Engineering topics
Publications and source records attributed to Matthew Graham.
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Gamma-ray bursts (GRBs) are among the brightest and most energetic events in the Universe. The duration and hardness distribution of GRBs has two clusters, now understood to reflect (at least) two different progenitors. Short-hard GRBs (SGRBs; T(sub 90) < 2 s) arise from compact binary mergers, and long-soft GRBs (LGRBs; T(sub 90) > 2 s) have been attributed to the collapse of peculiar massive stars (collapsars). The discovery of SN 1998bw/GRB 980425 marked the first association of an LGRB with a collapsar, and AT 2017gfo/GRB 170817A/GW170817 marked the first association of an SGRB with a binary neutron star merger, which also produced a gravitational wave. Here, we present the discovery of ZTF20abwysqy (AT2020scz), a fast-fading optical transient in the Fermi satellite and the Interplanetary Network localization regions of GRB 200826A; X-ray and radio emission further confirm that this is the afterglow. Follow-up imaging (at rest-frame 16.5 days) reveals excess emission above the afterglow that cannot be explained as an underlying kilonova, but which is consistent with being the supernova. Although the GRB duration is short (rest-frame T90 of 0.65 s), our panchromatic follow-up data confirm a collapsar origin. GRB 200826A is the shortest LGRB found with an associated collapsar; it appears to sit on the brink between a successful and a failed collapsar. Our discovery is consistent with the hypothesis that most collapsars fail to produce ultra-relativistic jets.
We study the properties of the galaxies hosting the first 19 tidal disruption events(TDEs)detected with the Zwicky Transient Facility(ZTF)within the context of a carefully constructed, representative host galaxy sample. We find that the ZTF sample of TDE hosts is dominated by compact “green valley” galaxies. After we restrict the comparison sample to galaxies with a similar concentration, as measured by the Sérsic index, we find this green valley overrepresentation is even larger. That is, concentrated red sequence galaxies are not producing TDEs at elevated levels. We present host galaxy spectra that show that E+A galaxies are overrepresented in the ZTF sample by a factor of≈22, which is lower than previous TDE host galaxy studies have found. We find that this overrepresentation can be fully accounted for when taking into account the masses, colors, and Sérsic indices of the ZTF TDE hosts. The combination of both green colors and high Sérsic index of the typical TDE host galaxy could be explained if the TDE rate is temporarily enhanced following a merger that leads to a higher central concentration of stars.
We present visible and near-infrared (NIR) photometric and spectroscopic observations of interstellar object (ISO) 2I/Borisov taken from 2019 September 10 to 2019 December 20 using the GROWTH, the Apache Point Observatory Astrophysical Research Consortium 3.5 m, and the NASA Infrared Telescope Facility 3.0 m combined with pre- and postdiscovery observations of 2I obtained by the Zwicky Transient Facility from 2019 March 17 to 2019 May 5. Comparison with imaging of distant solar system comets shows an object very similar to mildly active solar system comets with an outgassing rate of ∼1027 mol s−1. The photometry, taken in filters spanning the visible and NIR range, shows a gradual brightening trend of ∼0.03 mag day−1 since 2019 September 10 UTC for a reddish object becoming neutral in the NIR. The light curve from recent and prediscovery data reveals a brightness trend suggesting the recent onset of significant H2O sublimation with the comet being active with super volatiles such as CO at heliocentric distances >6 au consistent with its extended morphology. Using the advanced capability to significantly reduce the scattered light from the coma enabled by high-resolution NIR images from Keck adaptive optics taken on 2019 October 4, we estimate a diameter for 2I’s nucleus of <~1.4 km. We use the size estimates of 1I/’Oumuamua and 2I/Borisov to roughly estimate the slope of the ISO size distribution, resulting in a slope of ∼3.4 ± 1.2, similar to solar system comets and bodies produced from collisional equilibrium.
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.
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.
The Zwicky Transient Facility (ZTF) is a new robotic time-domain survey currently in progress using the Palomar 48-inch Schmidt Telescope. ZTF uses a 47 square degree field with a 600 megapixel camera to scan the entire northern visible sky at rates of ∼3760 square degrees/hour to median depths of g ~ 20.8 and r ~ 20.6 mag (AB, 5σ in 30 sec). We describe the Science Data System that is housed at IPAC, Caltech. This comprises the data-processing pipelines, alert production system, data archive, and user interfaces for accessing and analyzing the products. The real-time pipeline employs a novel image-differencing algorithm, optimized for the detection of point-source transient events. These events are vetted for reliability using a machine-learned classifier and combined with contextual information to generate data-rich alert packets. The packets become available for distribution typically within 13 minutes (95th percentile) of observation. Detected events are also linked to generate candidate moving-object tracks using a novel algorithm. Objects that move fast enough to streak in the individual exposures are also extracted and vetted. We present some preliminary results of the calibration performance delivered by the real-time pipeline. The reconstructed astrometric accuracy per science image with respect to Gaia DR1 is typically 45 to 85 milliarcsec. This is the RMS per-axis on the sky for sources extracted with photometric S/N ≥10 and hence corresponds to the typical astrometric uncertainty down to this limit. The derived photometric precision (repeatability) at bright unsaturated fluxes varies between 8 and 25 millimag. The high end of these ranges corresponds to an airmass approaching ∼2—the limit of the public survey. Photometric calibration accuracy with respect to Pan-STARRS1 is generally better than 2%. The products support a broad range of scientific applications: fast and young supernovae; rare flux transients; variable stars; eclipsing binaries; variability from active galactic nuclei; counterparts to gravitational wave sources; a more complete census of Type Ia supernovae; and solar-system objects.