Abstract
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 classification of ...large etendue telescope alert streams, such as that provided by the Zwicky Transient Facility (ZTF) and, in the future, the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). ALeRCE is a Chilean-led broker run by an interdisciplinary team of astronomers and engineers working to become intermediaries between survey and follow-up facilities. ALeRCE uses a pipeline that includes the real-time ingestion, aggregation, cross-matching, machine-learning (ML) classification, and visualization of the ZTF alert stream. We use two classifiers: a stamp-based classifier, designed for rapid classification, and a light curve–based classifier, which uses the multiband flux evolution to achieve a more refined classification. We describe in detail our pipeline, data products, tools, and services, which are made public for the community (see
https://alerce.science
). Since we began operating our real-time ML classification of the ZTF alert stream in early 2019, we have grown a large community of active users around the globe. We describe our results to date, including the real-time processing of 1.5 × 10
8
alerts, the stamp classification of 3.4 × 10
7
objects, the light-curve classification of 1.1 × 10
6
objects, the report of 6162 supernova candidates, and different experiments using LSST-like alert streams. Finally, we discuss the challenges ahead in going from a single stream of alerts such as ZTF to a multistream ecosystem dominated by LSST.
Abstract
We present the first version of the Automatic Learning for the Rapid Classification of Events (ALeRCE) broker light curve classifier. ALeRCE is currently processing the Zwicky Transient ...Facility (ZTF) alert stream, in preparation for the Vera C. Rubin Observatory. The ALeRCE light curve classifier uses variability features computed from the ZTF alert stream and colors obtained from AllWISE and ZTF photometry. We apply a balanced random forest algorithm with a two-level scheme where the top level classifies each source as periodic, stochastic, or transient, and the bottom level further resolves each of these hierarchical classes among 15 total classes. This classifier corresponds to the first attempt to classify multiple classes of stochastic variables (including core- and host-dominated active galactic nuclei, blazars, young stellar objects, and cataclysmic variables) in addition to different classes of periodic and transient sources, using real data. We created a labeled set using various public catalogs (such as the Catalina Surveys and Gaia DR2 variable stars catalogs, and the Million Quasars catalog), and we classify all objects with ≥6
g
-band or ≥6
r
-band detections in ZTF (868,371 sources as of 2020 June 9), providing updated classifications for sources with new alerts every day. For the top level we obtain macro-averaged precision and recall scores of 0.96 and 0.99, respectively, and for the bottom level we obtain macro-averaged precision and recall scores of 0.57 and 0.76, respectively. Updated classifications from the light curve classifier can be found at the ALeRCE Explorer website (
http://alerce.online
).
Gaia Data Release 3 Montegriffo, P.; De Angeli, F.; Andrae, R. ...
Astronomy and astrophysics (Berlin),
06/2023, Letnik:
674
Journal Article
Recenzirano
Odprti dostop
Context.Gaia
Data Release 3 contains astrometry and photometry results for about 1.8 billion sources based on observations collected by the European Space Agency (ESA)
Gaia
satellite during the first ...34 months of its operational phase (the same period covered by
Gaia
early Data Release 3;
Gaia
EDR3). Low-resolution spectra for 220 million sources are one of the important new data products included in this release.
Aims.
In this paper, we focus on the external calibration of low-resolution spectroscopic content, describing the input data, algorithms, data processing, and the validation of the results. Particular attention is given to the quality of the data and to a number of features that users may need to take into account to make the best use of the catalogue.
Methods.
We calibrated an instrument model to relate mean
Gaia
spectra to the corresponding spectral energy distributions (SEDs) using an extended set of calibrators: this includes modelling of the instrument dispersion relation, transmission, and line spread functions. Optimisation of the model is achieved through total least-squares regression, accounting for errors in
Gaia
and external spectra.
Results.
The resulting instrument model can be used for forward modelling of
Gaia
spectra or for inverse modelling of externally calibrated spectra in absolute flux units.
Conclusions.
The absolute calibration derived in this paper provides an essential ingredient for users of
BP
/
RP
spectra. It allows users to connect
BP
/
RP
spectra to absolute fluxes and physical wavelengths.
Gaia Data Release 3 De Angeli, F.; Weiler, M.; Montegriffo, P. ...
Astronomy and astrophysics (Berlin),
06/2023, Letnik:
674
Journal Article
Recenzirano
Odprti dostop
Context.
