1) La descarga del recurso depende de la página de origen
2) Para poder descargar el recurso, es necesario ser usuario registrado en Universia

Opción 1: Descargar recurso

Detalles del recurso


The search for fast optical transients, such as the expected electromagnetic counterparts to binary neutron star mergers, is riddled with false positives (FPs) ranging from asteroids to stellar flares. While moving objects are readily rejected via image pairs separated by ~1 hr, stellar flares represent a challenging foreground, significantly outnumbering rapidly evolving explosions. Identifying stellar sources close to and fainter than the transient detection limit can eliminate these FPs. Here, we present a method to reliably identify stars in deep co-adds of Palomar Transient Factory (PTF) imaging. Our machine-learning methodology utilizes the random forest (RF) algorithm, which is trained using > 3 x 10^6 sources with Sloan Digital Sky Survey (SDSS) spectra. When evaluated on an independent test set, the PTF RF model outperforms the SExtractor star classifier by ~4%. For faint sources (r' ≥ 21 mag), which dominate the field population, the PTF RF model produces a ~19% improvement over SExtractor. To avoid false negatives in the PTF transient-candidate stream, we adopt a conservative stellar classification threshold, corresponding to a galaxy misclassification rate of 0.005. Ultimately, 1.70 x 10^8 objects are included in our PTF point-source catalog, of which only ~10^6 are expected to be galaxies. We demonstrate that the PTF RF catalog reveals transients that otherwise would have been missed. To leverage its superior image quality, we additionally create an SDSS point-source catalog, which is also tuned to have a galaxy misclassification rate of 0.005. These catalogs have been incorporated into the PTF real-time pipelines to automatically reject stellar sources as non-extragalactic transients.

Pertenece a

Caltech Authors  


Miller, A. A. -  Kulkarni, M. K. -  Cao, Y. -  Laher, R. R. -  Masci, F. J. -  Surace, J. A. - 

Id.: 69583247

Versión: 1.0

Estado: Final

Tipo:  application/pdf - 

Tipo de recurso: Article  -  PeerReviewed  - 

Tipo de Interactividad: Expositivo

Nivel de Interactividad: muy bajo

Audiencia: Estudiante  -  Profesor  -  Autor  - 

Estructura: Atomic

Coste: no

Copyright: sí

Formatos:  application/pdf - 

Requerimientos técnicos:  Browser: Any - 

Relación: [References] http://resolver.caltech.edu/CaltechAUTHORS:20170127-151845228
[References] https://authors.library.caltech.edu/73792/

Fecha de contribución: 23-nov-2017


* Miller, A. A. and Kulkarni, M. K. and Cao, Y. and Laher, R. R. and Masci, F. J. and Surace, J. A. (2017) Preparing for Advanced LIGO: A Star–Galaxy Separation Catalog for the Palomar Transient Factory. Astronomical Journal, 153 (2). Art. No. 73. ISSN 0004-6256. http://resolver.caltech.edu/CaltechAUTHORS:20170127-151845228

Otros recursos de la mismacolección

  1. The Gaia-ESO Survey and CSI 2264: Substructures, disks, and sequential star formation in the young open cluster NGC 2264 Context. Reconstructing the structure and history of young clusters is pivotal to understanding the ...
  2. The Dynamics of Mesoscale Winds in the Upper Troposphere and Lower Stratosphere Spectral analysis is applied to infer the dynamics of mesoscale winds from aircraft observations in ...
  3. Baroclinic Instability in the Presence of Convection Baroclinic mixed-layer instabilities have recently been recognized as an important source of submeso...
  4. The role of mixed-layer instabilities in submesoscale turbulence Upper-ocean turbulence at scales smaller than the mesoscale is believed to exchange surface and ther...
  5. Seasonality in submesoscale turbulence Although the strongest ocean surface currents occur at horizontal scales of order 100 km, recent num...

Aviso de cookies: Usamos cookies propias y de terceros para mejorar nuestros servicios, para análisis estadístico y para mostrarle publicidad. Si continua navegando consideramos que acepta su uso en los términos establecidos en la Política de cookies.