arXiv subjects

Juan P. Carvajal

Publications and source records attributed to Juan P. Carvajal.

2 recordsLinked to original sources

arXiv2026

The complex history of NGC 1427A revealed by its star clusters and star formation history

Star-forming low-mass galaxies in the dense environments of galaxy clusters provide opportunities to study how environmental effects such as ram-pressure stripping, tidal interactions, or galaxy mergers shape a galaxy's star formation history. We combined integral-field spectroscopic observations with the Multi Unit Spectroscopic Explorer (MUSE) and available multi-band imaging of the star-forming galaxy NGC 1427A, located near the centre of the Fornax galaxy cluster, at a distance of 20 Mpc. Our aim was to trace the evolutionary history of NGC 1427A using the star formation history reconstructed from the integrated spectra and employing star clusters as surviving tracers of past star formation episodes. We fitted the spectral energy distribution of 222 star cluster candidates using archival $u,g,r$, and $i$ photometry to derive the ages and masses. For 58 clusters, we additionally incorporated their MUSE spectra in the fits and found an encouraging agreement between the photometric and spectroscopic results. The comparison of the age distribution of star clusters with star formation histories from a full spectrum fitting of the MUSE data found a reasonable agreement, with evidence for multiple episodes of star formation throughout the history of NGC 1427A. In particular, we found a population of young clusters ($\sim$ 10 Myr) that is located along the star formation edge and within the northern object, and a population of intermediate-age clusters ($\sim$ 100 - 300 Myr) with corresponding peaks in the star formation history of NGC 1427A. We interpret these populations in the context of the orbital evolution of NGC 1427A in the Fornax cluster and conclude that this galaxy has experienced not only ram-pressure stripping, but also tidal interactions or even a minor galaxy merger. The northern object is likely a regular component of the galaxy.

astro-ph.GA↗

arXiv2025

The Next Generation Fornax Survey (NGFS).VIII. A Support Vector Machine Approach for Disentangling Globular Clusters from other Sources

Wide-field, multi-band surveys now detect millions of unresolved sources in nearby galaxy clusters, yet separating globular clusters (GCs) from foreground stars and background galaxies remains challenging. Scalable, automated classification is therefore essential to convert the forthcoming data from facilities such as the Vera C. Rubin/LSST, the Roman and Euclid into robust constraints on galaxy assembly. We introduce a supervised classification method to separate GCs, stars, and galaxies based on their locations in color-color diagrams. The main objective is to recover a clean GC sample for future scientific analysis. The method exploits broad spectral energy distribution coverage, deep photometry, and is optimized for next-generation survey volumes. We use the central 3deg2 of the Next Generation Fornax Survey (NGFS), which images the Fornax cluster in u'g'i'JKs. We build a Support Vector Machine (SVM; svm.SVC, scikit-learn) using 15 features: all color combinations and basic morphological parameters. Spectroscopically confirmed sources define the training classes. Color pairs connecting near-UV/optical/near-IR. The full 15 feature model achieves 97.3% accuracy and a pruned 7 feature model built from the most informative, least correlated features achieves 96.6% accuracy. Misclassifications amount 8.4% and 10.4%, respectively. Omitting the u' or/and near-IR bands degrades performance. Emulating LSST filters with NGFS u'g'i' and DES r'z'Y shows that u' and Y bands are crucial, but models lacking NIR remain suboptimal. Combining broad SED coverage with simple morphological parameters enables precise, scalable separation of unresolved sources. Including NIR bands significantly improves GC classification, and joining LSST with forthcoming Euclid and Roman data will further enhance machine-learning frameworks.

astro-ph.GA↗