Browsing by Author "Huijse, P."

Now showing 1 - 9 of 9
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    A self-regulated convolutional neural network for classifying variable stars
    (2025) Pérez Galarce, Francisco Javier; Martínez-Palomera, J.; Pichara Baksai, Karim Elías; Huijse, P.; Catelan, Márcio
    Over the last two decades, machine learning models have been widely applied and have proven effective in classifying variable stars, particularly with the adoption of deep learning architectures such as convolutional neural networks, recurrent neural networks, and transformer models. While these models have achieved high accuracy, they require high-quality, representative data and a large number of labelled samples for each star type to generalise well, which can be challenging in time-domain surveys. This challenge often leads to models learning and reinforcing biases inherent in the training data, an issue that is not easily detectable when validation is performed on subsamples from the same catalogue. The problem of biases in variable star data has been largely overlooked, and a definitive solution has yet to be established. In this paper, we propose a new approach to improve the reliability of classifiers in variable star classification by introducing a self-regulated training process. This process utilises synthetic samples generated by a physics-enhanced latent space variational autoencoder, incorporating six physical parameters from Gaia Data Release 3. Our method features a dynamic interaction between a classifier and a generative model, where the generative model produces ad-hoc synthetic light curves to reduce confusion during classifier training and populate underrepresented regions in the physical parameter space. Experiments conducted under various scenarios demonstrate that our self-regulated training approach outperforms traditional training methods for classifying variable stars on biased datasets, showing statistically significant improvements.
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    ALeRCE light curve classifier: Tidal disruption event expansion pack
    (2025) Pavez Herrera, M.; Sánchez Sáez, P.; Hernández García, L.; Bauer, F. E.; Förster, F.; Catelan, Márcio; Muñoz Arancibia, A.; Ricci, C.; Reyes Jainaga, I.; Bayo, A.; Huijse, P.; Cabrera Vives, G.
    Context. ALeRCE (Automatic Learning for the Rapid Classification of Events) is currently processing the Zwicky Transient Facility (ZTF) alert stream, in preparation for the Vera C. Rubin Observatory, and classifying objects using a broad taxonomy. The ALeRCE light curve classifier is a balanced random forest (BRF) algorithm with a two-level scheme that uses variability features computed from the ZTF alert stream, and colors obtained from AllWISE and ZTF photometry. Aims. This work develops an updated version of the ALeRCE broker light curve classifier that includes tidal disruption events TDEs) as a new subclass. For this purpose we incorporated 24 new features, notably including the distance to the nearest source detected in ZTF science images and a parametric model of the power-law decay for transients. We also expanded the labeled set to include 219 792 spectroscopically classified sources, including 60 TDEs. Methods. To effectively integrate TDEs into the ALeRCE’s taxonomy, we identified specific characteristics that set them apart from other transient classes, such as their central position in a galaxy, the typical decay pattern displayed when fully disrupted, and the lack of color variability after disruption. Based on these attributes, we developed features to distinguish TDEs from other transient events. Results. The modified classifier can distinguish between a broad range of classes with a better performance compared to the previous version and it can integate the TDE class achieving 91% recall, also identifying a large number of potential TDE candidates in ZTF alert stream unlabeled data.
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    Alert Classification for the ALeRCE Broker System: The Light Curve Classifier
    (2021) Sánchez-Sáez, P.; Reyes, I.; Valenzuela, C.; Förster, F.; Eyheramendy, S.; Elorrieta, F.; Bauer, F. E.; Cabrera-Vives, G.; Estévez, P. A.; Catelan, Márcio; Pignata, G.; Huijse, P.; De Cicco, D.; Arévalo, P.; Carrasco-Davis, R.; Abril, J.; Kurtev, R.; Borissova, J.; Arredondo, J.; Castillo-Navarrete, E.; Rodríguez, D.; Ruz-Mieres, D.; Moya, A.; Sabatini-Gacitúa, L.; Sepúlveda-Cobo, C.; Camacho-Iñiguez, E.
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    An Information Theory Approach on Deciding Spectroscopic Follow-ups
    (2020) Astudillo, J.; Protopapas, P.; Pichara Baksai, Karim Elías; Huijse, P.
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    Informative Bayesian model selection for RR Lyrae star classifiers
    (2021) Pérez-Galarce, F.; Pichara, K.; Huijse, P.; Catelan, M.; Mery Quiroz, Domingo Arturo
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    Persistent and occasional: Searching for the variable population of the ZTF/4MOST sky using ZTF Data Release 11
    (2023) Sánchez-Sáez, P.; Arredondo, J.; Bayo, A.; Arévalo, P.; Bauer, F. E.; Cabrera-Vives, G.; Catelan, Márcio; Coppi, P.; Estévez, P. A.; Förster, F.; Hernández-García, L.; Huijse, P.; Kurtev, R.; Lira, P.; Muñoz Arancibia. A. M.; Pignata, G.
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    Proper motions in the VVV Survey: Results for more than 15 million stars across NGC 6544
    (2017) Contreras Ramos, Rodrigo Andrés; Zoccali, Manuela; Rojas, F.; Rojas Arriagada, A.; Gárate, M.; Huijse, P.; Gran, F.; Valcarce Bravo, Aldo Alfonso Raúl; Estévez, P. A.; Minniti, D.
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    The delay of shock breakout due to circumstellar material evident in most type II supernovae
    (2018) Forster, F.; Moriya, T. J.; Maureira, J. C.; Anderson, J. P.; Blinnikov, S.; Bufano, F.; Cabrera Vives, G.; Clocchiatti, Alejandro; De Jaeger, T.; Estevez, P. A.; Galbany, L.; González -Gaitán, S.; Grafener, G.; Hamuy, M.; Hsiao, E. Y.; Huentelemu, P.; Huijse, P.; Kuncarayakti, H.; Martínez, J.; Medina, G.; Olivares, F.; Pignata, Giuliano; Razza, A.; Reyes, I.; San Martín, J.; Smith, R. C.; Vera, E.; Vivas, A. K.; Postigo, A. D.; Yoon, S. C.; Ashall, C.; Fraser, M.; Gal-Yam, A.; Kankare, E.; Le Guillou, L.; Mazzali, P. A.; Walton, N. A.; Young, D. R.
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    Transient Classification Report for 2020-12-01
    (2020) Dodin, A.; Tsvetkov, D.; Shatski, N.; Belinski, A.; Galbany, L.; Munoz-Arancibia, A.; Forster, F.; Bauer, F. E.; Hernandez-Garcia, L.; Pignata, G.; Camacho, E.; Silva-Farfan, J.; Mourao, A.; Arredondo, J.; Cabrera-Vives, G.; Carrasco-Davis, R.; Estevez, P. A.; Huijse, P.; Reyes, E.; Reyes, I.; Sanchez-Saez, P.; Valenzuela, C.; Castillo, E.; Ruz-Mieres, D.; Rodriguez-Mancini, D.; Catelan, Marcio; Eyheramendy, S.; Graham, M. J.
    F. Forster, F.E. Bauer, G. Pignata, J. Arredondo, G. Cabrera-Vives, R. Carrasco-Davis, P.A. Estevez, P. Huijse, E. Reyes, I. Reyes, P. Sanchez-Saez, C. Valenzuela, E. Castillo, D. Ruz-Mieres, D. Rodriguez-Mancini, F.E. Bauer, M. Catelan, S. Eyheramendy, M.J. Graham on behalf of the ALeRCE broker report/s the discovery of a new astronomical transient.