perspective on the properties of the unburned material, radioactive and stable nickel produced, progenitor magnetic fields, and searches for possible signatures of companion stars.Comment: 20 pages, 7 figures, accepted for publication in PAS

Multivariate NIR studies of seed-water interaction in Scots Pine Seeds (Pinus sylvestris L.)

This thesis describes seed-water interaction using near infrared (NIR) spectroscopy, multivariate regression models and Scots pine seeds. The presented research covers classification of seed viability, prediction of seed moisture content, selection of NIR wavelengths and interpretation of seed-water interaction modelled and analysed by principal component analysis, ordinary least squares (OLS), partial least squares (PLS), bi-orthogonal least squares (BPLS) and genetic algorithms. The potential of using multivariate NIR calibration models for seed classification was demonstrated using filled viable and non-viable seeds that could be separated with an accuracy of 98-99%. It was also shown that multivariate NIR calibration models gave low errors (0.7% and 1.9%) in prediction of seed moisture content for bulk seed and single seeds, respectively, using either NIR reflectance or transmittance spectroscopy. Genetic algorithms selected three to eight wavelength bands in the NIR region and these narrow bands gave about the same prediction of seed moisture content (0.6% and 1.7%) as using the whole NIR interval in the PLS regression models. The selected regions were simulated as NIR filters in OLS regression resulting in predictions of the same quality (0.7 % and 2.1%). This finding opens possibilities to apply NIR sensors in fast and simple spectrometers for the determination of seed moisture content. Near infrared (NIR) radiation interacts with overtones of vibrating bonds in polar molecules. The resulting spectra contain chemical and physical information. This offers good possibilities to measure seed-water interactions, but also to interpret processes within seeds. It is shown that seed-water interaction involves both transitions and changes mainly in covalent bonds of O-H, C-H, C=O and N-H emanating from ongoing physiological processes like seed respiration and protein metabolism. I propose that BPLS analysis that has orthonormal loadings and orthogonal scores giving the same predictions as using conventional PLS regression, should be used as a standard to harmonise the interpretation of NIR spectra

NIR Calibrations for Soybean Seeds and Soy Food Composition Analysis: Total Carbohydrates, Oil, Proteins and Water Contents [v.2]

Conventional chemical analysis techniques are expensive, time consuming, and often destructive. The non-invasive Near Infrared (NIR) technology was introduced over the last decades for wide-scale, inexpensive chemical analysis of food and crop seed composition (see Williams and Norris, 1987; Wilcox and Cavins, 1995; Buning and Diller, 2000 for reviews of the NIR technique development stage prior to 1998, when Diode Arrays were introduced to NIR). NIR spectroscopic measurements obey Lambert and Beer’s law, and quantitative measurements can be successfully made with high speed and ease of operation. NIR has been used in a great variety of food applications. General applications of products analyzed come from all sectors of the food industry including meats, grains, and dairy products (Shadow, 1998).
Novel NIR calibrations for rapid, reliable and accurate composition analysis of a variety of several soy based foods and bulk soybean seeds were developed and validated in a six-year collaborative project with a large number of different samples (N >~12, 000). The availability of such calibrations is important for establishing NIR as a secondary method for composition analysis of foods and soybeans both in applications and fundamental research

Determination of Soybean Oil, Protein and Amino Acid Residues in Soybean Seeds by High Resolution Nuclear Magnetic Resonance (NMRS) and Near Infrared (NIRS)

A detailed account is presented of our high resolution nuclear magnetic resonance (HR-NMR) and near infrared (NIR) calibration models, methodologies and validation procedures, together with a large number of composition analyses for soybean seeds. NIR calibrations were developed based on both HR-NMR and analytical chemistry reference data for oil and twelve amino acid residues in mature soybeans and soybean embryos. This is our first report of HR-NMR determinations of amino acid profiles of proteins from whole soybean seeds, without protein extraction from the seed. It was found that the best results for both oil and protein calibrations were obtained with a Partial Least Squares Regression (PLS-1) analysis of our extensive NIR spectral data, acquired with either a DA7000 Dual Diode Array (Si and InGaAs detectors) instrument or with several Fourier Transform NIR (FT-NIR) spectrometers equipped with an integrating sphere/InGaAs detector accessory. In order to extend the bulk soybean samples calibration models to the analysis of single soybean seeds, we have analized in detail the component NIR spectra of all major soybean constituents through spectral deconvolutions for bulk, single and powdered soybean seeds. Baseline variations and light scattering effects in the NIR spectra were corrected, respectively, by calculating the first-order derivatives of the spectra and the Multiplicative Scattering Correction (MSC). The single soybean seed NIR spectra are broadly similar to those of bulk whole soybeans, with the exception of minor peaks in single soybean NIR spectra in the region from 950 to 1,000 nm. Based on previous experience with bulk soybean NIR calibrations, the PLS-1 calibration model was selected for protein, oil and moisture calibrations that we developed for single soybean seed analysis. In order to improve the reliability and robustness of our calibrations with the PLS-1 model we employed standard samples with a wide range of soybean constituent compositions: from 34% to 55% for protein, from 11% to 22% for oil and from 2% to 16% for moisture. Such calibrations are characterized by low standard errors and high degrees of correlation for all major soybean constituents. Morever, we obtained highly resolved NIR chemical images for selected regions of mature soybean embryos that allow for the quantitation of oil and protein components. Recent developments in high-resolution FT-NIR microspectroscopy extend the NIR sensitivity range to the picogram level, with submicron spatial resolution in the component distribution throughout intact soybean seeds and embryos. Such developments are potentially important for biotechnology applications that require rapid and ultra- sensitive analyses, such as those concerned with high-content microarrays in Genomics and Proteomics research. Other important applications of FT-NIR microspectroscopy are envisaged in biomedical research aimed at cancer prevention, the early detection of tumors by NIR-fluorescence, and identification of single cancer cells, or single virus particles in vivo by super-resolution microscopy/ microspectroscopy

