Engineering topics

Scott Adams

Publications and source records attributed to Scott Adams.

2 records

NASA NTRS2020

The Broad-lined Ic Supernova ZTF18aaqjovh (SN 2018bvw): An Optically Discovered Engine-driven Supernova Candidate with Luminous Radio Emission

We present ZTF18aaqjovh (SN 2018bvw), a high-velocity ("broad-lined") stripped-envelope (Type Ic) supernova (Ic-BL SN) discovered in the Zwicky Transient Facility one-day cadence survey. ZTF18aaqjovh shares a number of features in common with engine-driven explosions: the photospheric velocity and the shape of the optical light curve are very similar to those of the Type Ic-BL SN 1998bw, which was associated with a low-luminosity gamma-ray burst (LLGRB) and had relativistic ejecta. However, the radio luminosity of ZTF18aaqjovh is almost two orders of magnitude fainter than that of SN 1998bw at the same velocity phase, and the shock velocity is at most mildly relativistic (v = 0.06–0.4c). A search of high-energy catalogs reveals no compelling gamma-ray burst (GRB) counterpart to ZTF18aaqjovh, and the limit on the prompt GRB luminosity of L(γ(ꙩ)iso) ≈ 1.6 x 10^(48) erg/s excludes a classical GRB but not an LLGRB. Altogether, ZTF18aaqjovh represents another transition event between engine-driven SNe associated with GRBs and "ordinary" Ic-BL SNe.

Anna Y. Q. Ho↗

NASA NTRS2019

Machine Learning for the Zwicky Transient Facility

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.

Ashish Mahabal↗