21 cm Foreground Subtraction in the Image Plane and in the uv-Plane

Slides:

Advertisements

Similar presentations

An Introduction to Fourier and Wavelet Analysis: Part I Norman C. Corbett Sunday, June 1, 2014.

Advertisements

ACHIZITIA IN TIMP REAL A SEMNALELOR. Three frames of a sampled time domain signal. The Fast Fourier Transform (FFT) is the heart of the real-time spectrum.

Morphing & Warping 2D Morphing Involves 2 steps 1.Image warping “get features to line up” 2.Cross-dissolve “mix colors” (fade-in/fadeout transition)

Spectral Characterization of calibration lamps: STIS An improved list of emission lines for STIS Calibration Enhancement Florian Kerber, ST-ECF TIPS Meeting.

Foreground cleaning in CMB experiments Carlo Baccigalupi, SISSA, Trieste.

Cleaned Three-Year WMAP CMB Map: Magnitude of the Quadrupole and Alignment of Large Scale Modes Chan-Gyung Park, Changbom Park (KIAS), J. Richard Gott.

EOR Detection Strategies Somnath Bharadwaj IIT Kharagpur.

FastICA as a LOFAR-EoR Foreground Cleaning Technique Filipe Abdalla and Emma Woodfield University College London with Saleem Zaroubi, Vibor Jelic, Panos.

Subtleties in Foreground Subtraction Adrian Liu, MIT mK 1 K 100 mK.

Indo – SA Joint Astronomy Workshop, August 2012 / 22 Study of Foregrounds and Limitations to EoR Detection Nithyanandan Thyagarajan N. Udaya Shankar Ravi.

Wed. 17th Sept Hamburg LOFAR Workshop.  Extract a cosmological signal from a datacube, the three axes of which are x and y positions, and frequency.

Interferometric Spectral Line Imaging Martin Zwaan (Chapters of synthesis imaging book)

November 4, 2014Computer Vision Lecture 15: Shape Representation II 1Signature Another popular method of representing shape is called the signature. In.

lecture 5, Sampling and the Nyquist Condition Sampling Outline  FT of comb function  Sampling Nyquist Condition  sinc interpolation Truncation.

Fourier Analysis Without Tears : Computational Photography Alexei Efros, CMU, Fall 2005 Somewhere in Cinque Terre, May 2005.

Continuous Time Signals All signals in nature are in continuous time.

Discrete Time Periodic Signals A discrete time signal x[n] is periodic with period N if and only if for all n. Definition: Meaning: a periodic signal keeps.

CSIRO; Swinburne Error Recognition Emil Lenc University of Sydney / CAASTRO CASS Radio Astronomy School 2012 Based on lectures given previously.

Principles of MRI Physics and Engineering

Ninth Synthesis Imaging Summer School Socorro, June 15-22, 2004 Mosaicing Tim Cornwell.

Transforms. 5*sin (2  4t) Amplitude = 5 Frequency = 4 Hz seconds A sine wave.

Ping Zhang, Zhen Li,Jianmin Zhou, Quan Chen, Bangsen Tian

Seeram Chapter 7: Image Reconstruction

J. Hibbard Spectral Line II: Calibration & Analysis 1 Spectral Line II: Calibration and Analysis Bandpass Calibration Flagging Continuum Subtraction Imaging.

lecture 4, Convolution and Fourier Convolution Convolution Fourier Convolution Outline  Review linear imaging model  Instrument response function.

Anisotropy Studies in the Pierre Auger Observatory Beatriz Blanco Siffert for the Auger Collaboration Instituto de Física - Universidade Federal do Rio.

Image Processing Edge detection Filtering: Noise suppresion.

09/19/2002 (C) University of Wisconsin 2002, CS 559 Last Time Color Quantization Dithering.

How natural scenes might shape neural machinery for computing shape from texture? Qiaochu Li (Blaine) Advisor: Tai Sing Lee.

Ninth Synthesis Imaging Summer School Socorro, June 15-22, 2004 Spectral Line II John Hibbard.

Fundamental limits of radio interferometers: Source parameter estimation Cathryn Trott Randall Wayth Steven Tingay Curtin University International Centre.

Area of Complex Figures. What are complex figures? Figures that can be subdivided into simple figures.

Artificial ‘Physics-light’ Ring Data Rachel Howe, Irene Gonzalez-Hernandez, and Frank Hill.

NASSP Masters 5003F - Computational Astronomy Lecture 14 Reprise: dirty beam, dirty image. Sensitivity Wide-band imaging Weighting –Uniform vs Natural.

The Australia Telescope National Facility Ray Norris CSIRO ATNF.

Creating a Silhouette in Illustrator. Go File>Place and place the photo on the artboard. Select the photo and click Live Trace (its on the tool bar right.

