Engineering topics

Lien, Amy Y.

Publications and source records attributed to Lien, Amy Y..

4 records

NASA NTRS2019

Energetic Particles of Cosmic Accelerators II: Active Galactic Nuclei and Gamma-ray Bursts

The high-energy universe has revealed that energetic particles are ubiquitous in the cosmos and play a vital role in the cultivation of cosmic environments on all scales. Our pursuit of more than a century to uncover the origins and fate of these cosmic energetic particles has given rise to some of the most interesting and challenging questions in astrophysics. Within our own galaxy, we have seen that energetic particles engage in a complex interplay with the galactic environment and even drive many of its key characteristics (for more information, see the first white paper in this series). On cosmological scales, the energetic particles supplied by the jets of active galactic nuclei (AGN) are an important source of energy for the intracluster and intergalactic media, providing a mechanism for regulating star formation and black hole growth and cultivating galaxy evolution (AGN feedback). Gamma-ray burst (GRB) afterglows encode information about their circumburst environment, which has implications for massive stellar winds during previous epochs over the stellar lifecycle. As such, GRB afterglows provide a means for studying very high-redshift galaxies since GRBs can be detected even if their host galaxy cannot. It has even been suggest that GRB could be used to measure cosmological distance scales if they could be shown to be standard candles. Though they play a key role in cultivating the cosmological environment and/or enabling our studies of it, there is still much we do not know about AGNs and GRBs, particularly the avenue in which and through which they supply radiation and energetic particles, namely their jets. Despite the enormous progress in particle-in-cell and magnetohydrodynamic simulations, we have yet to pinpoint the processes involved in jet formation and collimation and the conditions under which they can occur. For that matter, we have yet to identify the mechanism(s) through which the jet accelerates energetic particles – is it the commonly invoked diffusive shock acceleration process or is another mechanism, such as magnetic reconnection, required? Do AGNs and GRBs accelerate hadrons, and if so, do they accelerate them to ultra-high energies and are there high-energy neutrinos associated with them? MeV gamma-ray astronomy, enabled by technological advances that will be realized in the coming decade, will provide a unique and indispensable perspective on the persistent mysteries of the energetic universe. This White Paper is the second of a two-part series highlighting the most well-known high-energy cosmic accelerators and contributions that MeV gamma-ray astronomy will bring to understanding their energetic particle phenomena. Specifically, MeV astronomy will: 1. Determine whether AGNs accelerate CRs to ultra-high energies; 2. Provide the missing pieces for the physics of the GRB prompt emission; 3. Measure magnetization in cosmic accelerators and search for acceleration via reconnection.

Venters, Tonia M.↗

NASA NTRS2019

Energetic Particles of Cosmic Accelerators I: Galactic Accelerators

The high-energy universe has revealed that energetic particles are ubiquitous in the cosmos and play a vital role in the cultivation of cosmic environments on all scales. Our pursuit of more than a century to uncover the origins and fate of these cosmic energetic particles has given rise to some of the most interesting and challenging questions in astrophysics. Energetic particles in our own galaxy, galactic cosmic rays (GCRs), engage in a complex interplay with the interstellar medium and magnetic fields in the galaxy, giving rise to many of its key characteristics. For instance, GCRs act in concert with galactic magnetic fields to support its disk against its own weight. GCR ionization and heating are essential ingredients in promoting and regulating the formation of stars and protostellar disks. GCR ionization also drives astrochemistry, leading to the build up of complex molecules in the interstellar medium. GCR transport throughout the galaxy generates and maintains turbulence in the interstellar medium, alters its multi-phase structure, and amplifies magnetic fields. GCRs could even launch galactic winds that enrich the circumgalactic medium and alter the structure and evolution of galactic disks. As crucial as they are for many of the varied phenomena in our galaxy, there is still much we do not understand about GCRs. While they have been linked to supernova remnants (SNRs), it remains unclear whether these objects can fully account for their entire population, particularly at the lower (approximately less than 1 GeV per nucleon) and higher (~PeV) ends of the spectrum. In fact, it is entirely possible that the SNRs that have been found to accelerate CRs merely re-accelerate them, leaving the origins of the original GCRs a mystery. The conditions for particle acceleration that make SNRs compelling source candidates are also likely to be present in sources such as protostellar jets, superbubbles, and colliding wind binaries (CWBs), but we have yet to ascertain their roles in producing GCRs. For that matter, key details of diffusive shock acceleration (DSA) have yet to be revealed, and it remains to be seen whether DSA can adequately explain particle acceleration in the cosmos. This White Paper is the first of a two-part series highlighting the most well-known high-energy cosmic accelerators and contributions that MeV gamma-ray astronomy will bring to understanding their energetic particle phenomena. For the case of GCRs, MeV astronomy will: 1) Search for fresh acceleration of GCRs in SNRs; 2) Test the DSA process, particularly in SNRs and CWBs; 3) Search for signs of CR acceleration in protostellar jets and superbubbles.

