activity
20122017
most citedPhotometric redshift estimation via deep learning

162 citations · 193 across the 3 of their papers we have counts for

collaborators

5 papers

astro-ph.IM2017162 cited

Photometric redshift estimation via deep learning

Antonio D'Isanto, Kai Lars Polsterer

The need to analyze the available large synoptic multi-band surveys drives the development of new data-analysis methods. Photometric redshift estimation is one field of application…

astro-ph.IM2016

A Spectral Model for Multimodal Redshift Estimation

Sven D. Kugler, Nikolaos Gianniotis, Kai L. Polsterer

We present a physically inspired model for the problem of redshift estimation. Typically, redshift estimation has been treated as a regression problem that takes as input magnitude…

astro-ph.IM2015

Featureless Classification of Light Curves

Sven Dennis Kügler, Nikos Gianniotis, Kai Lars Polsterer

In the era of rapidly increasing amounts of time series data, classification of variable objects has become the main objective of time-domain astronomy. Classification of irregular…

astro-ph.IM20155 cited

Autoencoding Time Series for Visualisation

Nikolaos Gianniotis, Dennis Kügler, Peter Tino +2

We present an algorithm for the visualisation of time series. To that end we employ echo state networks to convert time series into a suitable vector representation which is capabl…

astro-ph.IM201226 cited

Finding New High-Redshift Quasars by Asking the Neighbours

Kai Lars Polsterer, Peter-Christian Zinn, Fabian Gieseke

Quasars with a high redshift (z) are important to understand the evolution processes of galaxies in the early universe. However only a few of these distant objects are known to thi…