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88 Publications visible to you, out of a total of 88
From Photometric Redshifts to Improved Weather Forecasts: machine learning and proper scoring rules as a basis for interdisciplinary work

Abstract (Expand)

The amount, size, and complexity of astronomical data-sets and databases are growing rapidly in the last decades, due to new technologies and dedicated survey telescopes. Besides dealing with poly-structured … and complex data, sparse data has become a field of growing scientific interest. A specific field of Astroinformatics research is the estimation of redshifts of extra-galactic sources by using sparse photometric observations. Many techniques have been developed to produce those estimates with increasing precision. In recent years, models have been favored which instead of providing a point estimate only, are able to generate probabilistic density functions (PDFs) in order to characterize and quantify the uncertainties of their estimates. Crucial to the development of those models is a proper, mathematically principled way to evaluate and characterize their performances, based on scoring functions as well as on tools for assessing calibration. Still, in literature inappropriate methods are being used to express the quality of the estimates that are often not sufficient and can potentially generate misleading interpretations. In this work we summarize how to correctly evaluate errors and forecast quality when dealing with PDFs. We describe the use of the log-likelihood, the continuous ranked probability score (CRPS) and the probability integral transform (PIT) to characterize the calibration as well as the sharpness of predicted PDFs. We present what we achieved when using proper scoring rules to train deep neural networks as well as to evaluate the model estimates and how this work led from well calibrated redshift estimates to improvements in probabilistic weather forecasting. The presented work is an example of interdisciplinarity in data-science and illustrates how methods can help to bridge gaps between different fields of application.

Authors: Kai Polsterer, Sebastian Lerch, Antonio D'Isanto

Date Published: 5th Mar 2021

Publication Type: InProceedings

Unveiling the rarest morphologies of the LOFAR Two-metre Sky Survey radio source population with self-organised maps

Abstract

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Authors: Rafaël I. J. Mostert, Kenneth J. Duncan, Huub J. A. Röttgering, Kai L. Polsterer, Philip N. Best, Marisa Brienza, Marcus Brüggen, Martin J. Hardcastle, Nika Jurlin, Beatriz Mingo, Raffaella Morganti, Tim Shimwell, Dan Smith, Wendy L. Williams

Date Published: 2021

Publication Type: Journal

First-Order Geometric Multilevel Optimization for Discrete Tomography

Abstract

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Authors: Jan Plier, Fabrizio Savarino, Michal Kočvara, Stefania Petra

Date Published: 2021

Publication Type: InProceedings

Birds of a Feather Session on Machine Learning in Astronomy

Abstract

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Author: K. L. Polsterer

Date Published: 18th Nov 2020

Publication Type: InProceedings

Cataloguing the radio-sky with unsupervised machine learning: a new approach for the SKA era

Abstract

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Authors: T J Galvin, M T Huynh, R P Norris, X R Wang, E Hopkins, K Polsterer, N O Ralph, A N O’Brien, G H Heald

Date Published: 1st Sep 2020

Publication Type: Journal

Comparison of outlier detection methods on astronomical image data

Abstract (Expand)

Among the many challenges posed by the huge data volumes produced by the new generation of astronomical instruments there is also the search for rare and peculiar objects. Unsupervised outlier detection … algorithms may provide a viable solution. In this work we compare the performances of six methods: the Local Outlier Factor, Isolation Forest, k-means clustering, a measure of novelty, and both a normal and a convolutional autoencoder. These methods were applied to data extracted from SDSS stripe 82. After discussing the sensitivity of each method to its own set of hyperparameters, we combine the results from each method to rank the objects and produce a final list of outliers.

Authors: Lars Doorenbos, Stefano Cavuoti, Massimo Brescia, Antonio D'Isanto, Giuseppe Longo

Date Published: 2020

Publication Type: Journal

Integration of Distributed Parallel Simulation Environment with Cloud-Infrastructures

Abstract (Expand)

This article describes an integration of the subsystems of the distributed parallel simulation environment with cloud infrastructures. A complex support for simulation of the dynamic network object … with distributed parameters on Amazon AWS cloud is provided. As well as a tool helping to significantly save running costs for cloud simulations.

Authors: O. Shcherbakov, Kai L. Polsterer, V. A. Svyatnyy

Date Published: 2020

Publication Type: InProceedings

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