Voltage-controlled wavelength conversion by terahertz electro-optic modulation in double quantum wells

An undoped double quantum well (DQW) was driven with a terahertz (THz) electric field of frequency \omega_{THz} polarized in the growth direction, while simultaneously illuminated with a near-infrared (NIR) laser at frequency \omega_{NIR}. The intensity of NIR upconverted sidebands \omega_{sideband}=\omega_{NIR} + \omega_{THz} was maximized when a dc voltage applied in the growth direction tuned the excitonic states into resonance with both the THz and NIR fields. There was no detectable upconversion far from resonance. The results demonstrate the possibility of using gated DQW devices for all-optical wavelength shifting between optical communication channels separated by up to a few THz.Comment: 3 pages, 6 figures. Figures 5 and 6 are JPEG files, figures/fig5.jpg and fig6.jp

Deep Near-Infrared Observations and Identifications of Chandra Sources in the Orion Molecular Cloud 2 and 3

We conducted deep NIR imaging observations of the Orion molecular cloud 2 and 3 using QUIRC on the 88-inch telescope of the University of Hawaii. Our purposes are 1) to generate a comprehensive NIR source catalog of these star forming clouds, and 2) to identify the NIR counterpart of the Chandra X-ray sources that have no counterpart in the 2MASS catalog. Our J-, H-, and K-band observations are about 2 mag deeper than those of 2MASS, and well match the current Chandra observation. We detected 1448 NIR sources, for which we derived the position, the J-, H-, and K-band magnitude, and the 2MASS counterpart. Using this catalog, we identified the NIR counterpart for about 42% of the 2MASS-unIDed Chandra sources. The nature of these Chandra sources are discussed using their NIR colors and spatial distributions, and a dozen protostar and brown dwarf candidates are identified.Comment: 39 pages, 9 postscript figures, accepted for publication in A

Near Infrared Microspectroscopy, Fluorescence Microspectroscopy, Infrared Chemical Imaging and High-Resolution Nuclear Magnetic Resonance Analysis of Soybean Seeds, Embryos and Single Cells

Chemical analysis of soybean seeds, somatic embryos and single cells were carried out by Fourier Transform Infrared (FT-IR), Fourier Transform Near Infrared (FT-NIR) Microspectroscopy, Fluorescence and High-Resolution NMR (HR-NMR). The first FT-NIR chemical images of biological systems approaching 1 micron (1μ) resolution are presented here. Chemical images obtained by FT-NIR and FT-IR Microspectroscopy are presented for oil in soybean seeds and somatic embryos under physiological conditions. FT-NIR spectra of oil and proteins were obtained for volumes as small as 2μ3. Related, HR-NMR analyses of oil contents in somatic embryos are also presented here with nanoliter precision. Such 400 MHz 1H NMR analyses allowed the selection of mutagenized embryos with higher oil content (e.g. ~20%) compared to non-mutagenized control embryos. Moreover, developmental changes in single soybean seeds and/or somatic embryos may be monitored by FT-NIR with a precision approaching the picogram level. Indeed, detailed chemical analyses of oils and phytochemicals are now becoming possible by FT-NIR Chemical Imaging/ Microspectroscopy of single cells. The cost, speed and analytical requirements of plant breeding and genetic selection programs are fully satisfied by FT-NIR spectroscopy and Microspectroscopy for soybeans and soybean embryos. FT-NIR Microspectroscopy and Chemical Imaging are also shown to be potentially important in functional Genomics and Proteomics research through the rapid and accurate detection of high-content microarrays (HCMA). Multi-photon (MP), pulsed femtosecond laser NIR Fluorescence Excitation techniques were shown to be capable of Single Molecule Detection (SMD). Therefore, such powerful techniques allow for the most sensitive and reliable quantitative analyses to be carried out both in vitro and in vivo. Thus, MP NIR excitation for Fluorescence Correlation Spectroscopy (FCS) allows not only single molecule detection, but also molecular dynamics and high resolution, submicron imaging of femtoliter volumes inside living cells and tissues. These novel, ultra-sensitive and rapid NIR/FCS analyses have numerous applications in important research areas, such as: agricultural biotechnology, food safety, pharmacology, medical research and clinical diagnosis of viral diseases and cancers