12 research outputs found
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
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 100
day precursor, with a slow rise, followed by a rapid rise to M in
the and bands. The post-peak light-curve decline is well fit with an
exponential decay with a timescale of days, but it shows prominent
undulations, with an amplitude of 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, km s, even in
the last spectra. The last spectrum 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
M yr for a CSM velocity of 100 km s, and a CSM mass of
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. }Comment: 18 pages, 12 figures. Astronomy & Astrophysics, in pres
A data science platform to enable time-domain astronomy
International audienceSkyPortal is an open-source platform designed to efficiently discover interesting transients, manage follow-up, perform characterization, and visualize the results, all in one application. By enabling fast access to archival and catalog data, cross-matching heterogeneous data streams, and the triggering and monitoring of on-demand observations for further characterization, SkyPortal has been operating at scale for > 2 yr for the Zwicky Transient Facility Phase II community, with hundreds of users, containing tens of millions of time-domain sources, interacting with dozens of telescopes, and enabling community reporting. While SkyPortal emphasizes rich user experiences (UX) across common frontend workflows, recognizing that scientific inquiry is increasingly performed programmatically, SkyPortal also surfaces an extensive and well-documented API system. From backend and frontend software to data science analysis tools and visualization frameworks, the SkyPortal design emphasizes the re-use and leveraging of best-in-class approaches, with a strong extensibility ethos. For instance, SkyPortal now leverages ChatGPT large-language models (LLMs) to automatically generate and surface source-level human-readable summaries. With the imminent re-start of the next-generation of gravitational wave detectors, SkyPortal now also includes dedicated multi-messenger features addressing the requirements of rapid multi-messenger follow-up: multi-telescope management, team/group organizing interfaces, and cross-matching of multi-messenger data streams with time-domain optical surveys, with interfaces sufficiently intuitive for the newcomers to the field. (abridged
A data science platform to enable time-domain astronomy
International audienceSkyPortal is an open-source platform designed to efficiently discover interesting transients, manage follow-up, perform characterization, and visualize the results, all in one application. By enabling fast access to archival and catalog data, cross-matching heterogeneous data streams, and the triggering and monitoring of on-demand observations for further characterization, SkyPortal has been operating at scale for > 2 yr for the Zwicky Transient Facility Phase II community, with hundreds of users, containing tens of millions of time-domain sources, interacting with dozens of telescopes, and enabling community reporting. While SkyPortal emphasizes rich user experiences (UX) across common frontend workflows, recognizing that scientific inquiry is increasingly performed programmatically, SkyPortal also surfaces an extensive and well-documented API system. From backend and frontend software to data science analysis tools and visualization frameworks, the SkyPortal design emphasizes the re-use and leveraging of best-in-class approaches, with a strong extensibility ethos. For instance, SkyPortal now leverages ChatGPT large-language models (LLMs) to automatically generate and surface source-level human-readable summaries. With the imminent re-start of the next-generation of gravitational wave detectors, SkyPortal now also includes dedicated multi-messenger features addressing the requirements of rapid multi-messenger follow-up: multi-telescope management, team/group organizing interfaces, and cross-matching of multi-messenger data streams with time-domain optical surveys, with interfaces sufficiently intuitive for the newcomers to the field. (abridged
A data science platform to enable time-domain astronomy
SkyPortal is an open-source platform designed to efficiently discover interesting transients, manage follow-up, perform characterization, and visualize the results, all in one application. By enabling fast access to archival and catalog data, cross-matching heterogeneous data streams, and the triggering and monitoring of on-demand observations for further characterization, SkyPortal has been operating at scale for > 2 yr for the Zwicky Transient Facility Phase II community, with hundreds of users, containing tens of millions of time-domain sources, interacting with dozens of telescopes, and enabling community reporting. While SkyPortal emphasizes rich user experiences (UX) across common frontend workflows, recognizing that scientific inquiry is increasingly performed programmatically, SkyPortal also surfaces an extensive and well-documented API system. From backend and frontend software to data science analysis tools and visualization frameworks, the SkyPortal design emphasizes the re-use and leveraging of best-in-class approaches, with a strong extensibility ethos. For instance, SkyPortal now leverages ChatGPT large-language models (LLMs) to automatically generate and surface source-level human-readable summaries. With the imminent re-start of the next-generation of gravitational wave detectors, SkyPortal now also includes dedicated multi-messenger features addressing the requirements of rapid multi-messenger follow-up: multi-telescope management, team/group organizing interfaces, and cross-matching of multi-messenger data streams with time-domain optical surveys, with interfaces sufficiently intuitive for the newcomers to the field. (abridged
A data science platform to enable time-domain astronomy
International audienceSkyPortal is an open-source platform designed to efficiently discover interesting transients, manage follow-up, perform characterization, and visualize the results, all in one application. By enabling fast access to archival and catalog data, cross-matching heterogeneous data streams, and the triggering and monitoring of on-demand observations for further characterization, SkyPortal has been operating at scale for > 2 yr for the Zwicky Transient Facility Phase II community, with hundreds of users, containing tens of millions of time-domain sources, interacting with dozens of telescopes, and enabling community reporting. While SkyPortal emphasizes rich user experiences (UX) across common frontend workflows, recognizing that scientific inquiry is increasingly performed programmatically, SkyPortal also surfaces an extensive and well-documented API system. From backend and frontend software to data science analysis tools and visualization frameworks, the SkyPortal design emphasizes the re-use and leveraging of best-in-class approaches, with a strong extensibility ethos. For instance, SkyPortal now leverages ChatGPT large-language models (LLMs) to automatically generate and surface source-level human-readable summaries. With the imminent re-start of the next-generation of gravitational wave detectors, SkyPortal now also includes dedicated multi-messenger features addressing the requirements of rapid multi-messenger follow-up: multi-telescope management, team/group organizing interfaces, and cross-matching of multi-messenger data streams with time-domain optical surveys, with interfaces sufficiently intuitive for the newcomers to the field. (abridged