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Measurements of the dark energy equation-of-state parameter, $w$, have been limited by uncertainty in the selection effects and photometric calibration of $z0.1$ data include 875 SNe without spectroscopic classifications and we show that we can robustly marginalize over CC SN contamination. We measure Foundation Hubble residuals to be fainter than the pre-existing low-$z$ Hubble residuals by $0.046 \pm 0.027$ mag (stat+sys). By combining the SN Ia data with cosmic microwave background constraints, we find $w=-0.938 \pm 0.053$, consistent with $��$CDM. With 463 spectroscopically classified SNe Ia alone, we measure $w=-0.933\pm0.061$. Using the more homogeneous and better-characterized Foundation sample gives a 55% reduction in the systematic uncertainty attributed to SN Ia sample selection biases. Although use of just a single photometric system at low and high redshift increases the impact of photometric calibration uncertainties in this analysis, previous low-$z$ samples may have correlated calibration uncertainties that were neglected in past studies. The full Foundation sample will observe up to 800 SNe to anchor the LSST and WFIRST Hubble diagrams., 30 pages, 17 figures, accepted by ApJ

The Foundation Supernova Survey aims to provide a large, high-fidelity, homogeneous, and precisely-calibrated low-redshift Type Ia supernova (SN Ia) sample for cosmology. The calibration of the current low-redshift SN sample is the largest component of systematic uncertainties for SN cosmology, and new data are necessary to make progress. We present the motivation, survey design, observation strategy, implementation, and first results for the Foundation Supernova Survey. We are using the Pan-STARRS telescope to obtain photometry for up to 800 SNe Ia at z < 0.1. This strategy has several unique advantages: (1) the Pan-STARRS system is a superbly calibrated telescopic system, (2) Pan-STARRS has observed 3/4 of the sky in grizy making future template observations unnecessary, (3) we have a well-tested data-reduction pipeline, and (4) we have observed ~3000 high-redshift SNe Ia on this system. Here we present our initial sample of 225 SN Ia griz light curves, of which 180 pass all criteria for inclusion in a cosmological sample. The Foundation Supernova Survey already contains more cosmologically useful SNe Ia than all other published low-redshift SN Ia samples combined. We expect that the systematic uncertainties for the Foundation Supernova Sample will be 2-3 times smaller than other low-redshift samples. We find that our cosmologically useful sample has an intrinsic scatter of 0.111 mag, smaller than other low-redshift samples. We perform detailed simulations showing that simply replacing the current low-redshift SN Ia sample with an equally sized Foundation sample will improve the precision on the dark energy equation-of-state parameter by 35%, and the dark energy figure-of-merit by 72%., MNRAS, accepted; 157 pages, but 139 pages are tables; 9 figures

Recent analyses suggest that distance residuals measured from Type Ia supernovae (SNe Ia) are correlated with local host galaxy properties within a few kpc of the SN explosion. However, the well-established correlation with global host galaxy properties is nearly as significant, with a shift of 0.06 mag across a low to high mass boundary (the mass step). Here, with 273 SNe Ia at $z2\sigma$ after global mass correction, and we conservatively estimate a systematic shift in H$_0$ measurements of -0.14 $\textrm{km}\,\textrm{s}^{-1}\textrm{Mpc}^{-1}$ with an additional uncertainty of 0.14 $\textrm{km}\,\textrm{s}^{-1}\textrm{Mpc}^{-1}$, $\sim$10\% of the present uncertainty., Comment: Accepted for publication in ApJ

Scouting to determine soil water status throughout a golf course or large athletic field complex is quite time consuming and requires numerous observations to characterize variability across the site. The objective of this research was to evaluate the use of a ground-based remote sensing system to predict soil water content through partial least squares regression analysis of canopy reflectance data collected from perennial ryegrass [Lolium perenne L.) maintained at 12.7 mm and creeping bentgrass (Agrostis stolonifera L.) maintained at 6.3 mm during 2002 and 2003 on a Coland silty clay loam. Volumetric soil water at a 5 cm depth was measured by time domain reflectometry and was collected in conjunction with spectral radiance measurements obtained using a fiber optic spectrometer. Volumetric soil water content was best predicted with partial least squares regression analysis of of creeping bentgrass canopy reflectance data with a maximum r 2 of 0.64 (P < 0.001) 1 d before development of drought stress symptoms. Similar results were observed for canopy reflectance data collected from perennial ryegrass plots, indicating that this technology and method of data analysis may be useful in the development of automated turfgrass irrigation management systems.