We present the design and performance of the multi-object fiber spectrographs for the Sloan Digital Sky Survey (SDSS) and their upgrade for the Baryon Oscillation Spectroscopic Survey (BOSS). Originally commissioned in Fall 1999 on the 2.5-m aperture Sloan Telescope at Apache Point Observatory, the spectrographs produced more than 1.5 million spectra for the SDSS and SDSS-II surveys, enabling a wide variety of Galactic and extra-galactic science including the first observation of baryon acoustic oscillations in 2005. The spectrographs were upgraded in 2009 and are currently in use for BOSS, the flagship survey of the third-generation SDSS-III project. BOSS will measure redshifts of 1.35 million massive galaxies to redshift 0.7 and Lyman-α absorption of 160,000 high redshift quasars over 10,000 square degrees of sky, making percent level measurements of the absolute cosmic distance scale of the Universe and placing tight constraints on the equation of state of dark energy.The twin multi-object fiber spectrographs utilize a simple optical layout with reflective collimators, gratings, all-refractive cameras, and state-of-the-art CCD detectors to produce hundreds of spectra simultaneously in two channels over a bandpass covering the near ultraviolet to the near infrared, with a resolving power R = λ/FWHM ∼ 2000. Building on proven heritage, the spectrographs were upgraded for BOSS with volume-phase holographic gratings and modern CCD detectors, improving the peak throughput by nearly a factor of two, extending the bandpass to cover 360 < λ < 1000 nm, and increasing the number of fibers from 640 to 1000 per exposure. In this paper we describe the original SDSS spectrograph design and the upgrades implemented for BOSS, and document the predicted and measured performances.The 2.5-m SDSS telescope is a modified distortion-free Richey-Chrétien design with a 3 • diameter field of view, and f/5 final focal ratio, which provides a good match to fibers for spectroscopy (180 µm diameter, 3 ) and to the imaging CCDs (pixel size 24 µm, 0.4 ). The optical design incorporates two aspheric corrector lenses, a Gascoigne-type design located near the vertex of the primary mirror, and two interchangeable secondary correctors, one used for imaging and the other for spectroscopy. The imaging corrector is a thick fused silica lens located close to the focal plane and is incorporated into the SDSS camera, where it serves a mechanical function in addition

We present the first measurements of clustering in the Sloan Digital Sky Survey (SDSS) galaxy redshift survey. Our sample consists of 29,300 galaxies with redshifts 5700 km s À1 cz 39; 000 km s À1 , distributed in several long but narrow (2=5-5) segments, covering 690 deg 2. For the full, flux-limited sample, the redshiftspace correlation length is approximately 8 h À1 Mpc. The two-dimensional correlation function ðr p ; Þ shows clear signatures of both the small-scale, '' fingers-of-God '' distortion caused by velocity dispersions in collapsed objects and the large-scale compression caused by coherent flows, though the latter cannot be measured with high precision in the present sample. The inferred real-space correlation function is well described by a power law, ðrÞ ¼ ðr=6:1 AE 0:2 h À1 MpcÞ À1:75AE0:03 , for 0:1 h À1 Mpc r 16 h À1 Mpc. The galaxy pairwise velocity dispersion is 12 % 600 AE 100 km s À1 for projected separations 0:15 h À1 Mpc r p 5 h À1 Mpc. When we divide the sample by color, the red galaxies exhibit a stronger and steeper real-space correlation function and a higher pairwise velocity dispersion than do the blue galaxies. The relative behavior of subsamples defined by high/low profile concentration or high/low surface brightness is qualitatively similar to that of the red/blue subsamples. Our most striking result is a clear measurement of scale-independent luminosity bias at rd10 h À1 Mpc: subsamples with absolute magnitude ranges centered on M Ã À 1:5, M Ã , and M Ã þ 1:5 have real-space correlation functions that are parallel power laws of slope %À1.8 with correlation lengths of approximately 7.4, 6.3, and 4.7 h À1 Mpc, respectively.

