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60 Publications visible to you, out of a total of 60
Interactive Exploration Framework for Big Data Sets

Abstract (Expand)

Big data issues are the order of the day for many researchers in astronomy. In the past, several machine learning methods were proposed to organize, classify, or condense big data sets. However, this … is not the end of the road. In most cases, researchers need to take further analysis by hand on automatically preprocessed data to gather valuable conclusions. To facilitate the pipeline of data analysis, we suggest a generic front-end framework allowing the user not only to process the data automatically, but also to interactively explore and investigate the results of machine learning procedures. A compact visualization gives an initial overview and can be adjusted to point out the parts of interest. By providing abstract accommodation functions such as zooming, scrolling, filtering, and labeling, crucial data fragments can be found and marked in an intuitive way.

Authors: Fenja Kollasch, Kai Polsterer

Date Published: 1st Jul 2022

Publication Type: InProceedings

Convolutional autoencoders for spatially-informed ensemble post-processing

Abstract (Expand)

Ensemble weather predictions typically show systematic errors that have to be corrected via post-processing. Even state-of-the-art post-processing methods based on neural networks often solely rely on … location-specific predictors that require an interpolation of the physical weather model's spatial forecast fields to the target locations. However, potentially useful predictability information contained in large-scale spatial structures within the input fields is potentially lost in this interpolation step. Therefore, we propose the use of convolutional autoencoders to learn compact representations of spatial input fields which can then be used to augment location-specific information as additional inputs to post-processing models. The benefits of including this spatial information is demonstrated in a case study of 2-m temperature forecasts at surface stations in Germany.

Authors: Sebastian Lerch, Kai L. Polsterer

Date Published: 25th Apr 2022

Publication Type: InProceedings

An Absolute Calibration of the Near-infrared Period–Luminosity Relations of Type II Cepheids in the Milky Way and in the Large Magellanic Cloud

Abstract (Expand)

Abstract We present time-series photometry of 21 nearby type II Cepheids in the near-infrared J , H , and K s passbands. We use this photometry, together with the Third Gaia Early Data Release parallaxes, … K s passbands. We use this photometry, together with the Third Gaia Early Data Release parallaxes, to determine for the first time period–luminosity relations (PLRs) for type II Cepheids from field representatives of these old pulsating stars in the near-infrared regime. We found PLRs to be very narrow for BL Herculis stars, which makes them candidates for precision distance indicators. We then use archival photometry and the most accurate distance obtained from eclipsing binaries to recalibrate PLRs for type II Cepheids in the Large Magellanic Cloud (LMC). Slopes of our PLRs in the Milky Way and in the LMC differ by slightly more than 2 σ and are in a good agreement with previous studies of the LMC, Galactic bulge, and Galactic globular cluster type II Cepheids samples. We use PLRs of Milky Way type II Cepheids to measure the distance to the LMC, and we obtain a distance modulus of 18.540 ± 0.026(stat.) ± 0.034(syst.) mag in the W JK Wesenheit index. We also investigate the metallicity effect within our Milky Way sample, and we find a rather significant value of about −0.2 mag dex −1 in each band meaning that more metal-rich type II Cepheids are intrinsically brighter than their more metal-poor counterparts, in agreement with the value obtained from type II Cepheids in Galactic globular clusters. The main source of systematic error on our Milky Way PLRs calibration, and the LMC distance, is the current uncertainty of the Gaia parallax zero-point.

Authors: Piotr Wielgórski, Grzegorz Pietrzyński, Bogumił Pilecki, Wolfgang Gieren, Bartłomiej Zgirski, Marek Górski, Gergely Hajdu, Weronika Narloch, Paulina Karczmarek, Radosław Smolec, Pierre Kervella, Jesper Storm, Alexandre Gallenne, Louise Breuval, Megan Lewis, Mikołaj Kałuszyński, Dariusz Graczyk, Wojciech Pych, Ksenia Suchomska, Mónica Taormina, Gonzalo Rojas Garcia, Aleksandra Kotek, Rolf Chini, Francisco Pozo Nũnez, Sadegh Noroozi, Catalina Sobrino Figaredo, Martin Haas, Klaus Hodapp, Przemysław Mikołajczyk, Krzysztof Kotysz, Dawid Moździerski, Piotr Kołaczek-Szymański

Date Published: 8th Mar 2022

Publication Type: Journal

Learning Features via Transformer Networks for Cardiomyocyte Profiling

Abstract

Not specified

Authors: Jan Plier, Matthias Zisler, Jennifer Furkel, Maximilian Knoll, Alexander Marx, Alena Fischer, Kai Polsterer, Mathias H. Konstandin, Stefania Petra

Date Published: 2022

Publication Type: InProceedings

Disentangling the optical AGN and host-galaxy luminosity with a probabilistic flux variation gradient

Abstract

Not specified

Authors: N. Gianniotis, F. Pozo Nuñez, K. L. Polsterer

Date Published: 29th Oct 2021

Publication Type: Journal

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

Not specified

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

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