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6 Publications visible to you, out of a total of 6
Probabilistic photometric redshift estimation in massive digital sky surveys via machine learning

Abstract (Expand)

The problem of photometric redshift estimation is a major subject in astronomy, since the need of estimating distances for a huge number of sources, as required by the data deluge of the recent years. … The ability to estimate redshifts through spectroscopy does not scale with this avalanche of data. Photometric redshifts provide the required redshift estimates at the cost of some precision. The success of several forthcoming missions is highly dependent on the availability of photometric redshifts. The purpose of this thesis is to provide innovative methods for photometric redshift estimation. Two models are proposed. The first is fully-automatized, based on the combination of a convolutional neural network with a mixture density network, to predict probabilistic multimodal redshifts directly from images. The second model is features-based, performing a massive combination of photometric parameters to apply a forward selection in a huge feature space. The proposed models perform very efficiently compared to some of the most common models used in the literature. An important part of the work is dedicated to the correct estimation of the errors and prediction quality. The proposed models are very general and can be applied to different topics in astronomy and beyond.

Author: Antonio D'Isanto

Date Published: 1st Feb 2019

Publication Type: Doctoral Thesis

Photometric redshift estimation via deep learning. Generalized and pre-classification-less, image based, fully probabilistic redshifts

Abstract

Not specified

Authors: A. D’Isanto, K. L. Polsterer

Date Published: 2018

Publication Type: Journal

Return of the features - Efficient feature selection and interpretation for photometric redshifts

Abstract

Not specified

Authors: A. D\’Isanto, S. Cavuoti, F. Gieseke, K. L. Polsterer

Date Published: 2018

Publication Type: Journal

Uncertain photometric redshifts via combining deep convolutional and mixture density networks

Abstract

Not specified

Authors: A. D’Isanto, K. L. Polsterer

Date Published: 2017

Publication Type: InProceedings

Uncertain Photometric Redshifts with Deep Learning Methods

Abstract

Not specified

Author: Antonio Disanto

Date Published: 1st Oct 2016

Publication Type: Journal

An analysis of feature relevance in the classification of astronomical transients with machine learning methods

Abstract

Not specified

Authors: Antonio Disanto, S. Cavuoti, M. Brescia, C. Donalek, G. Longo, G. Riccio, S. G. Djorgovski

Date Published: 2016

Publication Type: Journal

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