377 citations · 3k across the 52 of their papers we have counts for
48 papers
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…
Electron-capture Supernova Candidates from Light Curves: Implications for Their Progenitors and Explosion Properties
Masato Sato, Nozomu Tominaga, Sergei I. Blinnikov +5
Core-collapse supernovae are explosions of massive stars. While most massive stars end as iron-core-collapse supernovae, less massive stars are expected to explode as electron-capt…
Young Stellar Objects in the Carina nebula: Near-Infrared variability and spectroscopy
Jura Borissova, Radostin Kurtev, Josemanuel Escobar +10
We present a catalog of 652 young stellar objects (YSOs) in the Carina star-forming region. The catalog was constructed by combining near-infrared Ks-band variability from the VIST…
Temporal Stamp Classifier: Classifying Short Sequences of Astronomical Alerts
Daniel Neira O., Pablo A. Estévez, Francisco Förster
In this work, we propose a deep learning-based classification model of astronomical objects using alerts reported by the Zwicky Transient Facility (ZTF) survey. The model takes as…
Physical Properties of Type II Supernovae Inferred from ZTF and ATLAS Photometric Data
Javier Silva-Farfán, Francisco Förster, Takashi J. Moriya +6
We report an analysis of a sample of 186 spectroscopically confirmed Type II supernova (SN) light curves (LCs) obtained from a combination of Zwicky Transient Facility (ZTF) and As…
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,…
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…
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…
Deep Attention-Based Supernovae Classification of Multi-Band Light-Curves
Óscar Pimentel, Pablo A. Estévez, Francisco Förster
In astronomical surveys, such as the Zwicky Transient Facility, supernovae (SNe) are relatively uncommon objects compared to other classes of variable events. Along with this scarc…
Type II supernovae from the Carnegie Supernova Project-I. I. Bolometric light curves of 74 SNe II using uBgVriYJH photometry
L. Martinez, M. C. Bersten, J. P. Anderson +18
The present study is the first of a series of three papers where we characterise the type II supernovae (SNe~II) from the Carnegie Supernova Project-I to understand their diversity…
Carnegie Supernova Project: The First Homogeneous Sample of "Super-Chandrasekhar Mass"/2003fg-like Type Ia Supernova
C. Ashall, J. Lu, E. Y. Hsiao +38
We present a multi-wavelength photometric and spectroscopic analysis of thirteen "Super-Chandrasekhar Mass"/2003fg-like type Ia Supernova (SNe~Ia). Nine of these objects were obser…
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…
The early discovery of SN 2017ahn: signatures of persistent interaction in a fast declining Type II supernova
L. Tartaglia, D. J. Sand, J. H. Groh +25
We present high-cadence, comprehensive data on the nearby () Type II SN 2017ahn, discovered within 1 day of explosion, from the very early phases after e…
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…
Studying Type II supernovae as cosmological standard candles using the Dark Energy Survey
T. de Jaeger, L. Galbany, S. González-Gaitán +85
Despite vast improvements in the measurement of the cosmological parameters, the nature of dark energy and an accurate value of the Hubble constant (H) in the Hubble-Lemaître l…
Design and operation of the ATLAS Transient Science Server
K. W. Smith, S. J. Smartt, D. R. Young +19
The Asteroid Terrestrial impact Last Alert System (ATLAS) system consists of two 0.5m Schmidt telescopes with cameras covering 29 square degrees at plate scale of 1.86 arcsec per p…
Introducing the Search for Intermediate-mass Black-hole In Nearby Galaxy (SIBLING) Survey
Jorge Martínez-Palomera, Paulina Lira, India Bhalla-Ladd +2
Intermediate mass black holes (IMBHs) have masses between the M and are key to our understanding of the formation of massive black holes. The known populati…
Enabling real-time multi-messenger astrophysics discoveries with deep learning
E. A. Huerta, Gabrielle Allen, Igor Andreoni +57
Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that…
Luminous Type II supernovae for their low expansion velocities
Ó. Rodríguez, G. Pignata, J. P. Anderson +27
We present optical and near-IR data of three Type II supernovae (SNe II), SN 2008bm, SN 2009aj, and SN 2009au. These SNe display the following common characteristics: signs of earl…
Fast algorithms for slow moving asteroids: constraints on the distribution of Kuiper Belt Objects
Peter J. Whidden, J. Bryce Kalmbach, Andrew J. Connolly +11
We introduce a new computational technique for searching for faint moving sources in astronomical images. Starting from a maximum likelihood estimate for the probability of the det…
Carnegie Supernova Project-II: The Near-infrared Spectroscopy Program
E. Y. Hsiao, M. M. Phillips, G. H. Marion +42
Shifting the focus of Type Ia supernova (SN Ia) cosmology to the near-infrared (NIR) is a promising way to significantly reduce the systematic errors, as the strategy minimizes our…
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…