scholarly journals ANNz2 - Photometric redshift and probability density function estimation using machine-learning

2014 ◽  
Vol 10 (S306) ◽  
pp. 316-318
Author(s):  
Iftach Sadeh
Keyword(s):  
Machine Learning ◽  
Probability Density ◽  
Function Estimation ◽  
Photometric Redshifts ◽  
Galaxy Surveys ◽  
The Public ◽  
Photometric Redshift ◽  
Machine Learning Methods ◽  
Density Function Estimation ◽  
Full Probability

AbstractLarge photometric galaxy surveys allow the study of questions at the forefront of science, such as the nature of dark energy. The success of such surveys depends on the ability to measure the photometric redshifts of objects (photo-zs), based on limited spectral data. A new major version of the public photo-z estimation software, ANNz, is presented here. The new code incorporates several machine-learning methods, such as artificial neural networks and boosted decision/regression trees, which are all used in concert. The objective of the algorithm is to dynamically optimize the performance of the photo-z estimation, and to properly derive the associated uncertainties. In addition to single-value solutions, the new code also generates full probability density functions in two independent ways.

scholarly journals Cooperative photometric redshift estimation

2016 ◽  
Vol 12 (S325) ◽  
pp. 166-172
Author(s):  
S. Cavuoti ◽  
C. Tortora ◽  
M. Brescia ◽  
G. Longo ◽  
M. Radovich ◽  
...  
Keyword(s):  
Machine Learning ◽  
Galaxy Evolution ◽  
Gravitational Lensing ◽  
Hybrid Approach ◽  
Learning Rule ◽  
Multi Layer Perceptron ◽  
Photometric Redshifts ◽  
Galaxy Surveys ◽  
Photometric Redshift ◽  
Fitting Procedure

AbstractIn the modern galaxy surveys photometric redshifts play a central role in a broad range of studies, from gravitational lensing and dark matter distribution to galaxy evolution. Using a dataset of ~ 25,000 galaxies from the second data release of the Kilo Degree Survey (KiDS) we obtain photometric redshifts with five different methods: (i) Random forest, (ii) Multi Layer Perceptron with Quasi Newton Algorithm, (iii) Multi Layer Perceptron with an optimization network based on the Levenberg-Marquardt learning rule, (iv) the Bayesian Photometric Redshift model (or BPZ) and (v) a classical SED template fitting procedure (Le Phare). We show how SED fitting techniques could provide useful information on the galaxy spectral type which can be used to improve the capability of machine learning methods constraining systematic errors and reduce the occurrence of catastrophic outliers. We use such classification to train specialized regression estimators, by demonstrating that such hybrid approach, involving SED fitting and machine learning in a single collaborative framework, is capable to improve the overall prediction accuracy of photometric redshifts.

Maximum-likelihood maximum-entropy constrained probability density function estimation for prediction of rare events

AIChE Journal ◽  
2014 ◽  
Vol 60 (3) ◽  
pp. 1013-1026 ◽  
Author(s):  
Taha Mohseni Ahooyi ◽  
Masoud Soroush ◽  
Jeffrey E. Arbogast ◽  
Warren D. Seider ◽  
Ulku G. Oktem
Keyword(s):  
Maximum Likelihood ◽  
Probability Density Function ◽  
Probability Density ◽  
Maximum Entropy ◽  
Density Function ◽  
Rare Events ◽  
Function Estimation ◽  
Density Function Estimation

scholarly journals On the Method of Logarithmic Cumulants for Parametric Probability Density Function Estimation

2013 ◽  
Vol 22 (10) ◽  
pp. 3791-3806 ◽  
Author(s):  
Vladimir A. Krylov ◽  
Gabriele Moser ◽  
Sebastiano B. Serpico ◽  
Josiane Zerubia
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Function Estimation ◽  
Density Function Estimation

Sparse probability density function estimation using the minimum integrated square error

2013 ◽  
Vol 115 ◽  
pp. 122-129 ◽  
Author(s):  
Xia Hong ◽  
Sheng Chen ◽  
Abdulrohman Qatawneh ◽  
Khaled Daqrouq ◽  
Muntasir Sheikh ◽  
...  
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Function Estimation ◽  
Density Function Estimation
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