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The SuperCLASS Weak Lensing Deep Field Survey

This article explores the potential of radio weak lensing as a powerful tool for studying dark energy, cosmology, and the evolution of dark matter structures. It discusses the challenges of measuring ellipticities accurately and controlling systematics in radio observations.

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The SuperCLASS Weak Lensing Deep Field Survey

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  1. The SuperCLASS Weak Lensing Deep Field Survey Ian Harrison on behalf of the SuperCLASS collaboration AASTCS 2: Exascale Radio Astronomy 4 April 2014

  2. Introduction to Weak Lensing Radio Weak Lensing • Promises and challenges • Shape measurement with radio data SuperCLASS Survey • Description and status SuperCLuster Assisted Shear Survey Overview/Contents Pathfinder for weak lensing cosmologywith the SKA using UK e-Merlin

  3. Weak Lensing as a Cosmological Probe • Coherent distortion of background sources • …by baryonic and dark matter • Measure integrated mass on line of sight between us and source • Traces evolution of dark matter structures

  4. Weak Lensing as a Cosmological Probe • Track Dark Energy equation of state and how it evolves with time • Learn about DE physical nature • Cosmological constant? • Scalar field? • Modifications to GR? • Weak Lensing can be the best probe of Dark Energy Dark Energy Task Force FoM WL

  5. Weak Lensing as a Cosmological Probe Requirements • Large numbers of resolved background galaxies • Beat down random shape noise • ‘Exquisitely’ precise/accurate measurement of ellipticities ~1% level for detection ~0.01% level for 1% constraint on DE equation of state Systematics are key!

  6. Point-Spread-Function errors • Uncertainty in telescope, seeing • …even in space Intrinsic alignments • Galaxy ellipticities/orientations not random due to sharing of LSS environment Redshift uncertainties • Photo-zs can put sources in wrong tomographic bin Weak Lensing as a Cosmological Probe Optical Systematics

  7. Weak Lensing as a Cosmological Probe Systematics – How bad? Bad…

  8. PSF Errors • Radio interferometer beams are (in principle) • Precisely known • Highly deterministic Intrinsic alignments (Brown & Battye 2011) • Radio polarisation information tells about intrinsic alignment • Polarisation angle unchanged by gravitational lensing Redshift uncertainties • Large 21cm line surveys give spec-z for sources Cross Correlations • Euclid comparable, similar timescale to SKA The Promise of Radio Weak Lensing Control of Systematics

  9. The Promise of Radio Weak Lensing Current Status Patel et al (2010) • Merlin+VLA data • 0.4 arcsec resolution • 50 μJy depth • Only 70 arcmin2 • ~1-4 sources arcmin-2 • ~50-300 sources • No detection of cosmic shear • Measure shapes in images Chang, Refregier, Helfand (2004) • VLA FIRST data • 5 arcsec resolution • 1 mJy depth • 104 deg2 • ~20 sources deg-2 • ~20,000 source • 3σ detection of cosmic shear • Measure shapes in UV plane

  10. The Promise of Radio Weak Lensing Measuring Ellipticities • One method:shapelets • Model image using truncated basis • …or visibilities • FT is just a phase factor • Gives linear problem • Easy to solve χ2 for best-fitting coefficients • Can estimate shear from combination of coefficients

  11. The Promise of Radio Weak Lensing Current Status Chang, Refregier, Helfand (2004) • Take source positions from images • Use Fourier-plane shapelets to model visibilities directly • Model systematics with simulations of delta-function sources • 3σ detection

  12. The Promise of Radio Weak Lensing Current Status Patel et al (2010) • Use real-space shapelet basis functions • Model sources in reconstructed images • No shear signal recovered • Also cross-correlate with optical data (HDF-North) • Find no correlation

  13. The Promise of Radio Weak Lensing Current Status Patel et al (2013) • Simulate e-Merlin and LOFAR observations • Known input ellipticities • Noise free… • Measure shear using image plane shapelets • Quantify accuracy of fit εobs – εtrue = mεtrue + c Amara & Refregier (2008) gives: m < 0.05 c < 0.0075 For simulated survey to be dominated by statistics, not systematics m < 0.001 c < 0.0002 for SKA

  14. Understanding of shape measurement algorithms for radio data currently ‘not good’ Only 1.5 methods have been tried • On different datasets Are N potential shape measurement methods • Which galaxy model? • Physically motivated (e.g. Sersic) • Image decomposition (e.g. Shapelets) • Which data? • UV • Image • Method space needs exploring The Promise of Radio Weak Lensing Challenges of Radio Shape Measurement

