arXiv subjects

Nicolas Baron Perez

Publications and source records attributed to Nicolas Baron Perez.

3 recordsLinked to original sources

arXiv2026

Morphology of Radio Sources in Representation Space

Understanding radio source morphologies and their classification remains challenging. We previously developed a deep clustering method based on self-supervised learning to classify a subsample of radio sources from the LOFAR Two-meter Sky Survey DR2. This yielded a labelled subset used to fine-tune an ensemble of classifiers. We aim to identify rare morphological classes in a subsample of LoTSS-DR3, which contains > 13 million sources, beyond the 12 classes previously recognised in the DR2 sample. We further aim to characterise the resulting class distribution. We applied the classifier ensemble to derive class probabilities and representations for our samples. Among DR3 sources with low class probabilities, we searched for new clusters in representation space. Moreover, we use these clusters as centroids to classify the low-probability subset. We found that 88% of the sources fall into clearly separated clusters in representation space, coinciding with the DR2 clusters. Within the remaining sources, we identified representatives of six additional morphological classes, including rare morphologies like winged and FR-ambiguous sources. The number of sources in the resulting 18 classes exhibit a strongly skewed distribution, with four dominant classes constituting 69% of the DR3 sample, while rare morphologies account for only a small fraction. The identification of additional morphological classes shows the possibility of open-set recognition with representation learning in an unexplored dataset. Unlike anomaly detection, which flags individual sources as uncommon, this approach identifies representatives of novel morphological classes, providing a framework for discovering novel source populations. The highly skewed class distribution poses a fundamental challenge for constructing balanced training datasets and highlights the need for open-set approaches in future observations.

astro-ph.IM↗

arXiv2025

Classification of Radio Sources Through Self-Supervised Learning

The morphology of radio galaxies is indicative of their interaction with their surroundings, among other effects. Since modern radio surveys contain a large number of radio sources that would be impossible to analyse and classify manually, it is important to develop automatic schemes. Unlike other fields, which benefit from established theoretical frameworks and simulations, there are no such comprehensive models built for radio galaxies. This stands as a challenge to data analysis in this field and novel approaches are required. In this study, we investigate the classification of radio galaxies from the LOFAR Two-meter Sky Survey Data Release 2 (LoTSS-DR2) using self-supervised learning. Our deep clustering classification strategy involves three main steps: (i) self-supervised pre-training; (ii) fine-tuning using a labelled subsample created from the learned representations; and (iii) performing a final classification of the selected unlabelled sample. To enhance morphological information in the representations, we developed an additional random augmentation, called a random structural view (RSV). Our results demonstrate that the learned representations contain rich morphological information, enabling the creation of a labelled subsample that effectively captures the morphological diversity within the unlabelled sample. Additionally, the classification of the unlabelled sample into 12 morphological classes yields robust class probabilities. We successfully demonstrated that a subset of radio galaxies from LoTSS-DR2, encompassing diverse morphologies, can be classified using deep clustering based on self-supervised learning. The methodology developed here bridges the gap left by the absence of simulations and theoretical models, offering a framework that can readily be applied to astronomical image analyses in other bands.

astro-ph.IM↗

arXiv2023

The eROSITA Final Equatorial-Depth Survey (eFEDS): A Machine Learning Approach to Infer Galaxy Cluster Masses from eROSITA X-ray Images

We develop a neural network based pipeline to estimate masses of galaxy clusters with a known redshift directly from photon information in X-rays. Our neural networks are trained using supervised learning on simulations of eROSITA observations, focusing in this paper on the Final Equatorial Depth Survey (eFEDS). We use convolutional neural networks which are modified to include additional information of the cluster, in particular its redshift. In contrast to existing work, we utilize simulations including background and point sources to develop a tool which is usable directly on observational eROSITA data for an extended mass range from group size halos to massive clusters with masses in between $10^{13}M_\odot<M<10^{15}M_\odot.$ Using this method, we are able to provide for the first time neural network mass estimation for the observed eFEDS cluster sample from Spectrum-Roentgen-Gamma/eROSITA observations and we find consistent performance with weak lensing calibrated masses. In this measurement, we do not use weak lensing information and we only use previous cluster mass information which was used to calibrate the cluster properties in the simulations. When compared to simulated data, we observe a reduced scatter with respect to luminosity and count-rate based scaling relations. We comment on the application for other upcoming eROSITA All-Sky Survey observations.

astro-ph.CO↗