On Machine-Learned Classification of Variable Stars with Sparse and Noisy Time-Series Data

With the coming data deluge from synoptic surveys, there is a growing need for frameworks that can quickly and automatically produce calibrated classification probabilities for newly-observed variables based on a small number of time-series measurements. In this paper, we introduce a methodology for variable-star classification, drawing from modern machine-learning techniques. We describe how to homogenize the information gleaned from light curves by selection and computation of real-numbered metrics ("feature"), detail methods to robustly estimate periodic light-curve features, introduce tree-ensemble methods for accurate variable star classification, and show how to rigorously evaluate the classification results using cross validation. On a 25-class data set of 1542 well-studied variable stars, we achieve a 22.8% overall classification error using the random forest classifier; this represents a 24% improvement over the best previous classifier on these data. This methodology is effective for identifying samples of specific science classes: for pulsational variables used in Milky Way tomography we obtain a discovery efficiency of 98.2% and for eclipsing systems we find an efficiency of 99.1%, both at 95% purity. We show that the random forest (RF) classifier is superior to other machine-learned methods in terms of accuracy, speed, and relative immunity to features with no useful class information; the RF classifier can also be used to estimate the importance of each feature in classification. Additionally, we present the first astronomical use of hierarchical classification methods to incorporate a known class taxonomy in the classifier, which further reduces the catastrophic error rate to 7.8%. Excluding low-amplitude sources, our overall error rate improves to 14%, with a catastrophic error rate of 3.5%.Comment: 23 pages, 9 figure

Fast-transient Searches in Real Time with ZTFReST: Identification of Three Optically Discovered Gamma-Ray Burst Afterglows and New Constraints on the Kilonova Rate

The most common way to discover extragalactic fast transients, which fade within a few nights in the optical, is via follow-up of gamma-ray burst and gravitational-wave triggers. However, wide-field surveys have the potential to identify rapidly fading transients independently of such external triggers. The volumetric survey speed of the Zwicky Transient Facility (ZTF) makes it sensitive to objects as faint and fast fading as kilonovae, the optical counterparts to binary neutron star mergers, out to almost 200 Mpc. We introduce an open-source software infrastructure, the ZTF REaltime Search and Triggering, ZTFReST, designed to identify kilonovae and fast transients in ZTF data. Using the ZTF alert stream combined with forced point-spread-function photometry, we have implemented automated candidate ranking based on their photometric evolution and fitting to kilonova models. Automated triggering, with a human in the loop for monitoring, of follow-up systems has also been implemented. In 13 months of science validation, we found several extragalactic fast transients independently of any external trigger, including two supernovae with post-shock cooling emission, two known afterglows with an associated gamma-ray burst (ZTF20abbiixp, ZTF20abwysqy), two known afterglows without any known gamma-ray counterpart (ZTF20aajnksq, ZTF21aaeyldq), and three new fast-declining sources (ZTF20abtxwfx, ZTF20acozryr, ZTF21aagwbjr) that are likely associated with GRB200817A, GRB201103B, and GRB210204A. However, we have not found any objects that appear to be kilonovae. We constrain the rate of GW170817-like kilonovae to R < 900 Gpc-3 yr-1 (95% confidence). A framework such as ZTFReST could become a prime tool for kilonova and fast-transient discovery with the Vera Rubin Observatory

SkyPortal: An Astronomical Data Platform

SkyPortal is a web application that stores and interactively displays astronomical time-series datasets for annotation, analysis, and discovery. It is designed to be modular and extensible, so it can be customized for various scientific use cases.</p

HEALPix Alchemy: Fast All-Sky Geometry and Image Arithmetic in a Relational Database for Multimessenger Astronomy Brokers

