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  1. OA@INAF
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  3. 1 CONTRIBUTI IN RIVISTE (Journal articles)
  4. 1.01 Articoli in rivista
Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12386/32589
Title: Photometric redshifts with the quasi Newton algorithm (MLPQNA) Results in the PHAT1 contest
Authors: CAVUOTI, STEFANO 
BRESCIA, Massimo 
Longo, Giuseppe
MERCURIO, AMATA 
Issue Date: 2012
Journal: ASTRONOMY & ASTROPHYSICS 
Number: 546
First Page: 1
Abstract: Context. Since the advent of modern multiband digital sky surveys, photometric redshifts (photo-z's) have become relevant if not crucial to many fields of observational cosmology, such as the characterization of cosmic structures and the weak and strong lensing. <BR /> Aims: We describe an application to an astrophysical context, namely the evaluation of photometric redshifts, of MLPQNA, which is a machine-learning method based on the quasi Newton algorithm. <BR /> Methods: Theoretical methods for photo-z evaluation are based on the interpolation of a priori knowledge (spectroscopic redshifts or SED templates), and they represent an ideal comparison ground for neural network-based methods. The MultiLayer Perceptron with quasi Newton learning rule (MLPQNA) described here is an effective computing implementation of neural networks exploited for the first time to solve regression problems in the astrophysical context. It is offered to the community through the DAMEWARE (DAta Mining & Exploration Web Application REsource) infrastructure. <BR /> Results: The PHAT contest (Hildebrandt et al. 2010, A&A, 523, A31) provides a standard dataset to test old and new methods for photometric redshift evaluation and with a set of statistical indicators that allow a straightforward comparison among different methods. The MLPQNA model has been applied on the whole PHAT1 dataset of 1984 objects after an optimization of the model performed with the 515 available spectroscopic redshifts as training set. When applied to the PHAT1 dataset, MLPQNA obtains the best bias accuracy (0.0006) and very competitive accuracies in terms of scatter (0.056) and outlier percentage (16.3%), scoring as the second most effective empirical method among those that have so far participated in the contest. MLPQNA shows better generalization capabilities than most other empirical methods especially in the presence of underpopulated regions of the knowledge base.
URI: http://hdl.handle.net/20.500.12386/32589
URL: https://www.aanda.org/articles/aa/full_html/2012/10/aa19755-12/aa19755-12.html
http://arxiv.org/abs/1206.0876v3
ISSN: 0004-6361
DOI: 10.1051/0004-6361/201219755
Bibcode ADS: 2012A&A...546A..13C
Fulltext: open
Appears in Collections:1.01 Articoli in rivista

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