arXiv subjects

V. Contreras Rojas

Publications and source records attributed to V. Contreras Rojas.

3 recordsLinked to original sources

arXiv2026

On the performance of pre-trained vision transformers for supernova spectral classification using different spectral representations

The spectroscopic classification of supernovae is a key component of time-domain astronomy and plays an important role in the identification of Type Ia events. The increasing volume and diversity of spectroscopic data motivate the development of automated classification approaches. In this work, we explore the use of Transformer-based vision models for supernova spectral classification, focusing on how different visual representations of spectra and fine-tuning strategies affect classification performance when image-based architectures are applied to intrinsically one-dimensional data. We consider a dataset of 4,011 supernova spectra, augmented and rebalanced into three astrophysically motivated classes: normal Type Ia, other Type Ia subtypes, and core-collapse supernovae. Spectra are encoded as line-plot images using direct flux-wavelength visualizations as well as alternative flux-difference versus wavelength-difference heatmap representations designed to emphasize differential spectral structure. We evaluate several pre-trained Transformer architectures, including Vision Transformers (ViT), Swin Transformers, and a DINOv3-pretrained ViT, and examine the effects of fine-tuning depth, visual representation, and hyperparameter choices. We find that a plain flux-wavelength line plot outperforms the purpose-built maps on average, though log-scale maps remain competitive for specific architecture and fine-tuning combinations. On the held-out test set, our best model (\texttt{vitb-p16}) reaches a macro-F1 score of 86.3%, with per-class F1 scores of 94% for normal Ia, 77% for other Ia subtypes, and 88% for core-collapse supernovae. The dominant misclassification arises from confusion between Ia-91T and normal Ia spectra. These results demonstrate that Transformer-based vision models can provide competitive performance for single-spectrum supernova spectral classification.

astro-ph.IM↗

arXiv2026

A search for new symbiotic stars in the Milky Way: Using machine learning techniques applied to photometric databases

Symbiotic stars (SySts) are interacting binaries composed of a red giant transferring material to a hot compact star, typically a white dwarf. Although only about 300 systems are confirmed, the Galactic population is estimated at 1.2 x 10^3 - 1.5 x 10^4, indicating that most remain undiscovered. We identify new SySts using a machine-learning approach that combines Gaia DR3, 2MASS, and WISE photometry, parallaxes, and the pseudo-equivalent width of H alpha. A Random Forest model was trained on 166 confirmed S-type SySts and 1600 non-symbiotic stars, applying SMOTE to mitigate class imbalance. The model achieved an F1-score of 89% for the symbiotic class. Applied to 2.5 x 10^6 color-selected sources, it identified 990 candidates with probabilities more than 70%. We further refined the sample using physically motivated cuts on effective temperature, surface gravity, metallicity, and SkyMapper photometry, yielding 12 high-confidence candidates. These objects show cool temperatures, low surface gravities, near-solar metallicity, H alpha emission, moderate-to-high luminosities, and UV excess consistent with S-type SySts. Validation on recently confirmed systems recovered 92.3%, demonstrating the robustness and generalizability of our method.

astro-ph.SR↗

arXiv2025

The role of bars in triggering active galactic nuclei galaxies

Bars are considered an efficient mechanism for transporting gas toward the central regions of galaxies, potentially enhancing nuclear activity. However, the extent to which bars influence active galactic nuclei (AGNs), and whether their efficiency varies with environment, remain open questions. In this study, we aim to quantify the role of bars in triggering AGNs by comparing the AGN fraction in barred and non-barred galaxies across different environments. We constructed a sample from the Galaxy Zoo DECaLS catalog, ensuring a control selection where both samples share similar distributions in stellar mass, redshift, magnitude, concentration index, and local density parameter. AGNs were identified using spectroscopic data from the Sloan Digital Sky Survey, yielding 1330 barred AGNs and 1651 unbarred AGNs. We use the [OIII]5007 luminosity (Lum[OIII]) and the accretion rate parameter R as indicators of nuclear activity. Based on these, we applied criteria to distinguish powerful from weak AGNs, allowing a more precise assessment of the bar's impact on the supermassive black hole. Our analysis reveals that barred galaxies tend to host a higher fraction of powerful AGNs. From Lum[OIII], we find that more active nuclei reside in massive, blue galaxies with young stellar populations. We also observe a slight tendency for barred galaxies to host less massive black holes accreting more efficiently. The classification of strong and weak bars shows that more prominent bars correlate with higher nuclear activity. While this trend shows no significant differences in intermediate-density environments, it becomes evident in both low- and high-density regions, where galaxies with strong bars show enhanced AGN activity.

astro-ph.GA↗