AbstractEfficient searches for electromagnetic counterparts to gravitational wave, high-energy neutrino, and gamma-ray burst events demand rapid processing of image arithmetic and geometry set operations in a database to cross-match galaxy catalogs, observation footprints, and all-sky images. Here we introduce HEALPix Alchemy, an open-source, pure Python implementation of a set of methods that enables rapid all-sky geometry calculations. HEALPix Alchemy is built upon HEALPix, a spatial indexing strategy that is widely used in astronomical databases as well as the native format of LIGO-Virgo-KAGRA gravitational-wave sky localization maps. Our approach leverages new multirange types built into the PostgreSQL 14 database engine. This enables fast all-sky queries against probabilistic multimessenger event localizations and telescope survey footprints. Questions such as “What are the galaxies contained within the 90% credible region of an event?” and “What is the rank-ordered list of the fields within an observing footprint with the highest probability of containing the event?” can be performed in less than a few seconds on commodity hardware using off-the-shelf cloud-managed database implementations without server-side database extensions. Common queries scale roughly linearly with the number of telescope pointings. As the number of fields grows into the hundreds or thousands, HEALPix Alchemy is orders of magnitude faster than other implementations. HEALPix Alchemy is now used as the spatial geometry engine within SkyPortal, which forms the basis of the Zwicky Transient Facility transient marshal, called Fritz.</jats:p

SN 2019zrk, a bright SN 2009ip analog with a precursor

We present photometric and spectroscopic observations of the Type IIn supernova SN 2019zrk (also known as ZTF20aacbyec). The SN shows a $\gtrsim$ 100 day precursor, with a slow rise, followed by a rapid rise to M $\sim -19.2$ in the $r$ and $g$ bands. The post-peak light-curve decline is well fit with an exponential decay with a timescale of $\sim 39$ days, but it shows prominent undulations, with an amplitude of $\sim 1$ mag. Both the light curve and spectra are dominated by an interaction with a dense circumstellar medium (CSM), probably from previous mass ejections. The spectra evolve from a scattering-dominated Type IIn spectrum to a spectrum with strong P-Cygni absorptions. The expansion velocity is high, $\sim 16,000$ km s$^{-1}$, even in the last spectra. The last spectrum $\sim 110$ days after the main eruption reveals no evidence for advanced nucleosynthesis. From analysis of the spectra and light curves, we estimate the mass-loss rate to be $\sim 4 \times 10^{-2}$ M$_\odot$ yr$^{-1}$ for a CSM velocity of 100 km s$^{-1}$, and a CSM mass of $\gtrsim 1$ M$_\odot$. We find strong similarities for both the precursor, general light curve, and spectral evolution with SN 2009ip and similar SNe, although SN 2019zrk displays a brighter peak magnitude. Different scenarios for the nature of the 09ip-class of SNe, based on pulsational pair instability eruptions, wave heating, and mergers, are discussed. }Comment: 18 pages, 12 figures. Astronomy & Astrophysics, in pres

SN 2019zrk, a bright SN 2009ip analog with a precursor

We present photometric and spectroscopic observations of the Type IIn supernova SN 2019zrk (also known as ZTF 20aacbyec). The SN shows a &gt; 100 day precursor, with a slow rise, followed by a rapid rise to M ≈ −19.2 in the r and g bands. The post-peak light-curve decline is well fit with an exponential decay with a timescale of ∼39 days, but it shows prominent undulations, with an amplitude of ∼1 mag. Both the light curve and spectra are dominated by an interaction with a dense circumstellar medium (CSM), probably from previous mass ejections. The spectra evolve from a scattering-dominated Type IIn spectrum to a spectrum with strong P-Cygni absorptions. The expansion velocity is high, ∼16 000 km s−1, even in the last spectra. The last spectrum ∼110 days after the main eruption reveals no evidence for advanced nucleosynthesis. From analysis of the spectra and light curves, we estimate the mass-loss rate to be ∼4 × 10−2 M⊙ yr−1 for a CSM velocity of 100 km s−1, and a CSM mass of 1 M⊙. We find strong similarities for both the precursor, general light curve, and spectral evolution with SN 2009ip and similar SNe, although SN 2019zrk displays a brighter peak magnitude. Different scenarios for the nature of the 09ip-class of SNe, based on pulsational pair instability eruptions, wave heating, and mergers, are discussed.</jats:p