38 citations · 58 across the 15 of their papers we have counts for
15 papers
AGNFormer I: Reconstruction of AGN spectra using a probabilistic transformer model
Benedict L. Rouse, Franz E. Bauer, Tomasz Różański +8
We explore how an uncertainty-aware transformer-based architecture can leverage information embedded across the entire observed optical spectra of AGN, focusing on the algorithm's…
Querying an astronomical database using large language models: the ALeRCE text-to-SQL system
P. A. Estevez, J. Espejo-Moreira, S. Sanfeliu-Alvarez +10
We develop a text-to-SQL (structured query language) system based on large language models (LLMs) using in-context learning and apply it to the Automatic Learning for the Rapid Cla…
ASTROCO: Self-Supervised Conformer-Style Transformers for Light-Curve Embeddings
Antony Tan, Pavlos Protopapas, Martina Cádiz-Leyton +3
We present AstroCo, a Conformer-style encoder for irregular stellar light curves. By combining attention with depthwise convolutions and gating, AstroCo captures both global depend…
Astro-MoE: Mixture of Experts for Multiband Astronomical Time Series
Martina Cádiz-Leyton, Guillermo Cabrera-Vives, Pavlos Protopapas +2
Multiband astronomical time series exhibit heterogeneous variability patterns, sampling cadences, and signal characteristics across bands. Standard transformers apply shared parame…
Image-Based Multi-Survey Classification of Light Curves with a Pre-Trained Vision Transformer
Daniel Moreno-Cartagena, Guillermo Cabrera-Vives, Alejandra M. Muñoz Arancibia +10
We explore the use of Swin Transformer V2, a pre-trained vision Transformer, for photometric classification in a multi-survey setting by leveraging light curves from the Zwicky Tra…
Applying Vision Transformers on Spectral Analysis of Astronomical Objects
Luis Felipe Strano Moraes, Ignacio Becker, Pavlos Protopapas +1
We apply pre-trained Vision Transformers (ViTs), originally developed for image recognition, to the analysis of astronomical spectral data. By converting traditional one-dimensiona…
Leveraging pre-trained vision Transformers for multi-band photometric light curve classification
Daniel Moreno-Cartagena, Pavlos Protopapas, Guillermo Cabrera-Vives +3
This study investigates the potential of a pre-trained vision Transformer (VT) model, specifically the Swin Transformer V2 (SwinV2), to classify photometric light curves without th…
Uncertainty estimation for time series classification: Exploring predictive uncertainty in transformer-based models for variable stars
Martina Cádiz-Leyton, Guillermo Cabrera-Vives, Pavlos Protopapas +3
Classifying variable stars is key for understanding stellar evolution and galactic dynamics. With the demands of large astronomical surveys, machine learning models, especially att…
Transformer-Based Astronomical Time Series Model with Uncertainty Estimation for Detecting Misclassified Instances
Martina Cádiz-Leyton, Guillermo Cabrera-Vives, Pavlos Protopapas +2
In this work, we present a framework for estimating and evaluating uncertainty in deep-attention-based classifiers for light curves for variable stars. We implemented three techniq…
Domain Adaptation via Minimax Entropy for Real/Bogus Classification of Astronomical Alerts
Guillermo Cabrera-Vives, César Bolivar, Francisco Förster +3
Time domain astronomy is advancing towards the analysis of multiple massive datasets in real time, prompting the development of multi-stream machine learning models. In this work,…
Positional Encodings for Light Curve Transformers: Playing with Positions and Attention
Daniel Moreno-Cartagena, Guillermo Cabrera-Vives, Pavlos Protopapas +3
We conducted empirical experiments to assess the transferability of a light curve transformer to datasets with different cadences and magnitude distributions using various position…
Multi-Class Deep SVDD: Anomaly Detection Approach in Astronomy with Distinct Inlier Categories
Manuel Pérez-Carrasco, Guillermo Cabrera-Vives, Lorena Hernández-García +8
With the increasing volume of astronomical data generated by modern survey telescopes, automated pipelines and machine learning techniques have become crucial for analyzing and ext…
ASTROMER: A transformer-based embedding for the representation of light curves
C. Donoso-Oliva, I. Becker, P. Protopapas +3
Taking inspiration from natural language embeddings, we present ASTROMER, a transformer-based model to create representations of light curves. ASTROMER was pre-trained in a self-su…
ConDA: Simplifying Semi-supervised Domain Adaptation by Learning Consistent and Contrastive Feature Representations
Manuel Pérez-Carrasco, Pavlos Protopapas, Guillermo Cabrera-Vives
In this work, we present ConDA, a simple framework that extends recent advances in semi-supervised learning to the semi-supervised domain adaptation (SSDA) problem. Our frame…
Matching Embeddings for Domain Adaptation
Manuel Pérez-Carrasco, Guillermo Cabrera-Vives, Pavlos Protopapas +2
In this work we address the problem of transferring knowledge obtained from a vast annotated source domain to a low labeled target domain. We propose Adversarial Variational Domain…