| 1. |
- Kasliwal, M. M., et al.
(författare)
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Illuminating gravitational waves : A concordant picture of photons from a neutron star merger
- 2017
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Ingår i: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 358:6370, s. 1559-
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Tidskriftsartikel (refereegranskat)abstract
- Merging neutron stars offer an excellent laboratory for simultaneously studying strong-field gravity and matter in extreme environments. We establish the physical association of an electromagnetic counterpart (EM170817) with gravitational waves (GW170817) detected from merging neutron stars. By synthesizing a panchromatic data set, we demonstrate that merging neutron stars are a long-sought production site forging heavy elements by r-process nucleosynthesis. The weak gamma rays seen in EM170817 are dissimilar to classical short gamma-ray bursts with ultrarelativistic jets. Instead, we suggest that breakout of a wide-angle, mildly relativistic cocoon engulfing the jet explains the low-luminosity gamma rays, the high-luminosity ultraviolet-optical-infrared, and the delayed radio and x-ray emission. We posit that all neutron star mergers may lead to a wide-angle cocoon breakout, sometimes accompanied by a successful jet and sometimes by a choked jet.
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| 2. |
- Kasliwal, M. M., et al.
(författare)
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The GROWTH Marshal : A Dynamic Science Portal for Time-domain Astronomy
- 2019
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Ingår i: Publications of the Astronomical Society of the Pacific. - : IOP Publishing. - 0004-6280 .- 1538-3873. ; 131:997
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Tidskriftsartikel (refereegranskat)abstract
- We describe a dynamic science portal called the GROWTH Marshal that allows time-domain astronomers to define science programs; program filters to save sources from different discovery streams; coordinate follow-up with various robotic or classical telescopes; analyze the panchromatic follow-up data; and generate summary tables for publication. The GROWTH marshal currently serves 137 scientists, 38 science programs, and 67 telescopes. Every night, in real time, several science programs apply various customized filters to the 105 nightly alerts from the Zwicky Transient Facility. Here, we describe the schematic and explain the functionality of the various components of this international collaborative platform.
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| 3. |
- Cannella, David, et al.
(författare)
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Light-driven oxidation of polysaccharides by photosynthetic pigments and a metalloenzyme
- 2016
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 7
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Tidskriftsartikel (refereegranskat)abstract
- Oxidative processes are essential for the degradation of plant biomass. A class of powerful and widely distributed oxidative enzymes, the lytic polysaccharide monooxygenases (LPMOs), oxidize the most recalcitrant polysaccharides and require extracellular electron donors. Here we investigated the effect of using excited photosynthetic pigments as electron donors. LPMOs combined with pigments and reducing agents were exposed to light, which resulted in a never before seen 100-fold increase in catalytic activity. In addition, LPMO substrate specificity was broadened to include both cellulose and hemicellulose. LPMO enzymes and pigment derivatives common in the environment of plant-degrading organisms thus form a highly reactive and stable light-driven system increasing the turnover rate and versatility of LPMOs. This light-driven system may find applications in biotechnology and chemical processing.
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| 4. |
- Mahabal, Ashish, et al.
(författare)
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Machine Learning for the Zwicky Transient Facility
- 2019
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Ingår i: Publications of the Astronomical Society of the Pacific. - : IOP Publishing. - 0004-6280 .- 1538-3873. ; 131:997
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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.
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