| 1. |
- Bellm, Eric C., et al.
(författare)
-
The Zwicky Transient Facility : System Overview, Performance, and First Results
- 2019
-
Ingår i: Publications of the Astronomical Society of the Pacific. - : IOP Publishing. - 0004-6280 .- 1538-3873. ; 131:995
-
Tidskriftsartikel (refereegranskat)abstract
- The Zwicky Transient Facility (ZTF) is a new optical time-domain survey that uses the Palomar 48 inch Schmidt telescope. A custom-built wide-field camera provides a 47 deg(2) field of view and 8 s readout time, yielding more than an order of magnitude improvement in survey speed relative to its predecessor survey, the Palomar Transient Factory. We describe the design and implementation of the camera and observing system. The ZTF data system at the Infrared Processing and Analysis Center provides near-real-time reduction to identify moving and varying objects. We outline the analysis pipelines, data products, and associated archive. Finally, we present on-sky performance analysis and first scientific results from commissioning and the early survey. ZTF's public alert stream will serve as a useful precursor for that of the Large Synoptic Survey Telescope.
|
|
| 2. |
- Irani, Ido, et al.
(författare)
-
On the Origin of SN 2016hil-A Type II Supernova in the Remote Outskirts of an Elliptical Host
- 2019
-
Ingår i: Astrophysical Journal. - : American Astronomical Society. - 0004-637X .- 1538-4357. ; 887:2
-
Tidskriftsartikel (refereegranskat)abstract
- Type II supernovae (SNe) stem from the core collapse of massive (>8 M-circle dot) stars. Due to their short lifespan, we expect a very low rate of such events in elliptical hosts, where the star formation rate is low, and which are mostly comprised of an old stellar population. SN 2016hil (iPTF16hil) is an SN II located in the extreme outskirts of an elliptical galaxy at z.=.0.0608 (projected distance 27.2 kpc). It was detected near peak (M-r similar to -17 mag) 9 days after the last non-detection. The event has some potentially peculiar properties: it presented an apparently double-peaked light curve, and its spectra suggest low metallicity content (Z < 0.4 Z(circle dot)). We place a tentative upper limit on the mass of a potential faint host at log M/M-circle dot = 7.27(-0.24)(+0.43) using deep optical imaging from Keck/LRIS. In light of this, we discuss the possibility of the progenitor forming locally and other more exotic formation scenarios such as a merger or common-envelope evolution causing a time-delayed explosion. Further observations of the explosion site in the UV are needed in order to distinguish between the cases. Regardless of the origin of the transient, observing a population of such seemingly hostless SNe II could have many uses, including an estimate the amount of faint galaxies in a given volume, and tests of the prediction of a time-delayed population of core-collapse SNe in locations otherwise unfavorable for the detection of such events.
|
|
| 3. |
- Mahabal, Ashish, et al.
(författare)
-
Machine Learning for the Zwicky Transient Facility
- 2019
-
Ingår i: Publications of the Astronomical Society of the Pacific. - : IOP Publishing. - 0004-6280 .- 1538-3873. ; 131:997
-
Tidskriftsartikel (refereegranskat)abstract
- 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.
|
|
