Other Astrophysics and Astronomy Commons^™

Probing The 'Mev Gap': Discovery And Characterization Of Persistent And Transient Mev-Gev Gamma-Ray Sources With The Fermi Large Area Telescoope, Scott Joffre

All Dissertations

The MeV Gap is a moniker for the energy regime E~0.1-100 MeV that has lacked sensitive observations for the last 20 years. This is not out of scientific disinterest, but observational challenges. The only instrument currently available to make observations in the MeV Gap is the Fermi Large Area Telescope (LAT). For the first time ever, Fermi-LAT has been leveraged to push its observational capabilities to its absolute lower energy limit to end this 20-year observational gap. Observing the MeV sky is imperative for identifying some of the most luminous galaxies in the Universe known as blazars. The brightest blazars …

Classification Of Variable Stars Using Convolutional Neural Network, Abhina Premachandran Bindu

Dissertations and Theses

This research focuses on developing Convolutional Neural Networks (CNNs), for the process of classifying and identifying variable stars through the analysis of unprocessed light curves from Transiting Exoplanet Survey Satellite (TESS). As astronomical data is becoming increasingly complex, and as advanced missions deploy sophisticated instruments for data collection, both the quality and quantity of the data are improving at a rapid pace. This has created an urgent need to automate the analysis process using efficient and effective methods, such as those based on machine learning. While previous research has explored machine learning approaches, there has been limited focus on implementing …

Using Deep Neural Networks To Classify Astronomical Images, Andrew D. Macpherson

Honors Projects

As the quantity of astronomical data available continues to exceed the resources available for analysis, recent advances in artificial intelligence encourage the development of automated classification tools. This paper lays out a framework for constructing a deep neural network capable of classifying individual astronomical images by describing techniques to extract and label these objects from large images.

Searching Harder, Localizing Better, Classifying Faster: Optimizing Fast Radio Burst Detection And Analysis, Kshitij Aggarwal

Graduate Theses, Dissertations, and Problem Reports (ETD)

Fast Radio Bursts (or FRBs) are millisecond-duration transients of extragalactic origin. They exhibit dispersion caused by propagation through an ionized medium, and quantified by Dispersion Measure (DM). Around 800 FRBs (24 repeaters) have been discovered; so far, 24 FRBs have been confidently associated with a host galaxy. In this thesis, we discuss multiple new FRB search and analysis techniques and the corresponding tools that enable us to search for FRBs harder, localize them better, and classify candidates faster.

We discuss five open-source software suites that can be used in FRB analysis. These suites are used to distinguish between FRBs and …

Identification And Classification Of Radio Pulsar Signals Using Machine Learning, Di Pang

Graduate Theses, Dissertations, and Problem Reports (ETD)

Automated single-pulse search approaches are necessary as ever-increasing amount of observed data makes the manual inspection impractical. Detecting radio pulsars using single-pulse searches, however, is a challenging problem for machine learning because pul- sar signals often vary significantly in brightness, width, and shape and are only detected in a small fraction of observed data.

The research work presented in this dissertation is focused on development of ma- chine learning algorithms and approaches for single-pulse searches in the time domain. Specifically, (1) We developed a two-stage single-pulse search approach, named Single- Pulse Event Group IDentification (SPEGID), which automatically identifies and clas- …

Searching For Needles In The Cosmic Haystack, Thomas Ryan Devine

Graduate Theses, Dissertations, and Problem Reports (ETD)

Searching for pulsar signals in radio astronomy data sets is a difficult task. The data sets are extremely large, approaching the petabyte scale, and are growing larger as instruments become more advanced. Big Data brings with it big challenges. Processing the data to identify candidate pulsar signals is computationally expensive and must utilize parallelism to be scalable. Labeling benchmarks for supervised classification is costly. To compound the problem, pulsar signals are very rare, e.g., only 0.05% of the instances in one data set represent pulsars. Furthermore, there are many different approaches to candidate classification with no consensus on a best …