Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userDeep ugrizY Imaging and DEEP2/3 Spectroscopy: A Photometric Redshift Testbed for LSST and Public Release of Data from the DEEP3 Galaxy Redshift Survey
73
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
We present catalogs of calibrated photometry and spectroscopic redshifts in the Extended Groth Strip, intended for studies of photometric redshifts (photo-zs). The data includes ugriz photometry from CFHTLS and Y-band photometry from the Subaru Suprime camera, as well as spectroscopic redshifts from the DEEP2, DEEP3 and 3D-HST surveys. These catalogs incorporate corrections to produce effectively matched-aperture photometry across all bands, based upon object size information available in the catalog and Moffat profile point spread function fits. We test this catalog with a simple machine learning-based photometric redshift algorithm based upon Random Forest regression, and find that the corrected aperture photometry leads to significant improvement in photo-z accuracy compared to the original SExtractor catalogs from CFHTLS and Subaru. The deep ugrizY photometry and spectroscopic redshifts are well-suited for empirical tests of photometric redshift algorithms for LSST. The resulting catalogs are publicly available. We include a basic summary of the strategy of the DEEP3 Galaxy Redshift Survey to accompany the recent public release of DEEP3 data.
rate research
Read More
This paper describes a new catalog that supplements the existing DEEP2 Galaxy Redshift Survey photometric and spectroscopic catalogs with ugriz photometry from two other surveys; the Canada-France-Hawaii Legacy Survey (CFHTLS) and the Sloan Digital Sky Survey (SDSS). Each catalog is cross-matched by position on the sky in order to assign ugriz photometry to objects in the DEEP2 catalogs. We have recalibrated the CFHTLS photometry where it overlaps DEEP2 in order to provide a more uniform dataset. We have also used this improved photometry to predict DEEP2 BRI photometry in regions where only poorer measurements were available previously. In addition, we have included improved astrometry tied to SDSS rather than USNO-A2.0 for all DEEP2 objects. In total this catalog contains ~27,000 objects with full ugriz photometry as well as robust spectroscopic redshift measurements, 64% of which have r > 23. By combining the secure and accurate redshifts of the DEEP2 Galaxy Redshift Survey with ugriz photometry, we have created a catalog that can be used as an excellent testbed for future photo-z studies, including tests of algorithms for surveys such as LSST and DES.
We describe the design and data sample from the DEEP2 Galaxy Redshift Survey, the densest and largest precision-redshift survey of galaxies at z ~ 1 completed to date. The survey has conducted a comprehensive census of massive galaxies, their properties, environments, and large-scale structure down to absolute magnitude M_B = -20 at z ~ 1 via ~90 nights of observation on the DEIMOS spectrograph at Keck Observatory. DEEP2 covers an area of 2.8 deg^2 divided into four separate fields, observed to a limiting apparent magnitude of R_AB=24.1. Objects with z < 0.7 are rejected based on BRI photometry in three of the four DEEP2 fields, allowing galaxies with z > 0.7 to be targeted ~2.5 times more efficiently than in a purely magnitude-limited sample. Approximately sixty percent of eligible targets are chosen for spectroscopy, yielding nearly 53,000 spectra and more than 38,000 reliable redshift measurements. Most of the targets which fail to yield secure redshifts are blue objects that lie beyond z ~ 1.45. The DEIMOS 1200-line/mm grating used for the survey delivers high spectral resolution (R~6000), accurate and secure redshifts, and unique internal kinematic information. Extensive ancillary data are available in the DEEP2 fields, particularly in the Extended Groth Strip, which has evolved into one of the richest multiwavelength regions on the sky. DEEP2 surpasses other deep precision-redshift surveys at z ~ 1 in terms of galaxy numbers, redshift accuracy, sample number density, and amount of spectral information. We also provide an overview of the scientific highlights of the DEEP2 survey thus far. This paper is intended as a handbook for users of the DEEP2 Data Release 4, which includes all DEEP2 spectra and redshifts, as well as for the publicly-available DEEP2 DEIMOS data reduction pipelines. [Abridged]
Supernova (SN) classification and redshift estimation using photometric data only have become very important for the Large Synoptic Survey Telescope (LSST), given the large number of SNe that LSST will observe and the impossibility of spectroscopically following up all the SNe. We investigate the performance of a SN classifier that uses SN colors to classify LSST SNe with the Random Forest classification algorithm. Our classifier results in an AUC of 0.98 which represents excellent classification. We are able to obtain a photometric SN sample containing 99$%$ SNe Ia by choosing a probability threshold. We estimate the photometric redshifts (photo-z) of SNe in our sample by fitting the SN light curves using the SALT2 model with nested sampling. We obtain a mean bias ($left<z_mathrm{phot}-z_mathrm{spec}right>$) of 0.012 with $sigmaleft( frac{z_mathrm{phot}-z_mathrm{spec}}{1+z_mathrm{spec}}right) = 0.0294$ without using a host-galaxy photo-z prior, and a mean bias ($left<z_mathrm{phot}-z_mathrm{spec}right>$) of 0.0017 with $sigmaleft( frac{z_mathrm{phot}-z_mathrm{spec}}{1+z_mathrm{spec}}right) = 0.0116$ using a host-galaxy photo-z prior. Assuming a flat $Lambda CDM$ model with $Omega_m=0.3$, we obtain $Omega_m$ of $0.305pm0.008$ (statistical errors only), using the simulated LSST sample of photometric SNe Ia (with intrinsic scatter $sigma_mathrm{int}=0.11$) derived using our methodology without using host-galaxy photo-z prior. Our method will help boost the power of SNe from the LSST as cosmological probes.
We present the results of spectroscopic observations in the GOODS-N field completed using DEIMOS on the Keck II telescope as part of the DEEP3 Galaxy Redshift Survey. Observations of 370 unique targets down to a limiting magnitude of R = 24.4 yielded 156 secure redshifts. In addition to redshift information, we provide sky-subtracted one- and two-dimensional spectra of each target. Observations were conducted following the procedures of the Team Keck Redshift Survey (TKRS), thereby producing spectra that augment the TKRS sample while maintaining the uniformity of its spectral database.
We present and describe a catalog of galaxy photometric redshifts (photo-zs) for the Sloan Digital Sky Survey (SDSS) Data Release 6 (DR6). We use the Artificial Neural Network (ANN) technique to calculate photo-zs and the Nearest Neighbor Error (NNE) method to estimate photo-z errors for ~ 77 million objects classified as galaxies in DR6 with r < 22. The photo-z and photo-z error estimators are trained and validated on a sample of ~ 640,000 galaxies that have SDSS photometry and spectroscopic redshifts measured by SDSS, 2SLAQ, CFRS, CNOC2, TKRS, DEEP, and DEEP2. For the two best ANN methods we have tried, we find that 68% of the galaxies in the validation set have a photo-z error smaller than sigma_{68} =0.021 or $0.024. After presenting our results and quality tests, we provide a short guide for users accessing the public data.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
