Browsing by Author "Catelan, Márcio"

Now showing 1 - 20 of 257
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    A Data-driven Study of RR Lyrae Near-IR Light Curves: Principal Component Analysis, Robust Fits, and Metallicity Estimates
    (2018) Hajdu, Gergely; Dekany, Istvan; Catelan, Márcio; Grebel, Eva K.; Jurcsik, Johanna
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    A deep near-infrared view of the Galactic globular cluster 2 MASS GC 02
    (2007) Borissova, J.; Catelan, Márcio; Minniti, D.
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    A fork in the Sagittarius trailing debris
    (2017) Navarrete Silva, Camila Andrea; Belokurov, V; Koposov, S; Irwin, M; Catelan, Márcio; Duffau, S; Drake, A
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    A HOT HORIZONTAL BRANCH STAR WITH A CLOSE K-TYPE MAIN-SEQUENCE COMPANION
    (2015) Bidin, C. Moni; Momany, Y.; Montalto, M.; Catelan, Márcio; Villanova, S.; Piotto, G.; Geisler, D.
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    A machine learned classifier for RR Lyrae in the VVV survey
    (2016) Elorrieta López, Felipe; Eyheramendy Duerr, Susana; Jordán Colzani, Andrés Cristóbal; Dekany, Istvan; Catelan, Márcio; Angeloni, Rodolfo; Alonso, J.; Contreras, R.; Gran, F.; Hajdu, G.; Espinoza, N.; Saito, R.; Minniti, D.
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    A misidentified RR Lyrae variable star in omega Centauri
    (2013) Navarrete Silva, Camila Andrea; Catelan, Márcio; Contreras Ramos, Rodrigo Andrés; Alonso Garcia, J.; Dekany, Istvan; Gran Merino, Felipe Eduardo; Hempel, Maren; Angeloni, Rodolfo
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    A near-infrared catalogue of the Galactic novae in the VVV survey area
    (2013) Kalbusch Saito, Roberto; Minniti, D.; Angeloni, Rodolfo; Catelan, Márcio; Beamin, J. C.; Borissova, J.; Dekany, Istvan; Kerins, E.; Kurtev, R.; Mennickent, R. E.
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    A Near-infrared RR Lyrae Census along the Southern Galactic Plane: The Milky Way's Stellar Fossil Brought to Light
    (2018) Dekany, Istvan; Hajdu, Gergely; Grebel, Eva K.; Catelan, Márcio; Elorrieta López, Felipe; Eyheramendy Duerr, Susana; Majaess, Daniel; Jordán Colzani, Andrés Cristóbal
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    A New Lower Main Sequence Eclipsing Binary with Detached Components
    (2009) Rozyczka, M.; Pietrukowicz, Pawel; Catelan, Márcio
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    A search for dormant binaries with degenerate components in omega Centauri and NGC 6397
    (2010) Rozyczka, M.; Pietrukowicz, Pawel; Catelan, Márcio
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    A search for substructures near the Galactic plane and bulge using RR Lyrae stars as tracers
    (2024) Cristi Cambiaso, Nicolas Eduardo; Catelan, Márcio; Zoccali, Manuela; Pontificia Universidad Catolica de Chile. Faculty of Physics. Institute of Astrophysics
    Usando estrellas pulsantes de tipo RR Lyrae, hacemos una búsqueda de subestructuras Galácticas ("stellar streams" y cúmulos globulares) en la región del plano Galáctico. Construimos una muestra de estrellas RR Lyrae con valores de distancia y metalicidades. Estudiamos esta muestra con un algoritmo de "clustering" para encontrar grupos de estrellas cercanas y con propiedades similares. Mediante esta búsqueda, recuperamos estructuras conocidas y encontramos posibles nuevas estructuras.