Blue (BP) and Red (RP) Photometer low-resolution spectral data are one of the exciting new products in
Gaia
Data Release 3 (
Gaia
DR3). These data have also been used to derive astrometry ...and integrated photometry in
Gaia
Early Data Release 3 and astrophysical parameters and Solar System object reflectance spectra in
Gaia
DR3.
Aims.
In this paper, we give an overview of the processing techniques that allow raw satellite data of multiple transits per source to be converted into combined spectra calibrated to an internal reference system, resulting in low-resolution BP and RP mean spectra. We describe how we overcome challenges due to the complexity of the on-board instruments and to the various observation strategies. Furthermore, we show highlights from our scientific validation of the results. This work covers the internal calibration of BP/RP spectra to a self-consistent mean instrument, while the calibration of the BP/RP spectra to the absolute reference system of physical flux and wavelength is covered by one of the accompanying
Gaia
DR3 papers.
Methods.
We calibrate about 65 billion individual transit spectra onto the same mean BP/RP instrument through a series of calibration steps, including background subtraction, calibration of the CCD geometry, and an iterative procedure for the calibration of CCD efficiency as well as variations of the line-spread function and dispersion across the focal plane and in time. The calibrated transit spectra are then combined for each source in terms of an expansion into continuous basis functions. We discuss the configuration of these basis functions.
Results.
Time-averaged mean spectra covering the optical to near-infrared wavelength range 330, 1050 nm are published for approximately 220 million objects. Most of these are brighter than
G
= 17.65 but some BP/RP spectra are published for sources down to
G
= 21.43. Their signal-to-noise ratio (S/N) varies significantly over the wavelength range covered, and with magnitude and colour of the observed objects, with sources around
G
= 15 having a S/N above 100 in some wavelength ranges. The top-quality BP/RP spectra are achieved for sources with magnitudes 9 <
G
< 12, with S/N reaching 1000 in the central part of the RP wavelength range. Scientific validation suggests that the internal calibration was generally successful. However, there is some evidence for imperfect calibrations at the bright end
G
< 11, where calibrated BP/RP spectra can exhibit systematic flux variations that exceed their estimated flux uncertainties. We also report that, due to long-range noise correlations, BP/RP spectra can exhibit wiggles when sampled in pseudo-wavelength.
Conclusions.
The
Gaia
DR3 data products are the expansion coefficients and corresponding covariance matrices for BP and RP separately. Users are encouraged to work with the data in this format, with full covariance information showing that correlations between coefficients are typically very low. Documentation and instructions on how to access and use BP/RP spectral data from the archive are also provided.
Gaia Data Release 3 provides novel flux-calibrated low-resolution spectrophotometry for about 220 million sources in the wavelength range 330nm - 1050nm (XP spectra). Synthetic photometry directly ...tied to a flux in physical units can be obtained from these spectra for any passband fully enclosed in this wavelength range. We describe how synthetic photometry can be obtained from XP spectra, illustrating the performance that can be achieved under a range of different conditions - for example passband width and wavelength range - as well as the limits and the problems affecting it. Existing top-quality photometry can be reproduced within a few per cent over a wide range of magnitudes and colour, for wide and medium bands, and with up to millimag accuracy when synthetic photometry is standardised with respect to these external sources. Some examples of potential scientific application are presented, including the detection of multiple populations in globular clusters, the estimation of metallicity extended to the very metal-poor regime, and the classification of white dwarfs. A catalogue providing standardised photometry for ~220 million sources in several wide bands of widely used photometric systems is provided (Gaia Synthetic Photometry Catalogue; GSPC) as well as a catalogue of \(\simeq 10^5\) white dwarfs with DA/non-DA classification obtained with a Random Forest algorithm (Gaia Synthetic Photometry Catalogue for White Dwarfs; GSPC-WD).
Context. Gaia Data Release 3 contains astrometry and photometry results for about 1.8 billion sources based on observations collected by the European Space Agency (ESA) Gaia satellite during the ...first 34 months of its operational phase (the same period covered Gaia early Data Release 3; Gaia EDR3). Low-resolution spectra for 220 million sources are one of the important new data products included in this release. Aims. In this paper, we focus on the external calibration of low-resolution spectroscopic content, describing the input data, algorithms, data processing, and the validation of the results. Particular attention is given to the quality of the data and to a number of features that users may need to take into account to make the best use of the catalogue. Methods. We calibrated an instrument model to relate mean Gaia spectra to the corresponding spectral energy distributions using an extended set of calibrators: this includes modelling of the instrument dispersion relation, transmission, and line spread functions. Optimisation of the model is achieved through total least-squares regression, accounting for errors in Gaia and external spectra. Results. The resulting instrument model can be used for forward modelling of Gaia spectra or for inverse modelling of externally calibrated spectra in absolute flux units. Conclusions. The absolute calibration derived in this paper provides an essential ingredient for users of BP/RP spectra. It allows users to connect BP/RP spectra to absolute fluxes and physical wavelengths.
