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Low-Frequency Cosmology Lab ( LoCo )

Epoch of Reionization : Magic Planet Imaging, Grading of Data Quality Metrics Spring 2014 NASA Space Grant Symposium University of Arizona, Tucson, Arizona Michael Busch Dr. Judd Bowman Dr. Danny Jacobs School of Earth and Space Exploration Arizona State University.

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Low-Frequency Cosmology Lab ( LoCo )

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  1. Epoch of Reionization:Magic Planet Imaging,Grading of Data Quality MetricsSpring 2014 NASA Space Grant SymposiumUniversity of Arizona, Tucson, ArizonaMichael BuschDr. Judd BowmanDr. Danny JacobsSchool of Earth and Space ExplorationArizona State University

  2. Low-Frequency Cosmology Lab (LoCo) • Led by Professor Judd Bowman • Goal of developing radio instrumentation and conduct astronomical observations to study the evolution of the early Universe and the first stars and galaxies (EoR). • My first project was to aid LoCo’s science outreach component, and second project was to help grade metrics.

  3. Magic Planet • Sphere that displays data. • Solar system, radio sky, live weather, volcanoes, etc. • Used for outreach, tours of the Gallery of Scientific Exploration (GSE) At ASU. • Goal of enhancing the outreach efforts of the LoCo Lab to the public. • Javascript, HTML. Kiosk functions like a website.

  4. Outreach

  5. Murchison Widefield Array (MWA) • Radio array in Western Australia, radio quiet environment. Started operation in 2013. ASU is a partner. • Pathfinder (technology, skillset) for Square Kilometer Array (SKA). • Antennas, not parabolic dishes. • 2048 Antennas, 128 ‘Tiles’. • To study high redshift universe. Epoch of Reionization (EoR).

  6. Power Spectra • Goal is to eliminate the foregrounds, and try to detect faint neutral hydrogen emission. • Epoch of Reionization Window • Use algorithm to get rid of noise. • Calibrate algorithm with grading of metrics. Leads to further conclusion about particular algorithm. (In which I say a spectrum is either of ‘good’ or ‘bad’ quality.)

  7. Noisy vs. Good Power Spectra

  8. Results • First algorithm. • Demonstrated algorithm not properly accounting for phase shift between antennas. • Second algorithm. • Smoothed out quality hiccups. • Detected two different types of noise at different times.

  9. Next Steps • Create new algorithms based off visual cues to filter spectra. • Machine learning – Training artificial intelligence to grade mass quantity of power spectra rather than human calibration. • Aid in creation of the MWA data analysis pipeline.

  10. Questions? / Acknowledgements • Thanks to the ASU Space Grant Staff: • Dr. Thomas Sharp • Candace Jackson • Michelle Tanaka • My Mentors: • Dr. Judd Bowman • Dr. Danny Jacobs • Meg Hufford • My Fellow Space Grant Interns • The Arizona Space Grant Consortium • Starbucks

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