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The Zwicky Transient Facility is a large optical survey in multiple filters producing hundreds of thousands of transient alerts per night. We describe here various machine learning (ML) implementations and plans to make the maximal use of the large data set by taking advantage of the temporal nature of the data, and further combining it with other data sets. We start with the initial steps of separating bogus candidates from real ones, separating stars and galaxies, and go on to the classification of real objects into various classes. Besides the usual methods (e.g., based on features extracted from light curves) we also describe early plans for alternate methods including the use of domain adaptation, and deep learning. In a similar fashion we describe efforts to detect fast moving asteroids. We also describe the use of the Zooniverse platform for helping with classifications through the creation of training samples, and active learning. Finally we mention the synergistic aspects of ZTF and LSST from the ML perspective.

We present a cadence optimization strategy to unveil a large population of kilonovae using optical imaging alone. These transients are generated during binary neutron star and potentially neutron star-black hole mergers and are electromagnetic counterparts to gravitational-wave signals detectable in nearby events with Advanced LIGO, Advanced Virgo, and other interferometers that will be online in the near future. Discovering a large population of kilonovae will allow us to determine how heavy-element production varies with the intrinsic parameters of the merger and across cosmic time. The rate of binary neutron star mergers is still uncertain, but only few ( 15) events with associated kilonovae may be detectable per year within the horizon of next-generation ground-based interferometers. The rapid evolution (∼days) at optical/infrared wavelengths, relatively low luminosity, and the low volumetric rate of kilonovae makes their discovery difficult, especially during blind surveys of the sky. We propose future large surveys to adopt a rolling cadence in which g-i observations are taken nightly for blocks of 10 consecutive nights. With the current baseline2018a cadence designed for the Large Synoptic Survey Telescope (LSST), 7.5 poorly sampled kilonovae are expected to be detected in both the Wide Fast Deep (WFD) and Deep Drilling Fields (DDF) surveys per year, under optimistic assumptions on their rate, duration, and luminosity. We estimate the proposed strategy to return up to ∼272 GW170817-like kilonovae throughout the LSST WFD survey, discovered independently from gravitational-wave triggers.

There is significant interest in the models for production of short gamma-ray bursts (GRBs). Until now, the number of known short GRBs with multi-wavelength afterglows has been small. While the Fermi GRB Monitor detects many GRBs relative to the Neil Gehrels Swift Observatory, the large localization regions makes the search for counterparts difficult. With the Zwicky Transient Facility (ZTF) recently achieving first light, it is now fruitful to use its combination of depth (mAB ∼ 20.6), field of view ( 47 square degrees), and survey cadence (every ∼3 days) to perform Target of Opportunity observations. We demonstrate this capability on GRB 180523B, which was recently announced by the Fermi GRB Monitor as a short GRB. ZTF imaged 2900 square degrees of the localization region, resulting in the coverage of 61.6% of the enclosed probability over two nights to a depth of mAB ∼ 20.5. We characterized 14 previously unidentified transients, and none were found to be consistent with a short GRB counterpart. This search with the ZTF shows it is an efficient camera for searching for coarsely localized short GRB and gravitational-wave counterparts, allowing for a sensitive search with minimal interruption to its nominal cadence.

Cosmic neutrinos provide a unique window into the otherwise hidden mechanism of particle acceleration in astrophysical objects. The IceCube Collaboration recently reported the likely association of one high-energy neutrino with a flare from the relativistic jet of an active galaxy pointed towards the Earth. However a combined analysis of many similar active galaxies revealed no excess from the broader population, leaving the vast majority of the cosmic neutrino flux unexplained. Here we present the likely association of a radio-emitting tidal disruption event, AT2019dsg, with a second high-energy neutrino. AT2019dsg was identified as part of our systematic search for optical counterparts to high-energy neutrinos with the Zwicky Transient Facility. The probability of finding any coincident radio-emitting tidal disruption event by chance is 0.5%, while the probability of finding one as bright in bolometric energy flux as AT2019dsg is 0.2%. Our electromagnetic observations can be explained through a multizone model, with radio analysis revealing a central engine, embedded in a UV photosphere, that powers an extended synchrotron-emitting outflow. This provides an ideal site for petaelectronvolt neutrino production. Assuming that the association is genuine, our observations suggest that tidal disruption events with mildly relativistic outflows contribute to the cosmic neutrino flux. The tidal disruption event AT2019dsg is probably associated with a high-energy neutrino, suggesting that such events can contribute to the cosmic neutrino flux. The electromagnetic emission is explained in terms of a central engine, a photosphere and an extended synchrotron-emitting outflow.

Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos. In this Expert Recommendation, we review the key challenges of real-time observations of gravitational wave sources and their electromagnetic and astroparticle counterparts, and make a number of recommendations to maximize their potential for scientific discovery. These recommendations refer to the design of scalable and computationally efficient machine learning algorithms; the cyber-infrastructure to numerically simulate astrophysical sources, and to process and interpret multi-messenger astrophysics data; the management of gravitational wave detections to trigger real-time alerts for electromagnetic and astroparticle follow-ups; a vision to harness future developments of machine learning and cyber-infrastructure resources to cope with the big-data requirements; and the need to build a community of experts to realize the goals of multi-messenger astrophysics.A group of experts suggests ways in which deep learning can be used to enhance the potential for discovery in multi-messenger astrophysics.

We present a cadence optimization strategy to unveil a large population of kilonovae using optical imaging alone. These transients are generated during binary neutron star and potentially neutron star–black hole mergers and are electromagnetic counterparts to gravitational-wave signals detectable in nearby events with Advanced LIGO, Advanced Virgo, and other interferometers that will be online in the near future. Discovering a large population of kilonovae will allow us to determine how heavy-element production varies with the intrinsic parameters of the merger and across cosmic time. The rate of binary neutron star mergers is still uncertain, but only few (≲15) events with associated kilonovae may be detectable per year within the horizon of next-generation ground-based interferometers. The rapid evolution (∼days) at optical/infrared wavelengths, relatively low luminosity, and the low volumetric rate of kilonovae makes their discovery difficult, especially during blind surveys of the sky. We propose future large surveys to adopt a rolling cadence in which g-i observations are taken nightly for blocks of 10 consecutive nights. With the current baseline2018a cadence designed for the Large Synoptic Survey Telescope (LSST), ≲7.5 poorly sampled kilonovae are expected to be detected in both the Wide Fast Deep (WFD) and Deep Drilling Fields (DDF) surveys per year, under optimistic assumptions on their rate, duration, and luminosity. We estimate the proposed strategy to return up to ∼272 GW170817-like kilonovae throughout the LSST WFD survey, discovered independently from gravitational-wave triggers.

The Zwicky Transient Facility (ZTF), a public–private enterprise, is a new time-domain survey employing a dedicated camera on the Palomar 48-inch Schmidt telescope with a 47 deg² field of view and an 8 second readout time. It is well positioned in the development of time-domain astronomy, offering operations at 10% of the scale and style of the Large Synoptic Survey Telescope (LSST) with a single 1-m class survey telescope. The public surveys will cover the observable northern sky every three nights in g and r filters and the visible Galactic plane every night in g and r. Alerts generated by these surveys are sent in real time to brokers. A consortium of universities that provided funding (“partnership”) are undertaking several boutique surveys. The combination of these surveys producing one million alerts per night allows for exploration of transient and variable astrophysical phenomena brighter than r ∼ 20.5 on timescales of minutes to years. We describe the primary science objectives driving ZTF, including the physics of supernovae and relativistic explosions, multi-messenger astrophysics, supernova cosmology, active galactic nuclei, and tidal disruption events, stellar variability, and solar system objects.

There is significant interest in the models for production of short gamma-ray bursts (GRBs). Until now, the number of known short GRBs with multi-wavelength afterglows has been small. While the Fermi GRB Monitor detects many GRBs relative to the Neil Gehrels Swift Observatory, the large localization regions makes the search for counterparts difficult. With the Zwicky Transient Facility (ZTF) recently achieving first light, it is now fruitful to use its combination of depth (m AB ∼ 20.6), field of view (≈47 square degrees), and survey cadence (every ∼3 days) to perform Target of Opportunity observations. We demonstrate this capability on GRB 180523B, which was recently announced by the Fermi GRB Monitor as a short GRB. ZTF imaged ≈2900 square degrees of the localization region, resulting in the coverage of 61.6% of the enclosed probability over two nights to a depth of m AB ∼ 20.5. We characterized 14 previously unidentified transients, and none were found to be consistent with a short GRB counterpart. This search with the ZTF shows it is an efficient camera for searching for coarsely localized short GRB and gravitational-wave counterparts, allowing for a sensitive search with minimal interruption to its nominal cadence.

The Zwicky Transient Facility is a large optical survey in multiple filters producing hundreds of thousands of transient alerts per night. We describe here various machine learning (ML) implementations and plans to make the maximal use of the large data set by taking advantage of the temporal nature of the data, and further combining it with other data sets. We start with the initial steps of separating bogus candidates from real ones, separating stars and galaxies, and go on to the classification of real objects into various classes. Besides the usual methods (e.g., based on features extracted from light curves) we also describe early plans for alternate methods including the use of domain adaptation, and deep learning. In a similar fashion we describe efforts to detect fast moving asteroids. We also describe the use of the Zooniverse platform for helping with classifications through the creation of training samples, and active learning. Finally we mention the synergistic aspects of ZTF and LSST from the ML perspective.