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.
We present our statistical analysis of the connection between active galactic nucleus (AGN) variability and physical properties of the central supermassive black hole (SMBH). We constructed optical ...light curves using data from the QUEST-La Silla AGN variability survey. To model the variability, we used the structure function, among the excess variance and the amplitude from Damp Random Walk (DRW) modeling. For the measurement of SMBH physical properties, we used public spectra from the Sloan Digital Sky Survey (SDSS). Our analysis is based on an original sample of 2345 sources detected in both SDSS and QUEST-La Silla. For 1473 of these sources we could perform a proper measurement of the spectral and variability properties, and 1348 of these sources were classified as variable (91.5%). We found that the amplitude of the variability (A) depends solely on the rest-frame emission wavelength and the Eddington ratio, where A anticorrelates with both λrest and L/LEdd. This suggests that AGN variability does not evolve over cosmic time, and its amplitude is inversely related to the accretion rate. We found that the logarithmic gradient of the variability (γ) does not correlate significantly with any SMBH physical parameter, since there is no statistically significant linear regression model with an absolute value of the slope higher than 0.1. Finally, we found that the general distribution of γ measured for our sample differs from the distribution of γ obtained for light curves simulated from a DRW process. For 20.6% of the variable sources in our sample, a DRW model is not appropriate to describe the variability, since γ differs considerably from the expected value of 0.5.
Abstract
The classic classification scheme for active galactic nuclei (AGNs) was recently challenged by the discovery of the so-called changing-state (changing-look) AGNs. The physical mechanism ...behind this phenomenon is still a matter of open debate and the samples are too small and of serendipitous nature to provide robust answers. In order to tackle this problem, we need to design methods that are able to detect AGNs right in the act of changing state. Here we present an anomaly-detection technique designed to identify AGN light curves with anomalous behaviors in massive data sets. The main aim of this technique is to identify CSAGN at different stages of the transition, but it can also be used for more general purposes, such as cleaning massive data sets for AGN variability analyses. We used light curves from the Zwicky Transient Facility data release 5 (ZTF DR5), containing a sample of 230,451 AGNs of different classes. The ZTF DR5 light curves were modeled with a Variational Recurrent Autoencoder (VRAE) architecture, that allowed us to obtain a set of attributes from the VRAE latent space that describes the general behavior of our sample. These attributes were then used as features for an Isolation Forest (IF) algorithm that is an anomaly detector for a “one class” kind of problem. We used the VRAE reconstruction errors and the IF anomaly score to select a sample of 8809 anomalies. These anomalies are dominated by bogus candidates, but we were able to identify 75 promising CSAGN candidates.
Abstract 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 Asteroid ...Terrestrial-impact Last Alert System observations. We implement a method to infer physical parameters from these LCs using hydrodynamic models that take into account the progenitor mass, the explosion energy, and the presence of circumstellar matter (CSM). The CSM is modeled via the mass-loss rate, wind acceleration at the surface of the progenitor star with a β velocity law, and the CSM radius. We also infer the time of explosion, attenuation ( A V ), and the redshift for each SN. Our results favor low-mass progenitor stars ( M ZAMS < 14 M ⊙ ) with a dense CSM ( M ̇ > 10 −3 M ⊙ yr −1 , CSM radius ∼ 10 15 cm, and β > 2). Additionally, we find that the redshifts inferred from the SN LCs are significantly more accurate than those inferred using the host galaxy photometric redshift, suggesting that this method could be used to infer more accurate host galaxy redshifts from large samples of Type II SNe in the LSST era. Lastly, we compare our results with similar works from the literature.
ABSTRACT
The scarce optical variability studies in spectrally classified Type 2 active galactic nuclei (AGNs) have led to the discovery of anomalous objects that are incompatible with the simplest ...unified models (UMs). This paper focuses on the exploration of different variability features that allow to distinguish between obscured, Type 2 AGNs and the variable, unobscured Type 1s. We analyse systematically the Zwicky Transient Facility, 2.5-yr-long light curves of ∼15 000 AGNs from the Sloan Digital Sky Survey Data Release 16, which are generally considered Type 2s due to the absence of strong broad emission lines (BELs). Consistent with the expectations from the UM, the variability features are distributed differently for distinct populations, with spectrally classified weak Type 1s showing one order of magnitude larger variances than the Type 2s. We find that the parameters given by the damped random walk model lead to broader H α equivalent width for objects with τg > 16 d and long-term structure function SF∞, g > 0.07 mag. By limiting the variability features, we find that ∼11 per cent of Type 2 sources show evidence for optical variations. A detailed spectral analysis of the most variable sources (∼1 per cent of the Type 2 sample) leads to the discovery of misclassified Type 1s with weak BELs and changing-state candidates. This work presents one of the largest systematic investigations of Type 2 AGN optical variability to date, in preparation for future large photometric surveys.
ABSTRACT
Quasars emission is highly variable, and this variability gives us clues to understand the accretion process onto supermassive black holes. We can expect variability properties to correlate ...with the main physical properties of the accreting black hole, i.e. its mass and accretion rate. It has been established that the relative amplitude of variability anticorrelates with the accretion rate. The dependence of the variance on black hole mass has remained elusive, and contradicting results, including positive, negative, or no correlation, have been reported. In this work, we show that the key to these contradictions lies in the times-cales of variability studied (e.g. the length of the light curves available). By isolating the variance on different time-scales in well-defined mass and accretion rate bins we show that there is indeed a negative correlation between black hole mass and variance and that this anticorrelation is stronger for shorter time-scale fluctuations. The behaviour can be explained in terms of a universal variability power spectrum for all quasars, resembling a broken power law where the variance is constant at low temporal frequencies and then drops continuously for frequencies higher than a characteristic (break) frequency fb, where fb correlates with the black hole mass. Furthermore, to explain all the variance results presented here, not only the normalization of this power spectrum must anticorrelate with the accretion rate, but also the shape of the power spectra at short time-scales must depend on this parameter as well.