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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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    A semi-empirical study of the mass distribution of horizontal branch stars in M 3 (NGC 5272)
    (2008) Valcarce Bravo, Aldo Alfonso Raúl; Catelan, Márcio
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    A tale of three cataclysmic variables with distinct superhumps
    (2025) Joshi, Arti; Tappert, Claus; Catelan, Márcio; Schmidtobreick, Linda; Singh, Mridweeka
    Superhumps are among the most commonly observed variable features in the light curves of cataclysmic variables (CVs). To study the superhump behaviour of CVs, we present Transiting Exoplanet Survey Satellite (TESS) observations of three CVs: CRTS J110014.7+131552, SDSS J093537.46+161950.8, and [PK2008] HalphaJ130559. Among them, a super-outburst has been observed in CRTS J110014.7+131552, which is associated with the precursor outburst, where prominent superhumps have been observed during maximum of the outburst with a mean period of 0.06786(1) d. We observed variations in the superhump period, along with changes in the shape of the light curve profile and the amplitude of the superhumps during different phases of the outburst, indicating disc-radius variation as well as periodically variable dissipation at the accretion stream’s bright spot. The data on SDSS J093537.46+161950.8 reveal previously unknown variations modulated with periods of 0.06584(2) d and 2.36(2) d, related to the positive superhump and the disc-precession periods, respectively, which can reasonably be interpreted as a result of the prograde precession of an eccentric accretion disc. Despite its short orbital period, the lack of outburst activity, its stable long-term brightness, discovery spectrum, and absolute magnitude suggest that the object might not be an SU UMa type dwarf nova. Instead, it could belong to the group of high-mass-transfer CVs below the period gap: either a rare class of nova-like variables or a high-luminosity intermediate polar, a subclass of magnetic CVs. For [PK2008] HalphaJ130559, a new average orbital period of 0.15092(1) d has been identified. Additionally, this system displays previously undetected average periods of 0.14517(3) d and 3.83(1) d, which could be provisionally identified as negative superhump and disc-precession periods, respectively. If the identified simultaneous signals do indeed reflect negative superhump and disc-precession period variations, then their origin might be associated with the retrograde precession of a tilted disc and its interaction with the secondary stream.
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    A VIMOS spectroscopy study of photometric variables and straggler candidates in omega Centauri
    (2012) Rozyczka, M.; Pietrukowicz, Pawel; Catelan, Márcio
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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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    Alert Classification for the ALeRCE Broker System: The Real-time Stamp Classifier
    (2021) Carrasco-Davis, R.; Reyes, E.; Valenzuela, C.; Förster, F.; Estévez, P. A.; Pignata, G.; Bauer, F. E.; Reyes, I.; Sánchez-Sáez, P.; Cabrera-Vives, G.; Eyheramendy, S.; Catelan, Márcio; Arredondo, J.; Castillo-Navarrete, E.; Rodríguez-Mancini, D.; Ruz-Mieres, D.; Moya, A.; Sabatini-Gacitúa, L.; Sepúlveda-Cobo, C.; Mahabal, A. A.; Silva-Farfán, J.; Camacho-Iñiguez, E.; Galbany, L.
    We present a real-time stamp classifier of astronomical events for the Automatic Learning for the Rapid Classification of Events broker, ALeRCE. The classifier is based on a convolutional neural network, trained on alerts ingested from the Zwicky Transient Facility (ZTF). Using only the science, reference, and difference images of the first detection as inputs, along with the metadata of the alert as features, the classifier is able to correctly classify alerts from active galactic nuclei, supernovae (SNe), variable stars, asteroids, and bogus classes, with high accuracy (~94%) in a balanced test set. In order to find and analyze SN candidates selected by our classifier from the ZTF alert stream, we designed and deployed a visualization tool called SN Hunter, where relevant information about each possible SN is displayed for the experts to choose among candidates to report to the Transient Name Server database. From 2019 June 26 to 2021 February 28, we have reported 6846 SN candidates to date (11.8 candidates per day on average), of which 971 have been confirmed spectroscopically. Our ability to report objects using only a single detection means that 70% of the reported SNe occurred within one day after the first detection. ALeRCE has only reported candidates not otherwise detected or selected by other groups, therefore adding new early transients to the bulk of objects available for early follow-up. Our work represents an important milestone toward rapid alert classifications with the next generation of large etendue telescopes, such as the Vera C. Rubin Observatory....
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    An Algorithm for the Visualization of Relevant Patterns in Astronomical Light Curves
    (2019) Pieringer Baeza, Christian Philip; Pichara Baksai, Karim Elías; Catelan, Márcio; Protopapas, Pavlos