114 citations · 533 across the 17 of their papers we have counts for
17 papers
Galaxy-galaxy lensing in the VOICE deep survey
Ruibiao Luo, Liping Fu, Wentao Luo +11
The multi-band photometry of the VOICE imaging data, overlapping with 4.9 deg of the Chandra Deep Field South (CDFS) area, enables both shape measurement and photometric redshi…
Galaxy morphoto-Z with neural Networks (GaZNets). I. Optimized accuracy and outlier fraction from Imaging and Photometry
Rui Li, Nicola R. Napolitano, Haicheng Feng +8
In the era of large sky surveys, photometric redshifts (photo-z) represent crucial information for galaxy evolution and cosmology studies. In this work, we propose a new Machine Le…
Inferring galaxy dark halo properties from visible matter with Machine Learning
Rodrigo von Marttens, Luciano Casarini, Nicola R. Napolitano +6
Next-generation surveys will provide photometric and spectroscopic data of millions to billions of galaxies with unprecedented precision. This offers a unique chance to improve our…
Photometric redshifts with machine learning, lights and shadows on a complex data science use case
Massimo Brescia, Stefano Cavuoti, Oleksandra Razim +3
The current role of data-driven science is constantly increasing its importance within Astrophysics, due to the huge amount of multi-wavelength data collected every day, characteri…
Spectroscopic and Photometric Redshift Estimation by Neural Networks For the China Space Station Optical Survey (CSS-OS)
Xingchen Zhou, Yan Gong, Xian-Min Meng +6
The estimation of spectroscopic and photometric redshifts (spec-z and photo-z) is crucial for future cosmological surveys. It can directly affect several powerful measurements of t…
Euclid preparation: X. The Euclid photometric-redshift challenge
Euclid Collaboration, G. Desprez, S. Paltani +170
Forthcoming large photometric surveys for cosmology require precise and accurate photometric redshift (photo-z) measurements for the success of their main science objectives. Howev…
Rejection criteria based on outliers in the KiDS photometric redshifts and PDF distributions derived by machine learning
Valeria Amaro, Stefano Cavuoti, Massimo Brescia +7
The Probability Density Function (PDF) provides an estimate of the photometric redshift (zphot) prediction error. It is crucial for current and future sky surveys, characterized by…
Statistical analysis of probability density functions for photometric redshifts through the KiDS-ESO-DR3 galaxies
Valeria Amaro, Stefano Cavuoti, Massimo Brescia +8
Despite the high accuracy of photometric redshifts (zphot) derived using Machine Learning (ML) methods, the quantification of errors through reliable and accurate Probability Densi…
Evolution of galaxy size--stellar mass relation from the Kilo Degree Survey
N. Roy, N. R. Napolitano, F. La Barbera +16
We have obtained structural parameters of about 340,000 galaxies from the Kilo Degree Survey (KiDS) in 153 square degrees of data release 1, 2 and 3. We have performed a seeing con…
Weak Lensing Study in VOICE Survey I: Shear Measurement
Liping Fu, Dezi Liu, Mario Radovich +15
The VST Optical Imaging of the CDFS and ES1 Fields (VOICE) Survey is a Guaranteed Time program carried out with the ESO/VST telescope to provide deep optical imaging over two 4 deg…
Data Deluge in Astrophysics: Photometric Redshifts as a Template Use Case
Massimo Brescia, Stefano Cavuoti, Valeria Amaro +4
Astronomy has entered the big data era and Machine Learning based methods have found widespread use in a large variety of astronomical applications. This is demonstrated by the rec…
Photometric redshifts for the Kilo-Degree Survey. Machine-learning analysis with artificial neural networks
M. Bilicki, H. Hoekstra, M. J. I. Brown +24
We present a machine-learning photometric redshift analysis of the Kilo-Degree Survey Data Release 3, using two neural-network based techniques: ANNz2 and MLPQNA. Despite limited c…
Probability density estimation of photometric redshifts based on machine learning
Stefano Cavuoti, Massimo Brescia, Valeria Amaro +3
Photometric redshifts (photo-z's) provide an alternative way to estimate the distances of large samples of galaxies and are therefore crucial to a large variety of cosmological pro…
METAPHOR: Probability density estimation for machine learning based photometric redshifts
Valeria Amaro, Stefano Cavuoti, Massimo Brescia +3
We present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method able to provide a reliable PDF for photometric galaxy redshifts estimated throug…
Cooperative photometric redshift estimation
Stefano Cavuoti, Crescenzo Tortora, Massimo Brescia +5
In 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. Usi…
A cooperative approach among methods for photometric redshifts estimation: an application to KiDS data
Stefano Cavuoti, Crescenzo Tortora, Massimo Brescia +8
Photometric redshifts (photo-z's) are fundamental in galaxy surveys to address different topics, from gravitational lensing and dark matter distribution to galaxy evolution. The Ki…
METAPHOR: A machine learning based method for the probability density estimation of photometric redshifts
Stefano Cavuoti, Valeria Amaro, Massimo Brescia +3
A variety of fundamental astrophysical science topics require the determination of very accurate photometric redshifts (photo-z's). A wide plethora of methods have been developed,…