arXiv subjects

Kai Lars Polsterer

Publications and source records attributed to Kai Lars Polsterer.

7 recordsLinked to original sources

arXiv2021

From Photometric Redshifts to Improved Weather Forecasts: machine learning and proper scoring rules as a basis for interdisciplinary work

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.

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arXiv2018

Return of the features. Efficient feature selection and interpretation for photometric redshifts

The explosion of data in recent years has generated an increasing need for new analysis techniques in order to extract knowledge from massive datasets. Machine learning has proved particularly useful to perform this task. Fully automatized methods have recently gathered great popularity, even though those methods often lack physical interpretability. In contrast, feature based approaches can provide both well-performing models and understandable causalities with respect to the correlations found between features and physical processes. Efficient feature selection is an essential tool to boost the performance of machine learning models. In this work, we propose a forward selection method in order to compute, evaluate, and characterize better performing features for regression and classification problems. Given the importance of photometric redshift estimation, we adopt it as our case study. We synthetically created 4,520 features by combining magnitudes, errors, radii, and ellipticities of quasars, taken from the SDSS. We apply a forward selection process, a recursive method in which a huge number of feature sets is tested through a kNN algorithm, leading to a tree of feature sets. The branches of the tree are then used to perform experiments with the random forest, in order to validate the best set with an alternative model. We demonstrate that the sets of features determined with our approach improve the performances of the regression models significantly when compared to the performance of the classic features from the literature. The found features are unexpected and surprising, being very different from the classic features. Therefore, a method to interpret some of the found features in a physical context is presented. The methodology described here is very general and can be used to improve the performance of machine learning models for any regression or classification task.

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arXiv2017

Photometric redshift estimation via deep learning

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 where such new methods improved the results, substantially. Up to now, the vast majority of applied redshift estimation methods have utilized photometric features. We aim to develop a method to derive probabilistic photometric redshift directly from multi-band imaging data, rendering pre-classification of objects and feature extraction obsolete. A modified version of a deep convolutional network was combined with a mixture density network. The estimates are expressed as Gaussian mixture models representing the probability density functions (PDFs) in the redshift space. In addition to the traditional scores, the continuous ranked probability score (CRPS) and the probability integral transform (PIT) were applied as performance criteria. We have adopted a feature based random forest and a plain mixture density network to compare performances on experiments with data from SDSS (DR9). We show that the proposed method is able to predict redshift PDFs independently from the type of source, for example galaxies, quasars or stars. Thereby the prediction performance is better than both presented reference methods and is comparable to results from the literature. The presented method is extremely general and allows us to solve of any kind of probabilistic regression problems based on imaging data, for example estimating metallicity or star formation rate of galaxies. This kind of methodology is tremendously important for the next generation of surveys.

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arXiv2016

Uncertain Photometric Redshifts

Photometric redshifts play an important role as a measure of distance for various cosmological topics. Spectroscopic redshifts are only available for a very limited number of objects but can be used for creating statistical models. A broad variety of photometric catalogues provide uncertain low resolution spectral information for galaxies and quasars that can be used to infer a redshift. Many different techniques have been developed to produce those redshift estimates with increasing precision. Instead of providing a point estimate only, astronomers start to generate probabilistic density functions (PDFs) which should provide a characterisation of the uncertainties of the estimation. In this work we present two simple approaches on how to generate those PDFs. We use the example of generating the photometric redshift PDFs of quasars from SDSS(DR7) to validate our approaches and to compare them with point estimates. We do not aim for presenting a new best performing method, but we choose an intuitive approach that is based on well known machine learning algorithms. Furthermore we introduce proper tools for evaluating the performance of PDFs in the context of astronomy. The continuous ranked probability score (CRPS) and the probability integral transform (PIT) are well accepted in the weather forecasting community. Both tools reflect how well the PDFs reproduce the real values of the analysed objects. As we show, nearly all currently used measures in astronomy show severe weaknesses when used to evaluate PDFs.

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arXiv2015

Featureless Classification of Light Curves

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 irregularly sampled time series is particularly difficult because the data cannot be represented naturally as a vector which can be directly fed into a classifier. In the literature, various statistical features serve as vector representations. In this work, we represent time series by a density model. The density model captures all the information available, including measurement errors. Hence, we view this model as a generalisation to the static features which directly can be derived, e.g., as moments from the density. Similarity between each pair of time series is quantified by the distance between their respective models. Classification is performed on the obtained distance matrix. In the numerical experiments, we use data from the OGLE and ASAS surveys and demonstrate that the proposed representation performs up to par with the best cur- rently used feature-based approaches. The density representation preserves all static information present in the observational data, in contrast to a less complete description by features. The density representation is an upper boundary in terms of information made available to the classifier. Consequently, the predictive power of the proposed classification depends on the choice of similarity measure and classifier, only. Due to its principled nature, we advocate that this new approach of representing time series has potential in tasks beyond classification, e.g., unsupervised learning.

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arXiv2012

Finding New High-Redshift Quasars by Asking the Neighbours

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 this date. The costs of building and operating a 10-metre class telescope limit the number of facilities and, thus, the available observation time. Therefore an efficient selection of candidates is mandatory. This paper presents a new approach to select quasar candidates with high redshift (z>4.8) based on photometric catalogues. We have chosen to use the z>4.8 limit for our approach because the dominant Lyman alpha emission line of a quasar can only be found in the Sloan i and z-band filters. As part of the candidate selection approach, a photometric redshift estimator is presented, too. Three of the 120,000 generated candidates have been spectroscopically analysed in follow-up observations and a new z=5.0 quasar was found. This result is consistent with the estimated detection ratio of about 50 per cent and we expect 60,000 high-redshift quasars to be part of our candidate sample. The created candidates are available for download at MNRAS or at http://www.astro.rub.de/polsterer/quasar-candidates.csv.

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arXiv2011

Detecting Quasars in Large-Scale Astronomical Surveys

We present a classification-based approach to identify quasi-stellar radio sources (quasars) in the Sloan Digital Sky Survey and evaluate its performance on a manually labeled training set. While reasonable results can already be obtained via approaches working only on photometric data, our experiments indicate that simple but problem-specific features extracted from spectroscopic data can significantly improve the classification performance. Since our approach works orthogonal to existing classification schemes used for building the spectroscopic catalogs, our classification results are well suited for a mutual assessment of the approaches' accuracies.

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