Venters, Tonia M.↗

NASA NTRS2016

Modeling the Swift Bat Trigger Algorithm with Machine Learning

To draw inferences about gamma-ray burst (GRB) source populations based on Swift observations, it is essential to understand the detection efficiency of the Swift burst alert telescope (BAT). This study considers the problem of modeling the Swift / BAT triggering algorithm for long GRBs, a computationally expensive procedure, and models it using machine learning algorithms. A large sample of simulated GRBs from Lien et al. is used to train various models: random forests, boosted decision trees (with AdaBoost), support vector machines, and artificial neural networks. The best models have accuracies of greater than or equal to 97 percent (less than or equal to 3 percent error), which is a significant improvement on a cut in GRB flux, which has an accuracy of 89.6 percent (10.4 percent error). These models are then used to measure the detection efficiency of Swift as a function of redshift z, which is used to perform Bayesian parameter estimation on the GRB rate distribution. We find a local GRB rate density of n (sub 0) approaching 0.48 (sup plus 0.41) (sub minus 0.23) per cubic gigaparsecs per year with power-law indices of n (sub 1) approaching 1.7 (sup plus 0.6) (sub minus 0.5) and n (sub 2) approaching minus 5.9 (sup plus 5.7) (sub minus 0.1) for GRBs above and below a break point of z (redshift) (sub 1) approaching 6.8 (sup plus 2.8) (sub minus 3.2). This methodology is able to improve upon earlier studies by more accurately modeling Swift detection and using this for fully Bayesian model fitting.

gamma-ray burst: general â€“ gamma-rays: general â↗

NASA NTRS2015

Modeling the Swift BAT Trigger Algorithm with Machine Learning

To draw inferences about gamma-ray burst (GRB) source populations based on Swift observations, it is essential to understand the detection efficiency of the Swift burst alert telescope (BAT). This study considers the problem of modeling the Swift BAT triggering algorithm for long GRBs, a computationally expensive procedure, and models it using machine learning algorithms. A large sample of simulated GRBs from Lien et al. (2014) is used to train various models: random forests, boosted decision trees (with AdaBoost), support vector machines, and artificial neural networks. The best models have accuracies of approximately greater than 97% (approximately less than 3% error), which is a significant improvement on a cut in GRB flux which has an accuracy of 89:6% (10:4% error). These models are then used to measure the detection efficiency of Swift as a function of redshift z, which is used to perform Bayesian parameter estimation on the GRB rate distribution. We find a local GRB rate density of eta(sub 0) approximately 0.48(+0.41/-0.23) Gpc(exp -3) yr(exp -1) with power-law indices of eta(sub 1) approximately 1.7(+0.6/-0.5) and eta(sub 2) approximately -5.9(+5.7/-0.1) for GRBs above and below a break point of z(sub 1) approximately 6.8(+2.8/-3.2). This methodology is able to improve upon earlier studies by more accurately modeling Swift detection and using this for fully Bayesian model fitting. The code used in this is analysis is publicly available online.

gamma rays: general↗