Abstract. Since September 2014, NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite has been taking measurements of reflected solar spectra and using them to infer atmospheric carbon dioxide levels. This work provides details of the OCO-2 retrieval algorithm, versions 7 and 8, used to derive the column-averaged dry air mole fraction of atmospheric CO2 (XCO2) for the roughly 100 000 cloud-free measurements recorded by OCO-2 each day. The algorithm is based on the Atmospheric Carbon Observations from Space (ACOS) algorithm which has been applied to observations from the Greenhouse Gases Observing SATellite (GOSAT) since 2009, with modifications necessary for OCO-2. Because high accuracy, better than 0.25 %, is required in order to accurately infer carbon sources and sinks from XCO2, significant errors and regional-scale biases in the measurements must be minimized. We discuss efforts to filter out poor-quality measurements, and correct the remaining good-quality measurements to minimize regional-scale biases. Updates to the radiance calibration and retrieval forward model in version 8 have improved many aspects of the retrieved data products. The version 8 data appear to have reduced regional-scale biases overall, and demonstrate a clear improvement over the version 7 data. In particular, error variance with respect to TCCON was reduced by 20 % over land and 40 % over ocean between versions 7 and 8, and nadir and glint observations over land are now more consistent. While this paper documents the significant improvements in the ACOS algorithm, it will continue to evolve and improve as the CO2 data record continues to expand.

The photometric calibration of the Sloan Digital Sky Survey (SDSS) is a multi-step process which involves data from three different telescopes: the 1.0-m telescope at the US Naval Observatory (USNO), Flagstaff Station, Arizona (which was used to establish the SDSS standard star network); the SDSS 0.5-m Photometric Telescope (PT) at the Apache Point Observatory (APO), New Mexico (which calculates nightly extinctions and calibrates secondary patch transfer fields); and the SDSS 2.5-m telescope at APO (which obtains the imaging data for the SDSS proper). In this paper, we describe the Monitor Telescope Pipeline, MTPIPE, the software pipeline used in processing the data from the single-CCD telescopes used in the photometric calibration of the SDSS (i.e., the USNO 1.0-m and the PT). We also describe transformation equations that convert photometry on the USNO-1.0m u g r i z system to photometry the SDSS 2.5m ugriz system and the results of various validation tests of the MTPIPE software. Further, we discuss the semi-automated PT factory, which runs MTPIPE in the day-to-day standard SDSS operations at Fermilab. Finally, we discuss the use of MTPIPE in current SDSS-related projects, including the Southern u g r i z Standard Star project, the u g r i z Open Star Clusters project, and the SDSS extension (SDSS-II).

We present the Bright SHARC (Serendipitous High-Redshift Archival ROSAT Cluster) Survey, which is an objective search for serendipitously detected extended X-ray sources in 460 deep ROSAT PSPC pointings. The Bright SHARC Survey covers an area of 178.6 sq.deg and has yielded 374 extended sources. We discuss the X-ray data reduction, the candidate selection and present results from our on-going optical follow-up campaign. The optical follow-up concentrates on the brightest 94 of the 374 extended sources and is now 97% complete. We have identified thirty-seven clusters of galaxies, for which we present redshifts and luminosities. The clusters span a redshift range of 0.0696

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Presented are four winter seasons of data from an enhanced precipitation instrument suite based at the National Weather Service (NWS) Office in Marquette (MQT), Michigan (250–500 cm of annual snow accumulation). In 2014 the site was augmented with a Micro Rain Radar (MRR) and a Precipitation Imaging Package (PIP). MRR observations are utilized to partition large-scale synoptically driven (deep) and surface-forced (shallow) snow events. Coincident PIP and NWS MQT meteorological surface observations illustrate different characteristics with respect to snow event category. Shallow snow events are often extremely shallow, with MRR-indicated precipitation heights of less than 1500 m above ground level. Large vertical reflectivity gradients indicate efficient particle growth, and increased boundary layer turbulence inferred from observations of spectral width implies increased aggregation in shallow snow events. Shallow snow events occur 2 times as often as deep events; however, both categories contribute approximately equally to estimated annual accumulation. PIP measurements reveal distinct regime-dependent snow microphysical differences, with shallow snow events having broader particle size distributions and comparatively fewer small particles and deep snow events having narrower particle size distributions and comparatively more small particles. In addition, coincident surface meteorological measurements indicate that most shallow snow events are associated with surface winds originating from the northwest (over Lake Superior), cold temperatures, and relatively high surface pressures, which are characteristics that are consistent with cold-air outbreaks. Deep snow events have meteorologically distinct conditions that are accordant with midlatitude cyclones and frontal structures, with mostly southwest surface winds, warmer temperatures approaching freezing, and lower surface pressures.