Search Results for author: D. Fouchez

Found 6 papers, 4 papers with code

Photometric Redshift Estimation with Convolutional Neural Networks and Galaxy Images: A Case Study of Resolving Biases in Data-Driven Methods

no code implementations21 Feb 2022 Q. Lin, D. Fouchez, J. Pasquet, M. Treyer, R. Ait Ouahmed, S. Arnouts, O. Ilbert

Deep Learning models have been increasingly exploited in astrophysical studies, yet such data-driven algorithms are prone to producing biased outputs detrimental for subsequent analyses.

Photometric Redshift Estimation Representation Learning

PhotoWeb redshift: boosting photometric redshift accuracy with large spectroscopic surveys

no code implementations24 Mar 2020 Marko Shuntov, J. Pasquet, S. Arnouts, O. Ilbert, M. Treyer, E. Bertin, S. de la Torre, Y. Dubois, D. Fouchez, K. Kraljic, C. Laigle, C. Pichon, D. Vibert

Combining these PDFs with the density field distribution provides new photometric redshifts, $z_{web}$, whose accuracy is improved by a factor of two (i. e.,${\sigma} \sim 0. 004(1+z)$) for galaxies with $r \leq 17. 8$.

Astrophysics of Galaxies

Improved cosmological constraints from a joint analysis of the SDSS-II and SNLS supernova samples

6 code implementations16 Jan 2014 M. Betoule, R. Kessler, J. Guy, J. Mosher, D. Hardin, R. Biswas, P. Astier, P. El-Hage, M. Konig, S. Kuhlmann, J. Marriner, R. Pain, N. Regnault, C. Balland, B. A. Bassett, P. J. Brown, H. Campbell, R. G. Carlberg, F. Cellier-Holzem, D. Cinabro, A. Conley, C. B. D'Andrea, D. L. Depoy, M. Doi, R. S. Ellis, S. Fabbro, A. V. Filippenko, R. J. Foley, J. A. Frieman, D. Fouchez, L. Galbany, A. Goobar, R. R. Gupta, G. J. Hill, R. Hlozek, C. J. Hogan, I. M. Hook, D. A. Howell, S. W. Jha, L. Le Guillou, G. Leloudas, C. Lidman, J. L. Marshall, A. Möller, A. M. Mourão, J. Neveu, R. Nichol, M. D. Olmstead, N. Palanque-Delabrouille, S. Perlmutter, J. L. Prieto, C. J. Pritchet, M. Richmond, A. G. Riess, V. Ruhlmann-Kleider, M. Sako, K. Schahmaneche, D. P. Schneider, M. Smith, J. Sollerman, M. Sullivan, N. A. Walton, C. J. Wheeler

We have followed the methods and assumptions of the SNLS 3-year data analysis except for the following important improvements: 1) the addition of the full SDSS-II spectroscopically-confirmed SN Ia sample in both the training of the SALT2 light curve model and in the Hubble diagram analysis (\nsdssc SNe), 2) inter-calibration of the SNLS and SDSS surveys and reduced systematic uncertainties in the photometric calibration, performed blindly with respect to the cosmology analysis, and 3) a thorough investigation of systematic errors associated with the SALT2 modeling of SN Ia light-curves.

Cosmology and Nongalactic Astrophysics

Photometric Calibration of the Supernova Legacy Survey Fields

2 code implementations26 Aug 2009 N. Regnault, A. Conley, J. Guy, M. Sullivan, J. -C. Cuillandre, P. Astier, C. Balland, S. Basa, R. G. Carlberg, D. Fouchez, D. Hardin, I. M. Hook, D. A. Howell, R. Pain, K. Perrett, C. J. Pritchet

The photometric calibration of the SNLS requires obtaining a uniform response across the imager, calibrating the science field stars in each survey band (SDSS-like ugriz bands) with respect to standards with known flux in the same bands, and binding the calibration to the UBVRI Landolt standards used to calibrate the nearby SNe from the literature necessary to produce cosmological constraints.

Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics

The Core-collapse rate from the Supernova Legacy Survey

2 code implementations7 Apr 2009 G. Bazin, N. Palanque-Delabrouille, J. Rich, V. Ruhlmann-Kleider, E. Aubourg, L. Le Guillou, P. Astier, C. Balland, S. Basa, R. G. Carlberg, A. Conley, D. Fouchez, J. Guy, D. Hardin, I. M. Hook, D. A. Howell, R. Pain, K. Perrett, C. J. Pritchet, N. Regnault, M. Sullivan, P. Antilogus, V. Arsenijevic, S. Baumont, S. Fabbro, J. Le Du, C. Lidman, M. Mouchet, A. Mourão, E. S. Walker

Using spectroscopy and light-curve fitting to discriminate against SNIa, we find a sample of 117 core-collapse supernova candidates with redshifts $z<0. 4$ (median redshift of 0. 29) and measure their rate to be larger than the type Ia supernova rate by a factor $4. 5\pm0. 8(stat.)

Cosmology and Nongalactic Astrophysics

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