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
).
Context. The VISTA Variables in the Vía Láctea (VVV) is a near-IR time-domain survey of the Galactic bulge and southern plane. One of the main goals of this survey is to reveal the 3D structure of ...the Milky Way through their variable stars. In particular, enormous numbers of RR Lyrae stars have been discovered in the inner regions of the bulge (−8° ≲ b ≲ −1°) by optical surveys such as OGLE and MACHO, but leaving an unexplored window of more than ~47 sq deg (−10.0° ≲ ℓ ≲ + 10.7° and − 10.3° ≲ b ≲ −8.0°) observed by the VVV Survey. Aims. Our goal is to characterize the RR Lyrae stars in the outer bulge in terms of their periods, amplitudes, Fourier coefficients, and distances in order to evaluate the 3D structure of the bulge in this area. The distance distribution of RR Lyrae stars will be compared to that of red clump stars, which is known to trace a X-shaped structure, in order to determine whether these two different stellar populations share the same Galactic distribution. Methods. A search for RR Lyrae stars was performed in more than ~47 sq deg at low Galactic latitudes (−10.3° ≲ b ≲ −8.0°). In the procedure the χ2 value and analysis of variance (AoV) statistic methods were used to determine the variability and periodic features of the light curves, respectively. To prevent misclassifications, the analysis was performed only on the fundamental mode RR Lyrae stars (RRab) owing to similarities found in the near-IR light curve shapes of contact eclipsing binaries (W UMa) and first overtone RR Lyrae stars (RRc). On the other hand, the red clump stars of the same analyzed tiles were selected, and cuts in the color-magnitude diagram were applied and the maximum distance restricted to ~20 kpc in order to construct a similar catalog in terms of distances and covered area compared to the RR Lyrae stars. Results. We report the detection of more than 1000 RR Lyrae ab-type stars in the VVV Survey located in the outskirts of the Galactic bulge. A few of them are possibly associated with the Sagittarius Dwarf Spheroidal Galaxy. We calculated colours, reddening, extinction, and distances of the detected RR Lyrae stars in order to determine the outer bulge 3D structure. Our main result is that, at the low galactic latitudes mapped here, the RR Lyrae stars trace a centrally concentrated spheroidal distribution. This is a noticeably different spatial distribution to the one traced by red clump stars known to follow a bar and X-shaped structure. We estimate the completeness of our sample at 80% for Ks ≤ 15 mag.
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
In this study, we introduce a novel moving-average model for analyzing stationary time-series observed irregularly in time. The process is strictly stationary and ergodic under normality and ...weakly stationary when normality is not assumed. Maximum likelihood (ML) estimation can be efficiently carried out through a Kalman algorithm obtained from the state-space representation of the model. The Kalman algorithm has order O(n) (where n is the number of observations in the sequence), from which it is possible to efficiently generate parameter estimators, linear predictors, and their mean-squared errors. Two procedures were developed for assessing parameter estimation errors: one based on the Hessian of the likelihood function and another one based on the bootstrap method. The behaviour of these estimators was assessed through Monte Carlo experiments. Both methods give accurate estimation performance, even with relatively small number of observations. Moreover, it is shown that for non-Gaussian data, specifically for the Student's t and generalized error distributions, the parameters of the model can be estimated precisely by ML. The proposed model is compared to the continuous autoregressive moving average (MA) models, showing better performance when the MA parameter is negative or close to one. We illustrate the implementation of the proposed model with light curves of variable stars from the OGLE and HIPPARCOS surveys and stochastic objects from Zwicky Transient Facility. The results suggest that the irregular MA model is a suitable alternative for modelling astronomical light curves, particularly when they have negative autocorrelation.
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.