We used data from the QUEST-La Silla Active Galactic Nucleus (AGN) variability survey to construct light curves for 208,583 sources over ∼70 deg2, with a limiting magnitude r ∼ 21. Each light curve ...has at least 40 epochs and a length of ≥200 days. We implemented a random forest algorithm to classify our objects as either AGN or non-AGN according to their variability features and optical colors, excluding morphology cuts. We tested three classifiers, one that only includes variability features (RF1), one that includes variability features and also r − i and i − z colors (RF2), and one that includes variability features and also g − r, r − i, and i − z colors (RF3). We obtained a sample of high-probability candidates (hp-AGN) for each classifier, with 5941 candidates for RF1, 5252 candidates for RF2, and 4482 candidates for RF3. We divided each sample according to their g − r colors, defining blue (g − r ≤ 0.6) and red subsamples (g − r > 0.6). We find that most of the candidates known from the literature belong to the blue subsample, which is not necessarily surprising given that, unlike many literature studies, we do not cut our sample to point-like objects. This means that we can select AGNs that have a significant contribution from redshifted starlight in their host galaxies. In order to test the efficiency of our technique, we performed spectroscopic follow-up, confirming the AGN nature of 44 among 54 observed sources (81.5% efficiency). From the campaign, we concluded that RF2 provides the purest sample of AGN candidates.
Context. The optical variability of quasars is one of the few windows through which we can explore the behaviour of accretion discs around supermassive black holes. Aims. We aim to establish the ...dependence of variability properties, such as characteristic timescales and the variability amplitude, on basic quasar parameters such as black hole mass and the accretion rate, controlling for the rest-frame wavelength of emission. Methods. Using large catalogues of quasars, we selected the g -band light curves for 4770 objects from the Zwicky Transient Facility archive. All the selected objects fall into a narrow redshift bin, 0.6 < z < 0.7, but cover a wide range of accretion rates in Eddington units ( R Edd ) and black hole masses ( M ). We grouped these objects into 26 independent bins according to these parameters, calculated low-resolution g -band variability power spectra for each of these bins, and approximated the power spectra with a simple analytic model that features a break at a timescale, t b . Results. We find a clear dependence of the break timescale, t b , on R Edd , on top of the known dependence of t b on the black hole mass, M . In our fits, t b ∝ M 0.65 − 0.55 R Edd 0.35−0.3 , where the ranges in the exponents correspond to the best-fitting parameters of different power spectrum models. This mass dependence is slightly steeper than that found in other studies. Scaling t b to the orbital timescale of the innermost stable circular orbit (ISCO), t ISCO , results approximately in t b / t ISCO ∝ ( R Edd / M ) 0.35 . In the standard thin disc model, ( R Edd / M ) ∝ T max 4 , where T max is the maximum disc temperature, so that t b / t ISCO appears to scale approximately with the maximum temperature of the disc to a small power. The observed values of t b are ∼10 longer than the orbital timescale at the light-weighted average radius of the disc region emitting in the (observer frame) g -band. The different scaling of the break frequency with M and R Edd shows that the shape of the variability power spectrum cannot be solely a function of the quasar luminosity, even for a single rest-frame wavelength. Finally, the best-fitting models have slopes above the break in the range between −2.5 and −3. A slope of −2, as in the damped random walk models, fits the data significantly worse.
Context.
The survey of the COSMOS field by the VLT Survey Telescope is an appealing testing ground for variability studies of active galactic nuclei (AGN). With 54
r
-band visits over 3.3 yr and a ...single-visit depth of 24.6
r
-band mag, the dataset is also particularly interesting in the context of performance forecasting for the
Vera C. Rubin
Observatory Legacy Survey of Space and Time (LSST).
Aims.
This work is the fifth in a series dedicated to the development of an automated, robust, and efficient methodology to identify optically variable AGN, aimed at deploying it on future LSST data.
Methods.
We test the performance of a random forest (RF) algorithm in selecting optically variable AGN candidates, investigating how the use of different AGN labeled sets (LSs) and features sets affects this performance. We define a heterogeneous AGN LS and choose a set of variability features and optical and near-infrared colors based on what can be extracted from LSST data.
Results.
We find that an AGN LS that includes only Type I sources allows for the selection of a highly pure (91%) sample of AGN candidates, obtaining a completeness with respect to spectroscopically confirmed AGN of 69% (vs. 59% in our previous work). The addition of colors to variability features mildly improves the performance of the RF classifier, while colors alone prove less effective than variability in selecting AGN as they return contaminated samples of candidates and fail to identify most host-dominated AGN. We observe that a bright (
r
≲ 21 mag) AGN LS is able to retrieve candidate samples not affected by the magnitude cut, which is of great importance as faint AGN LSs for LSST-related studies will be hard to find and likely imbalanced. We estimate a sky density of 6.2 × 10
6
AGN for the LSST main survey down to our current magnitude limit.
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
).
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