(Abridged) Blue (BP) and Red (RP) Photometer low-resolution spectral data is one of the exciting new products in Gaia Data Release 3 (Gaia DR3). We calibrate about 65 billion individual transit ...spectra onto the same mean BP/RP instrument through a series of calibration steps, including background subtraction, calibration of the CCD geometry and an iterative procedure for the calibration of CCD efficiency as well as variations of the line-spread function and dispersion across the focal plane and in time. The calibrated transit spectra are then combined for each source in terms of an expansion into continuous basis functions. Time-averaged mean spectra covering the optical to near-infrared wavelength range 330, 1050 nm are published for approximately 220 million objects. Most of these are brighter than G = 17.65 but some BP/RP spectra are published for sources down to G = 21.43. Their signal- to-noise ratio varies significantly over the wavelength range covered and with magnitude and colour of the observed objects, with sources around G = 15 having S/N above 100 in some wavelength ranges. The top-quality BP/RP spectra are achieved for sources with magnitudes 9 < G < 12, having S/N reaching 1000 in the central part of the RP wavelength range. Scientific validation suggests that the internal calibration was generally successful. However, there is some evidence for imperfect calibrations at the bright end G < 11, where calibrated BP/RP spectra can exhibit systematic flux variations that exceed their estimated flux uncertainties. We also report that due to long-range noise correlations, BP/RP spectra can exhibit wiggles when sampled in pseudo-wavelength.
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 Transient Facility ...(ZTF) alert stream, in preparation for the Vera C. Rubin Observatory. The ALeRCE light curve classifier uses variability features computed from the ZTF alert stream, and colors obtained from AllWISE and ZTF photometry. We apply a Balanced Random Forest algorithm with a two-level scheme, where the top level classifies each source as periodic, stochastic, or transient, and the bottom level further resolves each of these hierarchical classes, amongst 15 total classes. This classifier corresponds to the first attempt to classify multiple classes of stochastic variables (including core- and host-dominated active galactic nuclei, blazars, young stellar objects, and cataclysmic variables) in addition to different classes of periodic and transient sources, using real data. We created a labeled set using various public catalogs (such as the Catalina Surveys and {\em Gaia} DR2 variable stars catalogs, and the Million Quasars catalog), and we classify all objects with \(\geq6\) \(g\)-band or \(\geq6\) \(r\)-band detections in ZTF (868,371 sources as of 2020/06/09), providing updated classifications for sources with new alerts every day. For the top level we obtain macro-averaged precision and recall scores of 0.96 and 0.99, respectively, and for the bottom level we obtain macro-averaged precision and recall scores of 0.57 and 0.76, respectively. Updated classifications from the light curve classifier can be found at the \href{http://alerce.online}{ALeRCE Explorer website}.
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 classification of large ...etendue telescope alert streams, such as that provided by the Zwicky Transient Facility (ZTF) and, in the future, the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). ALeRCE is a Chilean--led broker run by an interdisciplinary team of astronomers and engineers, working to become intermediaries between survey and follow--up facilities. ALeRCE uses a pipeline which includes the real--time ingestion, aggregation, cross--matching, machine learning (ML) classification, and visualization of the ZTF alert stream. We use two classifiers: a stamp--based classifier, designed for rapid classification, and a light--curve--based classifier, which uses the multi--band flux evolution to achieve a more refined classification. We describe in detail our pipeline, data products, tools and services, which are made public for the community (see \url{https://alerce.science}). Since we began operating our real--time ML classification of the ZTF alert stream in early 2019, we have grown a large community of active users around the globe. We describe our results to date, including the real--time processing of \(9.7\times10^7\) alerts, the stamp classification of \(1.9\times10^7\) objects, the light curve classification of \(8.5\times10^5\) objects, the report of 3088 supernova candidates, and different experiments using LSST-like alert streams. Finally, we discuss the challenges ahead to go from a single-stream of alerts such as ZTF to a multi--stream ecosystem dominated by LSST.
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