  15. The Promise of Radio Weak Lensing Challenges of Radio Shape Measurement • Image Plane • Only fit one object at a time • Optical algorithms can be easily leveraged • Correlated noise • Need to create image with no spurious shear from deconvolution! • Is a big challenge in itself… • UV Plane • Does not require deconvolution • Need to fit sources simultaneously! • ~5 parameters per source • ~100 sources per FoV • ~10n data points • (Probably) still need to image to source find • Probably won’t have visibilities any more

  16. Understanding of shape measurement algorithms for radio data currently ‘not good’ Optical weak lensing community has gained much from shape measurement challenges • STEP, STEP2, GREAT08, GREAT10, GREAT3 • Simulate weak lensing data set • Different algorithms compete to measure (blinded) shear in the data with greatest fidelity • Winners have come from non-astronomy backgrounds A Radio GREAT Challenge (Gravitational lEnsing Accuracy Test) • => A GREAT Challenge for radio data

  17. (Very simple) overview: Create sky model Simulate observation with a single pointing of a known antenna configuration Provide entrants with • Visibilities • Fiducial image with quantified systematics due to deconvolution Help and ideas welcome… Sign up for updates! jb.man.ac.uk/~harrison/ A Radio GREAT Challenge Plans

  18. SuperCLASS e-Merlin legacy survey Pathfinder for radio weak lensing with the SKA

  19. SuperCLASS Goals • Develop techniques for radio shear measurement • Prove effectiveness of polarisation for mitigation of intrinsic alignments • Learn about source populations at μJy radio fluxes which will be probed by SKA surveys • Number densities • Polarisation fraction and position angle scatter • ~few % and rms 10-20 deg for local spirals (Stil et al 2009)

  20. SuperCLASS The Survey • Specifications/performance goals: • 1.75 deg2 • 4μJy/beam flux rms • L-band (1.4 GHz), 512MHz bandwidth • 0.2 arcsecond resolution • 1-2 arcmin-2 source density • Dense supercluster target field • Observing strategy: • ~800 hours total • 430 mosaic pointings • ~20TB visibilities on disk

  21. SuperCLASS Collaboration David Bacon Bob Nichol Richard Battye (PI) Michael Brown Neal Jackson Ian Browne Simon Garrington Paddy Leahy Peter Wilkinson Anita Richards Scott Kay Rob Beswick Tom Muxlowe Sarah Bridle Lee Whittaker Constantinos Demetroullas Ian Harrison Rafal Szepietowski Torsten Ensslin Mike Bell Steve Myers Chris Hales Anna Scaife Chris Riseley Ian Smail Caitlin Casey Mark Birkinshaw Hung Chao-Ling 30 People 11 Institutions 3 Countries Meghan Gray Filipe Abdalla

  22. SuperCLASS e-Merlin Pipeline • What it does: • Loading & sorting • Averaging • Concatenating • Flagging • Diagnostic plotting • Calibration (with caveats) • What it doesn’t (yet) do: • Perfect calibration • Spectral line mode • Multiple source/phcal pairs • Wide-field imaging • Publication-quality images • Currently uses standard e-Merlin data reduction pipeline(Argo et al, in prep) • Requires ParselTongue, AIPS, Obit (from Megan Argo)

  23. Merlin data manual reduction e-Merlin data one button reduction SuperCLASS e-Merlin Pipeline (from Megan Argo)

  24. SERPent automated flagging of RFI (Peck & Fenech 2013) Fully parallelised Flags ~7GbCPU-1day-1 • Uses SumThreshold algorithm (Offringa et al 2010) • Subset of visibilitesthresholded • If above, flag to threshold level SuperCLASS RFI Mitigation

  25. SuperCLASS Current Status • Characterisation of polarisation leakage across field of view • Appears to be stable in time, position • Calibratable • Have observed initial 7 point mosaic • ~12 hours total • mJy sources visible in total intensity (from Neal Jackson)

  26. SuperCLASS Projected Performance (Brown & Battye 2011) • Expect up to 10σ detection of shear from each cluster • Lower limit should be ~6.6σ • Expected across a whole randomly chosen field

  27. Data Science Source populations at μJy fluxes Magnetic fields in super-clusters Dynamic state of ICM Strong lenses SuperCLASS Additional Data and Science • LOFAR • 120 – 180 MHz • GMRT • 325MHz • JVLA (proposed) • Short baselines • Optical data from Subaru SuprimeCam • Photometric redshifts

  28. Radio weak lensing can do good cosmology • Mitigates many systematics from optical surveys • Deterministic beam • Polarisation for intrinsic alignments (Brown & Battye 2011) • Cross-correlations (Euclid comparable, on same timescale to SKA) …but will be difficult • What are properties of sources? • How will we do the shape measurement? radioGREAT challenge for shape measurement from simulations jb.man.ac.uk/~harrison SuperCLASS providing real data to form a test bed SuperCLASS Summary

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