02/05/2002 (C) University of Wisconsin 2002, CS 559 Last Time Color Quantization Mach Banding –Humans exaggerate sharp boundaries, but not fuzzy ones.

MOS Data Reduction Michael Balogh University of Durham.

Max Tegmark Dept. of Physics, MIT Cosmologia en la Playa January 11-15, 2010 Measuring cosmological parameters.

Observations, Database, and Pipeline for EoR Jacqueline N. Hewitt MIT Kavli Institute for Astrophysics and Space Research Text.

Storage Centre HKOI 2007 Senior Q1 Kelly Choi. The Problem The COW Team are going to look for treasures in N ruins in a rectangular map. The COW Team.

Foreground Contamination and the EoR Window Nithyanandan Thyagarajan N. Udaya Shankar Ravi Subrahmanyan (Raman Research Institute, Bangalore)

Blind Component Separation for Polarized Obseravations of the CMB Jonathan Aumont, Juan-Francisco Macias-Perez Rencontres de Moriond 2006 La.

First Exploration of MWA Deep Field Point Source Population C. Williams - PhD Thesis J. Hewitt – reporting some analysis.

 Introduction to reciprocal space

Spectrum Reconstruction of Atmospheric Neutrinos with Unfolding Techniques Juande Zornoza UW Madison.

Imaging The EoR MWA Project Meeting EoR Session 5 June 2011 N. Udaya Shankar Raman Research Institute.

A latent Gaussian model for compositional data with structural zeroes Adam Butler & Chris Glasbey Biomathematics & Statistics Scotland.

Power Spectrum Estimation in Theory and in Practice Adrian Liu, MIT.

All-sky source search Aim: Look for a fast method to find sources over the whole sky Selection criteria Algorithms Iteration Simulations Energy information.

Planck working group 2.1 diffuse component separation review Paris november 2005.

EoR power spectrum systematics

Section II Digital Signal Processing ES & BM.

Constraining the redshift of reionization using a “modest” array

MECH 373 Instrumentation and Measurements

Lecture 15 Deconvolution CLEAN MEM Why does CLEAN work? Parallel CLEAN

Computer Vision Lecture 13: Image Segmentation III

Image Warping (Geometric Transforms)

Image Enhancement in the

Generalized Hough Transform

Wavelength Anomalies in UV-Vis Spectrophometry

M-OSRP 2006 Annual Meeting, June 6, 2007

Deconvolution and Multi frequency synthesis

Image Error Analysis Fourier-domain analysis of errors.

J/   analysis: results for ICHEP

Fourier Analysis Without Tears

J. Ellis, F. Jenet, & M. McLaughlin

Introduction to Sensor Interpretation

Introduction to Sensor Interpretation

DIGITAL IMAGE PROCESSING Elective 3 (5th Sem.)

Presentation transcript:

21 cm Foreground Subtraction in the Image Plane and in the uv-Plane Canberra MWA Project Meeting, 21st January, 2009 Max Tegmark, Adrian Liu, Matias Zaldarriaga & Jacqueline Hewitt

000619 Liu, Tegmark & Zaldarragia (2008) See also Bowman Ph.D. thesis & Bowman et al (2008)

000619

000619 Fitting a simple polynomial to each pixel… …works remarkably well

Fourier-space description of the subtraction algorithm The sudden increase in foreground residuals can be more easily understood in uv-plane. Old view: Fitting in frequency direction commutes with FT in transverse directions, so we can think of the fitting as taking place in uv-space. Start with the sky in uv-space. Instrument samples in uv. Fourier transform in transverse directions back into real space. Subtract foregrounds pixel-by-pixel. Fourier transform back to find the power spectrum.

Pixels in Fourier Space 1 1 2 3 2 Simple fits (in red) are unable to deal with missing pixels. Solution: skip frequencies with no data (in black) Emphasize how it’s not lack of uv coverage. It’s the fact that the incompleteness varies as a function of frequency. 3

Fitting in Fourier Space Tried two options: Binary weighted fit. Inverse-variance weighted fit. (Note: NOT the same as fitting to weighted data).

New algorithm as seen from real space New method requires fit to be done in real space. However, the effects of the method can still be seen in real space. Jagged Smooth Foregrounds are simulated to be smooth; jagged contributions due to missing frequencies at long baselines --> artificial. New method fit is better at tracing the smooth foreground component and provides a better map of the foregrounds.

Summary This is really good news! Sudden increase in foreground residuals at high k due to missing frequencies in spectra. Skipping missing frequencies in fits allows foregrounds to be cleaned to higher k. Negligible extra computational cost. Practical implications for MWA: probably fine to keep saving only real-space images, since we can FFT back before cleaning, but we we should check what maximum acceptable time integration is.