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J/A+A/611/A53       Redshift reliability flags (VVDS data)        (Jamal+, 2018)

Automated reliability assessment for spectroscopic redshift measurements. Jamal S., Le Brun V., Le Fevre O., Vibert D., Schmitt A., Surace C., Copin Y., Garilli B., Moresco M., Pozzetti L. <Astron. Astrophys. 611, A53 (2018)> =2018A&A...611A..53J (SIMBAD/NED BibCode)
ADC_Keywords: Galaxies, spectra ; Redshifts Keywords: methods: data analysis - methods: statistics - techniques: spectroscopic - galaxies: distance and redshift - surveys Abstract: Future large-scale surveys, as the ESA Euclid mission, will produce a large set of galaxy redshifts (≥10^6) that will require fully automated data-processing pipelines to analyze the data, extract crucial information and ensure that all requirements are met. A fundamental element in these pipelines is to associate to each galaxy redshift measurement a quality, or reliability, estimate. In this work, we introduce a new approach to automate the spectroscopic redshift reliability assessment based on machine learning (ML) and characteristics of the redshift probability density function. We propose to rephrase the spectroscopic redshift estimation into a Bayesian framework, in order to incorporate all sources of information and uncertainties related to the redshift estimation process and produce a redshift posterior probability density function (PDF). To automate the assessment of a reliability flag, we exploit key features in the redshift posterior PDF and machine learning algorithms. Description: The VIMOS VLT Deep Survey (Le Fevre et al. 2013A&A...559A..14L) is a combination of 3 i-band magnitude limited surveys: Wide (17.5≤iAB≤22.5; 8.6deg2), Deep (17.5≤iAB≤24; 0.6deg2) and Ultra-Deep (23≤iAB≤24.75; 512arcmin2), that produced a total of 35526 spectroscopic galaxy redshifts between 0 and 6.7 (22434 in Wide, 12051 in Deep and 1041 in UDeep). We supplement spectra of the VIMOS VLT Deep Survey (VVDS) with newly-defined redshift reliability flags obtained from clustering (unsupervised classification in Machine Learning) a set of descriptors from individual zPDFs. In this paper, we exploit a set of 24519 spectra from the VVDS database. After computing zPDFs for each individual spectrum, a set of (8) descriptors of the zPDF are extracted to build a feature matrix X (dimension = 24519 rows, 8 columns). Then, we use a clustering (unsupervised algorithms in Machine Learning) algorithm to partition the feature space into distinct clusters (5 clusters: C1,C2,C3,C4,C5), each depicting a different level of confidence to associate with the measured redshift zMAP (Maximum-A-Posteriori estimate that corresponds to the maximum of the redshift PDF). The clustering results (C1,C2,C3,C4,C5) reported in the table are those used in the paper (Jamal et al, 2017) to present the new methodology of automating the zspec reliability assessment. In particular, we would like to point out that they were obtained from first tests conducted on the VVDS spectroscopic data (end of 2016). Therefore, the table does not depict immutable results (on-going improvements). Future updates of the VVDS redshift reliability flags can be expected. File Summary:
FileName Lrecl Records Explanations
ReadMe 80 . This file table.dat 96 24519 Catalog of VVDS data and new zReliability labels
See also: III/250 : The VIMOS VLT deep survey (VVDS-DEEP) (Le Fevre+ 2005) Byte-by-byte Description of file: table.dat
Bytes Format Units Label Explanations
1- 5 A5 --- Survey Survey (1) 7- 19 A13 --- Field VVDS field (VVDS-FNNNN-NN or VVDS-CDFS) 21- 29 I09 --- ObsID Observation identification 31- 39 F9.5 deg RAdeg Right ascension (J2000) 41- 49 F9.5 deg DEdeg Declination (J2000) 51- 58 F8.5 mag Imag AB magnitude in I filter 60- 66 F7.5 --- zsp Spectroscopic redshift 68 I1 --- q_zsp [1/9] Initial VVDS z quality flags (2) 70- 71 A2 --- Relzsp [C1 C2 C3 C4 C5] New VVDS z reliability flags (3) 73- 96 A24 --- IAUName Name based on J2000 position (
Note (1): The VIMOS VLT Deep Survey is a combination of 3 surveys as follows: DEEP = VIMOS VLT Deep Survey UDEEP = VIMOS VLT Ultra-Deep Survey WIDE = VIMOS VLT Wide Survey (The table contains a subset of spectroscopic data from the 3 surveys) Note (2): The quality of a redshift is determined through quality flags (cf. Le Fevre et al., 2013A&A...559A..14L), as follows: 1 = Unreliable redshift 2 = Reliable redshift 9 = Reliable redshift, detection of a single emission line 3 = Very reliable redshift with strong spectral features 4 = Very reliable redshift with obvious spectral features Note (3): Newly-defined redshift reliability clusters refers to distinct partitions as follow: C1 = Highly dispersed PDFs with multiple equiprobable modes, P(zMAP)∼0.028±0.023 C2 = Less dispersed PDFs, with few modes and low probabilities P(zMAP)∼0.087±0.033 C3 = Low dispersion (σ), intermediate probabilities P(zMAP)∼0.166±0.035 C4 = Unimodal PDFs with low dispersion, higher probabilities P(zMAP)∼0.290±0.059 C5 = Strong unimodal PDFs with extremely low dispersion, better probabilities P(zMAP)∼0.618±0.204
Acknowledgements: Sara Jamal, sara.jamal(at) Vincent Le Brun, vincent.lebrun(at) References: Le Fevre et al., 2013A&A...559A..14L,
(End) Sara Jamal [LAM, France], Patricia Vannier [CDS] 15-Sep-2017
The document above follows the rules of the Standard Description for Astronomical Catalogues.From this documentation it is possible to generate f77 program to load files into arrays or line by line

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