38 citations · 47 across the 9 of their papers we have counts for
9 papers · 1 filter
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…
Multivariate time series transformer embeddings for light curves of periodic variable stars
Gabriel Chiong, Ignacio Becker, Pavlos Protopapas
Astronomical surveys produce time series data by observing stellar objects across multiple photometry bands. Foundational transformer-based models, such as Astromer, encode each ti…
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…
Astromer 2
Cristobal Donoso-Oliva, Ignacio Becker, Pavlos Protopapas +3
Foundational models have emerged as a powerful paradigm in deep learning field, leveraging their capacity to learn robust representations from large-scale datasets and effectively…
Multiband Embeddings of Light Curves
I. Becker, P. Protopapas, M. Catelan +1
In this work, we propose a novel ensemble of recurrent neural networks (RNNs) that considers the multiband and non-uniform cadence without having to compute complex features. Our p…
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…
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…