arXiv subjects

Ylenia Maruccia

Publications and source records attributed to Ylenia Maruccia.

4 recordsLinked to original sources

arXiv2026

Classification of blazars based on data-driven approaches

Active galactic nuclei (AGNs), including blazars, exhibit distinctive variability in their optical light curves, making them ideal for classification studies. This work uses data from the latest GAIA and Pan-STARRS data releases to analyze these patterns. The goal of this work is to classify AGNs into two categories: "blazars" and "non-blazars'' using only optical light curves. This strategy differs from most existing works, as it relies exclusively on optical variability without employing any other multiwavelength information. We processed optical light curves from GAIA and Pan-STARRS using the FATS library to extract standard time-series features. We computed additional features with custom algorithms based on literature methods. A Light Gradient-Boosting Machine (LightGBM) model was trained to classify AGNs into blazars and non-blazars based on these features. We then used this knowledge base to carry out a self-learning experiment with AGN candidates of an unknown nature. The LightGBM model achieved an accuracy of $86\%$, with precision, recall, and F1 score above $80-85\%$ for classifying blazars and non-blazar AGNs using optical data. The application of a BoostBoruta algorithm for feature selection reduced the feature space from 70 to 13. while maintaining comparable performance. A self-training classifier yielded similar results $85\%$, confirming the robustness of the model and the reliability of pseudo-labeling for unknown objects.

astro-ph.GA↗

arXiv2026

Scavenger hunt: Selection of obscured active galactic nuclei combining multiband optical variability and colors

As wide-field optical surveys such as Vera Rubin Observatory's Legacy Survey of Space and Time (LSST) begin operations, time-domain astronomy is facing a data revolution, paving the road for new, expanded variability studies. This work leverages the complementary power of optical variability and color selection to identify active galactic nuclei (AGN), focusing on optimizing the identification of obscured AGN, typically more challenging to distinguish from inactive galaxies based on optical variability alone. The analysis is designed to provide valuable insights in the context of performance preview for the LSST, albeit using a scaled-down version of the LSST dataset. We present the first combined AGN selection based on g+r+i band light curves from the VST-COSMOS survey, spanning 3.3 yr. We identify AGN candidates independently in each band using a random forest (RF) classifier trained on features mainly related to optical variability, along with six optical/infrared colors and a morphology indicator. We subsequently merge the three band-specific samples in order to enhance selection purity and reliability. We then focus on defining a subset of features that significantly improve the identification of obscured AGN. The RF classifiers yield a consistent performance across the three bands, highlighting the critical role of contamination. Using the combined three-band plus color selection we successfully recover $58^{+9}_{-8}\%$ of all AGN and $69^{+10}_{-8}\%$ of the known obscured AGN that have been independently confirmed in all three bands. When requiring confirmation in two out of the three bands, these fractions increase to $69^{+10}_{-8}\%$ and $80^{+10}_{-9}\%$, respectively. We also demonstrate that, while combining variability features with colors is crucial to improve obscured AGN selection, relying solely on color features returns a markedly higher contamination rate.

astro-ph.GA↗

arXiv2025

Navigating AGN variability with self-organizing maps

Context. The classification of active galactic nuclei (AGNs) is a challenge in astrophysics. Variability features extracted from light curves offer a promising avenue for distinguishing AGNs and their subclasses. This approach would be very valuable in sight of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). Aims. Our goal is to utilize self-organizing maps (SOMs) to classify AGNs based on variability features and investigate how the use of different subsets of features impacts the purity and completeness of the resulting classifications. Methods. We derived a set of variability features from light curves, similar to those employed in previous studies, and applied SOMs to explore the distribution of AGNs subclasses. We conducted a comparative analysis of the classifications obtained with different subsets of features, focusing on the ability to identify different AGNs types. Results. Our analysis demonstrates that using SOMs with variability features yields a relatively pure AGNs sample, though completeness remains a challenge. In particular, Type 2 AGNs are the hardest to identify, as can be expected. These results represent a promising step toward the development of tools that may support AGNs selection in future large-scale surveys such as LSST.

astro-ph.IM↗

arXiv2025

The Evolutionary Path of Star-Forming Clumps in Hi-GAL

Star formation (SF) studies are benefiting from the huge amount of data made available by recent large-area Galactic plane surveys conducted between 2 μm and 3 mm. Fully characterizing SF demands integrating far-infrared/sub-millimetre (FIR/sub-mm) data, tracing the earliest phases, with near-/mid-infrared (NIR/MIR) observations, revealing later stages characterized by YSOs just before main sequence star appearance. However, the resulting dataset is often a complex mix of heterogeneous and intricate features, limiting the effectiveness of traditional analysis in uncovering hidden patterns and relationships. In this framework, machine learning emerges as a powerful tool to handle the complexity of feature-rich datasets and investigate potential physical connections between the cold dust component traced by FIR/sub-mm emission and the presence of YSOs. We present a study on the evolutionary path of star forming clumps in the Hi-GAL survey through a multi-step approach, with the final aims of (a) obtaining a robust and accurate set of features able to well classify the star forming clumps in Hi-GAL based on their evolutionary properties, (b) establishing whether a connection exists between the cold material reservoir in clumps, traced by FIR/sub-mm emission, and the already formed YSOs, precursors of stars. For these purposes, our designed experiments aim at testing whether the FIR/sub-mm properties related to clumps are sufficient to predict the clump evolutionary stage, without considering the direct information about the embedded YSOs at NIR/MIR. Our machine learning-based method involves a four-step approach, based on feature engineering, data handling, feature selection and classification. Our findings suggest that FIR/sub-mm and NIR/MIR emissions trace different evolutionary phases of star forming clumps, highlighting the complex and asynchronous nature of the SF process.

astro-ph.GA↗