196 citations · 1.1k across the 19 of their papers we have counts for
18 papers
ALeRCE light curve classifier: Tidal disruption event expansion pack
M. Pavez-Herrera, P. Sánchez-Sáez, L. Hernández-García +9
ALeRCE (Automatic Learning for the Rapid Classification of Events) processes the Zwicky Transient Facility (ZTF) alert stream in preparation for the Vera C. Rubin Observatory, clas…
A Novel Optimal Transport-Based Approach for Interpolating Spectral Time Series: Paving the Way for Photometric Classification of Supernovae
M. Ramirez, G. Pignata, Francisco Förster +7
This paper introduces a novel method for creating spectral time series, which can be used for generating synthetic light curves for photometric classification but also for applicat…
ATAT: Astronomical Transformer for time series And Tabular data
G. Cabrera-Vives, D. Moreno-Cartagena, N. Astorga +14
The advent of next-generation survey instruments, such as the Vera C. Rubin Observatory and its Legacy Survey of Space and Time (LSST), is opening a window for new research in time…
Persistent and occasional: searching for the variable population of the ZTF/4MOST sky using ZTF data release 11
P. Sánchez-Sáez, J. Arredondo, A. Bayo +13
We present a variability, color and morphology based classifier, designed to identify transients, persistently variable, and non-variable sources, from the Zwicky Transient Facilit…
Multi-scale stamps for real-time classification of alert streams
Ignacio Reyes-Jainaga, Francisco Förster, Alejandra M. Muñoz Arancibia +15
In recent years, automatic classifiers of image cutouts (also called "stamps") have shown to be key for fast supernova discovery. The Vera C. Rubin Observatory will distribute abou…
DELIGHT: Deep Learning Identification of Galaxy Hosts of Transients using Multi-resolution Images
Francisco Förster, Alejandra M. Muñoz Arancibia, Ignacio Reyes +25
We present DELIGHT, or Deep Learning Identification of Galaxy Hosts of Transients, a new algorithm designed to automatically and in real-time identify the host galaxies of extragal…
Searching for changing-state AGNs in massive datasets -- I: applying deep learning and anomaly detection techniques to find AGNs with anomalous variability behaviours
P. Sánchez-Sáez, H. Lira, L. Martí +14
The classic classification scheme for Active Galactic Nuclei (AGNs) was recently challenged by the discovery of the so-called changing-state (changing-look) AGNs (CSAGNs). The phys…
The effect of phased recurrent units in the classification of multiple catalogs of astronomical lightcurves
C. Donoso-Oliva, G. Cabrera-Vives, P. Protopapas +2
In the new era of very large telescopes, where data is crucial to expand scientific knowledge, we have witnessed many deep learning applications for the automatic classification of…
The Automatic Learning for the Rapid Classification of Events (ALeRCE) Alert Broker
F. Förster, G. Cabrera-Vives, E. Castillo-Navarrete +37
We introduce the Automatic Learning for the Rapid Classification of Events (ALeRCE) broker, an astronomical alert broker designed to provide a rapid and self--consistent classifica…
Alert Classification for the ALeRCE Broker System: The Light Curve Classifier
P. Sánchez-Sáez, I. Reyes, C. Valenzuela +23
We present the first version of the ALeRCE (Automatic Learning for the Rapid Classification of Events) broker light curve classifier. ALeRCE is currently processing the Zwicky Tran…
Alert Classification for the ALeRCE Broker System: The Real-time Stamp Classifier
Rodrigo Carrasco-Davis, Esteban Reyes, Camilo Valenzuela +20
We present a real-time stamp classifier of astronomical events for the ALeRCE (Automatic Learning for the Rapid Classification of Events) broker. The classifier is based on a convo…
Asteroids' Size Distribution and Colors from HiTS
J. Peña, C. Fuentes, F. Förster +8
We report the observations of solar system objects during the 2015 campaign of the High cadence Transient Survey (HiTS). We found 5740 bodies (mostly Main Belt asteroids), 1203 of…
The delay of shock breakout due to circumstellar material seen in most Type II Supernovae
F. Förster, T. J. Moriya, J. C. Maureira +35
Type II supernovae (SNe) originate from the explosion of hydrogen-rich supergiant massive stars. Their first electromagnetic signature is the shock breakout, a short-lived phenomen…
The High Cadence Transient Survey (HITS): Compilation and characterization of light-curve catalogs
Jorge Martínez-Palomera, Francisco Förster, Pavlos Protopapas +12
The High Cadence Transient Survey (HiTS) aims to discover and study transient objects with characteristic timescales between hours and days, such as pulsating, eclipsing and explod…
Enhanced Rotational Invariant Convolutional Neural Network for Supernovae Detection
Esteban Reyes, Pablo A. Estévez, Ignacio Reyes +4
In this paper, we propose an enhanced CNN model for detecting supernovae (SNe). This is done by applying a new method for obtaining rotational invariance that exploits cyclic symme…
Deep Learning for Image Sequence Classification of Astronomical Events
Rodrigo Carrasco-Davis, Guillermo Cabrera-Vives, Francisco Förster +6
We propose a new sequential classification model for astronomical objects based on a recurrent convolutional neural network (RCNN) which uses sequences of images as inputs. This ap…
Asteroids in the High cadence Transient Survey
J. Peña, C. Fuentes, F. Förster +11
We report on the serendipitous observations of Solar System objects imaged during the High cadence Transient Survey (HiTS) 2014 observation campaign. Data from this high cadence, w…
Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection
Guillermo Cabrera-Vives, Ignacio Reyes, Francisco Förster +2
We introduce Deep-HiTS, a rotation invariant convolutional neural network (CNN) model for classifying images of transients candidates into artifacts or real sources for the High ca…