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Register a new userThe ZTF Source Classification Project: I. Methods and Infrastructure
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The Zwicky Transient Facility (ZTF) has been observing the entire northern sky since the start of 2018 down to a magnitude of 20.5 ($5 sigma$ for 30s exposure) in $g$, $r$, and $i$ filters. Over the course of two years, ZTF has obtained light curves of more than a billion sources, each with 50-1000 epochs per light curve in $g$ and $r$, and fewer in $i$. To be able to use the information contained in the light curves of variable sources for new scientific discoveries, an efficient and flexible framework is needed to classify them. In this paper, we introduce the methods and infrastructure which will be used to classify all ZTF light curves. Our approach aims to be flexible and modular and allows the use of a dynamical classification scheme and labels, continuously evolving training sets, and the use of different machine learning classifier types and architectures. With this setup, we are able to continuously update and improve the classification of ZTF light curves as new data becomes available, training samples are updated, and new classes need to be incorporated.
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The current generation of all-sky surveys is rapidly expanding our ability to study variable and transient sources. These surveys, with a variety of sensitivities, cadences, and fields of view, probe many ranges of timescale and magnitude. Data from the Zwicky Transient Facility (ZTF) yields an opportunity to find variables on timescales from minutes to months. In this paper, we present the codebase, ztfperiodic, and the computational metrics employed for the catalogue based on ZTFs Second Data Release. We describe the publicly available, graphical-process-unit optimized period-finding algorithms employed, and highlight the benefit of existing and future graphical-process-unit clusters. We show how generating metrics as input to catalogues of this scale is possible for future ZTF data releases. Further work will be needed for future data from the Vera C. Rubin Observatorys Legacy Survey of Space and Time.
The EPOCH (EROS-2 periodic variable star classification using machine learning) project aims to detect periodic variable stars in the EROS-2 light curve database. In this paper, we present the first result of the classification of periodic variable stars in the EROS-2 LMC database. To classify these variables, we first built a training set by compiling known variables in the Large Magellanic Cloud area from the OGLE and MACHO surveys. We crossmatched these variables with the EROS-2 sources and extracted 22 variability features from 28 392 light curves of the corresponding EROS-2 sources. We then used the random forest method to classify the EROS-2 sources in the training set. We designed the model to separate not only $delta$ Scuti stars, RR Lyraes, Cepheids, eclipsing binaries, and long-period variables, the superclasses, but also their subclasses, such as RRab, RRc, RRd, and RRe for RR Lyraes, and similarly for the other variable types. The model trained using only the superclasses shows 99% recall and precision, while the model trained on all subclasses shows 87% recall and precision. We applied the trained model to the entire EROS-2 LMC database, which contains about 29 million sources, and found 117 234 periodic variable candidates. Out of these 117 234 periodic variables, 55 285 have not been discovered by either OGLE or MACHO variability studies. This set comprises 1 906 $delta$ Scuti stars, 6 607 RR Lyraes, 638 Cepheids, 178 Type II Cepheids, 34 562 eclipsing binaries, and 11 394 long-period variables. A catalog of these EROS-2 LMC periodic variable stars will be available online at http://stardb.yonsei.ac.kr and at the CDS website (http://vizier.u-strasbg.fr/viz-bin/VizieR).
SONG (Stellar Observations Network Group) is a global network of 1-m class robotic telescopes that is under development. The SONG prototype will shortly be operational at Observatorio del Teide, Tenerife, and first light is expected by December 2011. The main scientific goals of the SONG project are asteroseismology of bright stars and follow-up and characterization of exo-planets by means of precise measurements of stellar surface motions and brightness variations. We present the Tenerife SONG node and its instruments.
The European Solar Telescope (EST) is a project of a new-generation solar telescope. It has a large aperture of 4~m, which is necessary for achieving high spatial and temporal resolution. The high polarimetric sensitivity of the EST will allow to measure the magnetic field in the solar atmosphere with unprecedented precision. Here, we summarise the recent advancements in the realisation of the EST project regarding the hardware development and the refinement of the science requirements.
We use the spectroscopy and homogeneous photometry of 97 Type Ia supernovae obtained by the emph{Carnegie Supernova Project} as well as a subset of 36 Type Ia supernovae presented by Zheng et al. (2018) to examine maximum-light correlations in a four-dimensional (4-D) parameter space: $B$-band absolute magnitude, $M_B$, ion{Si}{2}~$lambda6355$ velocity, vsi, and ion{Si}{2} pseudo-equivalent widths pEW(ion{Si}{2}~$lambda6355$) and pEW(ion{Si}{2}~$lambda5972$). It is shown using Gaussian mixture models (GMMs) that the original four groups in the Branch diagram are well-defined and robust in this parameterization. We find three continuous groups that describe the behavior of our sample in [$M_B$, vsi] space. Extending the GMM into the full 4-D space yields a grouping system that only slightly alters group definitions in the [$M_B$, vsi] projection, showing that most of the clustering information in [$M_B$, vsi] is already contained in the 2-D GMM groupings. However, the full 4-D space does divide group membership for faster objects between core-normal and broad-line objects in the Branch diagram. A significant correlation between $M_B$ and pEW(ion{Si}{2}~$lambda5972$) is found, which implies that Branch group membership can be well-constrained by spectroscopic quantities alone. In general, we find that higher-dimensional GMMs reduce the uncertainty of group membership for objects between the originally defined Branch groups. We also find that the broad-line Branch group becomes nearly distinct with the inclusion of vsi, indicating that this subclass of SNe Ia may be somehow different from the other groups.
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