The VISTA Variables in the Vía Láctea (VVV) is a near-IR time-domain survey of the Galactic bulge and southern plane. One of the main goals of this survey is to reveal the 3D structure of the Milky ...Way through their variable stars. Particularly the RR Lyrae stars have been massively discovered in the inner regions of the bulge (\(-8^\circ \lesssim b \lesssim -1^\circ\)) by optical surveys such as OGLE and MACHO but leaving an unexplored window of more than \(\sim 47\) sq deg (\(-10.0^\circ \lesssim \ell \lesssim +10.7^\circ\) and \(-10.3^\circ \lesssim b \lesssim -8.0^\circ\)) observed by the VVV Survey. Our goal is to characterize the RR Lyrae stars in the outer bulge in terms of their periods, amplitudes, Fourier coefficients, and distances, in order to evaluate the 3D structure of the bulge in this area. The distance distribution of RR Lyrae stars will be compared to the one of red clump stars that is known to trace a X-shaped structure in order to determine if these two different stellar populations share the same Galactic distribution. We report the detection of more than 1000 RR Lyrae ab-type stars in the VVV Survey located in the outskirts of the Galactic bulge. Some of them are possibly associated with the Sagittarius Dwarf Spheroidal Galaxy. We calculated colors, reddening, extinction, and distances of the detected RR Lyrae stars in order to determine the outer bulge 3D structure. Our main result is that, at the low galactic latitudes mapped here, the RR Lyrae stars trace a centrally concentrated spheroidal distribution. This is a noticeably different spatial distribution to the one traced by red clump stars known to follow a bar and X-shape structure. We estimate the completeness of our RRab sample in \(80\%\) for \(K_{\rm s}\lesssim15\) mag.
This dissertation proposes a model of height representation that can account for a variety of phenomena in terms of a set of unified features that define both vowels and consonants. Height features ...are organized under the Aperture node, which is divided into a basic and a dependent height level, both defined by subdivisions of the feature (Closed):(UNFORMATTED TABLE OR EQUATION FOLLOWS)$$\vbox{\halign{#\hfil&&\quad#\hfil\cr&Aperture &\cr&\qquad$\vert$&\cr&\lbrack Closed\rbrack&Basic height level\cr&\qquad$\vert$&\cr&\lbrack closed\rbrack&Dependent height level\cr}}$$(TABLE/EQUATION ENDS)There are three values for (Closed) at the basic level: (+1 Closed), (0 Closed) and ($-$1 Closed). These values define a basic ternary scale of height, corresponding to high, mid and low vowels. (+1 Closed) and ($-$1 Closed) also represent opposing polarities or pulls which characterize raising and lowering processes, respectively. It is shown that the scalar nature of height assimilations like Lena metaphony and others receive a straightforward account through the spread of the raising polarity (+1 Closed). The lowering polarity ($-$1 Closed) is also active in processes of height assimilation, such as Tunica. In contrast, the feature (0 Closed) is fundamentally different from the two active polarities, in that it shows no effects on phonological processes. This correctly predicts that no processes of "midding" exist in height assimilations. The basic height level can be further subdivided according to the presence of relative lowering (($-$1 closed)) or raising ((+1 closed)) polarities. These relative features cross-classify height levels. One of the features of the model is that it can account for the interaction of basic and derived height levels in processes of assimilation in a principled manner, through the use of the same features at both levels. This interaction plays a role in languages like Pulaar or Nzebi. The featural representation proposed for vowels is also shown to be active in processes relating to consonant-height, exemplified by the effects that consonants can have on adjacent vowels' height. These processes classify consonants into: (a) segments with a raising polarity (+1 Closed): palatals, alveopalatals and palatalized segments; (b) segments with a lowering polarity ($-$1 Closed): uvulars, pharyngeals, laryngeals and /r/-segments. In contrast with previous proposals, velars are shown to lack weight specifications.
Bilinguals are known to switch language spontaneously in everyday conversations, even if there are no external requirements to do so. However, in the laboratory setting, language control is often ...investigated using forced switching tasks, which result in significant performance costs. The present study assessed whether switching would be less costly when performed in a more natural fashion, and what factors might account for this. Mandarin-English bilinguals engaged in language switching under three different contexts with varied task demands. We examined two factors which may be characteristic of natural switching: (i) freedom of language selection; (ii) consistency of language used to name each item. Participants’ brain activities were recorded using magnetoencephalography (MEG), along with behavioural measures of reaction speed and accuracy. The natural context (with both free selection and consistent language use for each item) produced better performance overall, showing reduced mixing cost and no significant switch cost. The neural effect of language mixing was also reversed in this context, suggesting that freely mixing two languages was easier than staying in a single language. Further, while switching in the forced context elicited increased brain activity in the right inferior frontal gyrus, this switch effect disappeared when the language used to name each item was consistent. Together, these findings demonstrate that the two factors above conjointly contribute to eliminating significant performance costs and cognitive demands associated with language switching and mixing. Such evidence aligns with lexical selection models which do not assume bilingual production to be inherently effortful.
PDF
