Engineering topics

Tang, Kevin

Publications and source records attributed to Tang, Kevin.

4 records

DOE OSTI2022

Investigating the Nature of the Luminous Ambiguous Nuclear Transient ASASSN-17jz

Abstract We present observations of the extremely luminous but ambiguous nuclear transient (ANT) ASASSN-17jz, spanning roughly 1200 days of the object’s evolution. ASASSN-17jz was discovered by the All-Sky Automated Survey for Supernovae (ASAS-SN) in the galaxy SDSS J171955.84+414049.4 on UT 2017 July 27 at a redshift of z = 0.1641. The transient peaked at an absolute B -band magnitude of M B ,peak = −22.81, corresponding to a bolometric luminosity of L bol,peak = 8.3 × 10 44 erg s −1 , and exhibited late-time ultraviolet emission that was still ongoing in our latest observations. Integrating the full light curve gives a total emitted energy of E tot = (1.36 ±0.08) × 10 52 erg, with (0.80 ± 0.02) × 10 52 erg of this emitted within 200 days of peak light. This late-time ultraviolet emission is accompanied by increasing X-ray emission that becomes softer as it brightens. ASASSN-17jz exhibited a large number of spectral emission lines most commonly seen in active galactic nuclei (AGNs) with little evidence of evolution. It also showed transient Balmer features, which became fainter and broader over time, and are still being detected >1000 days after peak brightness. We consider various physical scenarios for the origin of the transient, including supernovae (SNe), tidal disruption events, AGN outbursts, and ANTs. We find that the most likely explanation is that ASASSN-17jz was a SN IIn occurring in or near the disk of an existing AGN, and that the late-time emission is caused by the AGN transitioning to a more active state.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

The Lick Observatory Supernova Search follow-up program: photometry data release of 70 SESNe

We present BVRI and unfiltered (Clear) light curves of 70 stripped-envelope supernovae (SESNe), observed between 2003 and 2020, from the Lick Observatory Supernova Search follow-up program. Our SESN sample consists of 19 spectroscopically normal SNe Ib, 2 peculiar SNe Ib, six SNe Ibn, 14 normal SNe Ic, 1 peculiar SN Ic, 10 SNe Ic-BL, 15 SNe IIb, 1 ambiguous SN IIb/Ib/c, and 2 superluminous SNe. Our follow-up photometry has (on a per-SN basis) a mean coverage of 81 photometric points (median of 58 points) and a mean cadence of 3.6 d (median of 1.2 d). From our full sample, a subset of 38 SNe have pre-maximum coverage in at least one passband, allowing for the peak brightness of each SN in this subset to be quantitatively determined. We describe our data collection and processing techniques, with emphasis toward our automated photometry pipeline, from which we derive publicly available data products to enable and encourage further study by the community. Using these data products, we derive host-galaxy extinction values through the empirical colour evolution relationship and, for the first time, produce accurate rise-time measurements for a large sample of SESNe in both optical and infrared passbands. By modelling multiband light curves, we find that SNe Ic tend to have lower ejecta masses and lower ejecta velocities than SNe Ib and IIb, but higher 56Ni masses.

Lick Observatory↗

DOE OSTI2019

Lick Observatory Supernova Search follow-up program: photometry data release of 93 Type Ia supernovae

Here, we present BVRI and unfiltered light curves of 93 Type Ia supernovae (SNe Ia) from the Lick Observatory Supernova Search (LOSS) follow-up program conducted between 2005 and 2018. Our sample consists of 78 spectroscopically normal SNe Ia, with the remainder divided between distinct subclasses (3 SN 1991bg-like, 3 SN 1991T-like, 4 SNe Iax, 2 peculiar, and 3 super-Chandrasekhar events), and has a median redshift of 0.0192. The SNe in our sample have a median coverage of 16 photometric epochs at a cadence of 5.4 d, and the median first observed epoch is ~4.6 d before maximum B-band light. We describe how the SNe in our sample are discovered, observed, and processed, and we compare the results from our newly developed automated photometry pipeline to those from the previous processing pipeline used by LOSS. After investigating potential biases, we derive a final systematic uncertainty of 0.03 mag in BVRI for our data set. We perform an analysis of our light curves with particular focus on using template fitting to measure the parameters that are useful in standardizing SNe Ia as distance indicators. All of the data are available to the community, and we encourage future studies to incorporate our light curves in their analyses.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2011

Power Minimization techniques for Networked Data Centers.

Our objective is to develop a mathematical model to optimize energy consumption at multiple levels in networked data centers, and develop abstract algorithms to optimize not only individual servers, but also coordinate the energy consumption of clusters of servers within a data center and across geographically distributed data centers to minimize the overall energy cost and consumption of brown energy of an enterprise. In this project, we have formulated a variety of optimization models, some stochastic others deterministic, and have obtained a variety of qualitative results on the structural properties, robustness, and scalability of the optimal policies. We have also systematically derived from these models decentralized algorithms to optimize energy efficiency, analyzed their optimality and stability properties. Finally, we have conducted preliminary numerical simulations to illustrate the behavior of these algorithms. We draw the following conclusion. First, there is a substantial opportunity to minimize both the amount and the cost of electricity consumption in a network of datacenters, by exploiting the fact that traffic load, electricity cost, and availability of renewable generation fluctuate over time and across geographical locations. Judiciously matching these stochastic processes can optimize the tradeoff between brown energy consumption, electricity cost, and response time. Second, given the stochastic nature of these three processes, real-time dynamic feedback should form the core of any optimization strategy. The key is to develop decentralized algorithms that can be implemented at different parts of the network as simple, local algorithms that coordinate through asynchronous message passing.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