This data consists of 286,401 with broad-band g,r,i,z,y photometry from the HSC DR2 survey and spectroscopic redshifts. The majority of galaxies in our sample lies between redshift of 0.01 and 2.
We present a supervised neural network approach to the determination of photometric redshifts. The m...
International audienceDeep-learning models have been increasingly exploited in astrophysical studies...
We present a technique for the estimation of photometric redshifts based on feed-forward neural netw...
This data consists of 286,401 with broad-band g,r,i,z,y photometry from the HSC DR2 survey and spec...
A new approach to estimating photometric redshifts – using artificial neural networks (ANNs) – is in...
International audienceImproving distance measurements in large imaging surveys is a major challenge ...
We developed a deep convolutional neural network (CNN), used as a classifier, to estimate photometri...
International audienceWe release photometric redshifts, reaching $\sim$0.7, for $\sim$14M galaxies a...
Galaxy redshifts are a key characteristic for nearly all extragalactic studies. Since spectroscopic ...
In cosmology and astronomy, measuring the distances to galaxies is an important task. This is done b...
Upcoming large scale photometric surveys will require accurate photometric redshifts (photo-zs) to o...
In this thesis work I explored the applicability of incorporating the galaxies spatial distribution ...
We present a supervised neural network approach to the determination of photometric redshifts. The m...
International audienceDeep-learning models have been increasingly exploited in astrophysical studies...
We present a technique for the estimation of photometric redshifts based on feed-forward neural netw...
This data consists of 286,401 with broad-band g,r,i,z,y photometry from the HSC DR2 survey and spec...
A new approach to estimating photometric redshifts – using artificial neural networks (ANNs) – is in...
International audienceImproving distance measurements in large imaging surveys is a major challenge ...
We developed a deep convolutional neural network (CNN), used as a classifier, to estimate photometri...
International audienceWe release photometric redshifts, reaching $\sim$0.7, for $\sim$14M galaxies a...
Galaxy redshifts are a key characteristic for nearly all extragalactic studies. Since spectroscopic ...
In cosmology and astronomy, measuring the distances to galaxies is an important task. This is done b...
Upcoming large scale photometric surveys will require accurate photometric redshifts (photo-zs) to o...
In this thesis work I explored the applicability of incorporating the galaxies spatial distribution ...
We present a supervised neural network approach to the determination of photometric redshifts. The m...
International audienceDeep-learning models have been increasingly exploited in astrophysical studies...
We present a technique for the estimation of photometric redshifts based on feed-forward neural netw...
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