 History of early Universe; the Epoch of Reionization  Goal: Map the evolution of structure of the early Universe using the Murchison Widefield Array.

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Presentation transcript:

 History of early Universe; the Epoch of Reionization  Goal: Map the evolution of structure of the early Universe using the Murchison Widefield Array (MWA).  Challenge: Bright radio galaxies are drown out reionization signal.  Idea!: Characterize bright galaxies with the Very Large Array (VLA), then we can subtract their signal from the EoR measurements.  End-goal: Simulate foreground impact on EoR measurements from VLA observations

The reionization history of the early universe, from Pritchard and Loeb 2012.

 Radio array in Western Australia, radio quiet environment. Started operation in ASU is a partner.  Pathfinder (technology, skillset) for Square Kilometer Array (SKA).  2048 Antennas, 128 ‘Tiles’.  Epoch of Reionization (EoR) is main science target. Both figures from Pober et al. 2016

 Finding the reionization signal is tough.  Foregrounds are predicted to be 1, ,000 brighter than EoR signal.  Need foreground spectra to characterize foregrounds in order to remove. Figure from Pober et al. 2016

Figures from Pober et al

Power leakage into EoR window when the sidelobe is near Milky Way!

o We observed 68 bright radio sources in the northern sidelobe of the MWA’s primary beam in April of o The calibrator =3C48 was also included in the observation, a primary calibrator for the MWA. o The P-band receiver ( MHz) is the lowest frequency band currently available at JVLA and is the closest frequency band to the MWA EoR observation ( MHz). o VLA has high spectral resolution. Antenna Layout of the VLA

A VLA Antenna in Socorro, New Mexico.

 RFI environment in the P-band at the VLA site is not well known.  There is no single algorithm to get ride of RFI, most algorithms are bootstrapped.  Iterative process. Must get rid of bad data! Bad RFI around ~360 MHz. How RFI affects a spectrum.

o All analysis of data is completed using CASA (Common Astronomical Software Applications) package, produced by the NRAO. o Radio frequency interference (RFI) needs to be flagged as bad data before, and after, calibrations are completed on all data sets. o Further calibrations are completed using gencal(), including the important phase and amplitudes calibrations using the calibrator =3C48. o In order to ‘invert’ the data to create an image, the clean() command is invoked. o Spectra can be extracted using imstat() and Python directories or using the CASA command specfit(). RFI Flagging flagdata() Calibration gencal() Image clean() Make SEDs specfit()

 In process of making and fitting spectra.  Want ~30 spectral indices of sources.  Have about 15 thus far. Both figures from Pober et al Spectral Index = Alpha in this equation.

 Continue to make and fit spectra. This is an iterative process and RFI needs to be flagged for each target source. Manually and automatically.  Compile Spectral Indices, end of April goal is ~30.  In the Summer, calculate spectral indices for all sources and create a catalogue.  Calculate MWA Power Spectrum response to these sources by simulating them with their associated spectral indices.

 Thanks to the ASU Space Grant Staff:  Dr. Thomas Sharp  Candace Jackson (Retired)  Michelle Tanaka (Retired)  Desiree Crawl  My Mentors:  Dr. Judd Bowman  Dr. Danny Jacobs  Meg Hufford  Boom Kittiwisit  My Fellow Space Grant Fellows  The Arizona Space Grant Consortium for all the opportunities these past three years!

 Source Name: =3C48, Jy_mean: , Jy_err: , Freq_mean: , Freq_err: , ModelAlpha:  Source Name: VLSSr , Jy_mean: , Jy_err: , Freq_mean: , Freq_err: , ModelAlpha:  Source Name: VLSSr , Jy_mean: , Jy_err: , Freq_mean: , Freq_err: , ModelAlpha:  Source Name: VLSSr , Jy_mean: , Jy_err: , Freq_mean: , Freq_err: , ModelAlpha:  Source Name: VLSSr , Jy_mean: , Jy_err: , Freq_mean: , Freq_err: , ModelAlpha:  Source Name: VLSSr , Jy_mean: , Jy_err: , Freq_mean: , Freq_err: , ModelAlpha:  Source Name: VLSSr , Jy_mean: , Jy_err: , Freq_mean: , Freq_err: , ModelAlpha:  Source Name: VLSSr , Jy_mean: , Jy_err: , Freq_mean: , Freq_err: , ModelAlpha:  Source Name: VLSSr , Jy_mean: , Jy_err: , Freq_mean: , Freq_err: , ModelAlpha:  Source Name: VLSSr , Jy_mean: , Jy_err: , Freq_mean: , Freq_err: , ModelAlpha:  Source Name: VLSSr , Jy_mean: , Jy_err: , Freq_mean: , Freq_err: , ModelAlpha:  Source Name: VLSSr , Jy_mean: , Jy_err: , Freq_mean: , Freq_err: , ModelAlpha:  Source Name: VLSSr , Jy_mean: , Jy_err: , Freq_mean: , Freq_err: , ModelAlpha:  Source Name: VLSSr , Jy_mean: , Jy_err: , Freq_mean: , Freq_err: , ModelAlpha:  Source Name: VLSSr , Jy_mean: , Jy_err: , Freq_mean: , Freq_err: , ModelAlpha:  Source Name: VLSSr , Jy_mean: , Jy_err: , Freq_mean: , Freq_err: , ModelAlpha:  Source Name: VLSSr , Jy_mean: , Jy_err: , Freq_mean: , Freq_err: , ModelAlpha:  Source Name: VLSSr , Jy_mean: , Jy_err: , Freq_mean: , Freq_err: , ModelAlpha: