Approximate Bayesian computation: an application to weak-lensing peak counts
|
|
- Ronald Freeman
- 5 years ago
- Views:
Transcription
1 STATISTICAL CHALLENGES IN MODERN ASTRONOMY VI Approximate Bayesian computation: an application to weak-lensing peak counts Chieh-An Lin & Martin Kilbinger SAp, CEA Saclay Carnegie Mellon University, Pittsburgh June 6 th, 2016
2 Outline linc.tw 1 Weak-lensing peak counts 2 Approximate Bayesian computation 3 Data application ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th,
3 linc.tw Weak gravitational lensing in 3 minutes Source: ALMA ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th,
4 linc.tw Weak gravitational lensing in 3 minutes Unlensed sources Weak lensing ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th,
5 linc.tw Weak gravitational lensing in 3 minutes ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th,
6 linc.tw Weak gravitational lensing in 3 minutes κ map γ map ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th,
7 Weak-lensing peak counts linc.tw κ map and peaks Halo number density dn/dlogm [(Mpc/h) 3 ] σ 8 = 0.76 σ 8 = 0.82 σ 8 = 0.88 Halo mass function Halo mass [log(m/m h 1 )] Local maxima of the projected mass Probe the mass function Non-Gaussian information ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th,
8 linc.tw Dealing with selection function Early studies Count only the true clusters with high S/N Recent studies Include the selection effect into the model Analytical formalism N-body simulations Fast stochastic model (this work) ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th,
9 linc.tw A new model to predict weak-lensing peak counts Sample halos from a mass function PDF Sampled mass Assign density profiles, randomize their positions Compute the projected mass, add noise Make maps, create peak catalogues Public code in C: Camelus@GitHub See also Lin & Kilbinger (2015a) ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th,
10 Validation linc.tw Peak number density n peak [deg 2 ν 1 ] Noise-only Peak abundance histogram Full N-body runs N-body: halos NFW N-body: halos NFW + random position Our model S/N ν Lin & Kilbinger (2015a) ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th,
11 linc.tw Approximate Bayesian computation Likelihood ABC P(x π) L(π x obs ) P(x π) x x obs ɛ x obs x obs x x ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th,
12 linc.tw Approximate Bayesian computation ABC Requirements: Stochastic model P ( π) Distance x x Tolerance level ɛ P(x π) x x obs ɛ x obs x ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th,
13 linc.tw Approximate Bayesian computation ABC Accept-reject process: Draw a π from the prior P( ) Draw a x from the model P ( π) Accept π if x x obs ɛ Reject otherwise P(x π) x x obs ɛ One-sample test x obs x ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th,
14 linc.tw Approximate Bayesian computation Parameter space Data space prior P(π) ABC posterior P(x π i ) x x obs ɛ π i x obs π Distribution of accepted π = x prior green areas prior 2ɛ likelihood posterior ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th,
15 linc.tw Combined with population Monte Carlo Population Monte Carlo (PMC) Iterative solution for ɛ Set ɛ = + for the first iteration Do ABC Update ɛ and the prior based on results from the previous iteration Repeat until satifying a stop criterion See also Lin & Kilbinger (2015b) ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th,
16 linc.tw Comparison with the likelihood 1.1 Ω m -σ 8 constraints 1.0 abd5, ABC, credible 1-σ, 68.3% 2-σ, 95.4% abd, cg, credible 1-σ, 68.3% 2-σ, 95.4% Contours and dots: ABC Colored regions: likelihood σ Gain of time cost for ABC: 2 orders of magnitude Ω m Lin & Kilbinger (2015b) ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th,
17 Data from three surveys linc.tw Survey Field size Number of Effective density [deg 2 ] galaxies [deg 2 ] CFHTLenS M KiDS DR1/ M 5.33 DES SV M 6.65 ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th,
18 Various settings linc.tw Model settings Compensated filter (suggested by Lin et al. 2016) Adaptive choice for pixel sizes and filtering scales No intrinsic alignment and baryons (not yet!) ABC settings Data vector (summary statistic): peak counts with S/N > 2.5 of all scales Distance: a χ 2 -like normalized sum ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th,
19 Preliminary result linc.tw Ω m -σ 8 constraints PMC ABC 1-σ, 68.3% 2-σ, 95.4% 1.2 σ Lin & Kilbinger in prep Ω m ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th,
20 Preliminary result linc.tw Ω m -σ 8 constraints PMC ABC 1-σ, 68.3% 2-σ, 95.4% 1.2 σ Width: Σ 8 = 0.13 Area: FoM = Ω m ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th,
21 Preliminary result linc.tw σ Parameter constraints with PMC ABC 1-σ, 68.3% 2-σ, 95.4% w de Ω m σ 8 Lin & Kilbinger in prep. ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th,
22 linc.tw Ongoing improvements and perspectives S/N bin choice: less bias, more accuracy and precision Halo correlation Tomography Intrinsic alignment Baryonic effects ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th,
23 Summary A new model to predict WLPC Likelihood-free parameter inference: ABC Constraints with CFHTLenS, KiDS, DES Collaborators: Martin Kilbinger Austin Peel (poster talk on Wednesday) Sandrine Pires References: [ ] [ ] [ ] Camelus@GitHub
24 Backup slides
25 Advantages of our model linc.tw Fast Only few seconds for creating a 25-deg 2 field, without MPI or GPU programming Flexible Straightforward to include real-world effects (photo-z errors, masks, intrinsic alignment, baryonic effects, etc.) Full PDF information Allow more flexible constraint methods (varying covariances, copula, p-value, approximate Bayesian computation, etc.) ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th, 2016 B 1
26 Map examples linc.tw Noise-free map Noisy map Smoothed map Mask ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th, 2016 B 2
27 linc.tw Cosmology-dependent covariance 1.1 Ω m -σ 8 constraints 1.1 Ω m -σ 8 constraints 1.0 abd, cg, confidence 1-σ, 68.3% 2-σ, 95.4% 1.0 abd, vg, confidence 1-σ, 68.3% 2-σ, 95.4% abd, svg, confidence 1-σ, 68.3% 2-σ, 95.4% abd, svg, confidence 1-σ, 68.3% 2-σ, 95.4% σ 8 σ Ω m Ω m cg svg vg FoM Lin & Kilbinger (2015b) ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th, 2016 B 3
28 True likelihood linc.tw 1.1 Ω m -σ 8 constraints abd, true, confidence 1-σ, 68.3% 2-σ, 95.4% abd, vc, confidence 1-σ, 68.3% 2-σ, 95.4% σ Ω m Lin & Kilbinger (2015b) ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th, 2016 B 4
29 Degeneracy with w de 0 linc.tw σ Parameter constraints with likelihood Starlet, θ ker =2, 4, 8 1-σ, 68.3% 2-σ, 95.4% w de Ω m σ 8 Lin et al. (2016) ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th, 2016 B 5
30 Technical detail linc.tw Mass function from Jenkins et al. (2001) M-c relation from Takada & Jain (2002) Source redshift fitted from surveys Random source position, not catalogue Used raw galaxy densities, not effective Derive σ ɛ from the emperical total variance Pixel size: n gal θpix 2 7 Kaiser-Squires inversion Filtering with the starlet function Scale = 2, 4, 8 pixels Locally determined noise Dimension of x = 75 (= ) ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th, 2016 B 6
31 Field examples linc.tw -3 CFHTLenS W1 field 58 CFHTLenS W3 field CFHTLenS W4 field 4-6 Declination [deg] -9 Declination [deg] 55 Declination [deg] Right ascension [deg] Right ascension [deg] Right ascension [deg] KiDS G09a field KiDS G12 field DES SPT-E field Declination [deg] 2 Declination [deg] 0 Declination [deg] Right ascension [deg] Right ascension [deg] Right ascension [deg] ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th, 2016 B 7
32 Covariance linc.tw Correlation matrix CFHTLenS KiDS DES 1.0 DES KiDS CFHTLenS Lin & Kilbinger in prep. ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th, 2016 B 8
33 linc.tw Constrain concentration paramters σ Parameter constraints with PMC ABC 1-σ, 68.3% 2-σ, 95.4% w de c β NFW Ω m σ w de c 0 Lin & Kilbinger in prep. ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th, 2016 B 9
34 linc.tw Definition of Σ 8 Ω m -Σ 8 constraint Σ ln(l/l max ) Ω m pdf(σ 8 ) Definition 1 Σ 8 = σ 8(Ω m/0.27) α Σ 8 = Σ 8 = 0.16 α = 0.48 ( ) 1 α ( ) α Ω Definition 2 Σ 8 = m+β σ 8 1 α α Σ 8 = Σ 8 = 0.13 α = 0.38 β = 0.82 ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th, 2016 B 10
35 PMC ABC algorithm linc.tw set t = 0 for i = 1 to Q do ( ) generate θ (0) i ( from P( ) and x from P θ (0) i set δ (0) i = D x, x obs) and w (0) i = 1/Q end for set ɛ (1) = median ( δ (0) i ) and C (0) = cov ( π (0) i, w (0) i while success rate r stop do t t + 1 for i = 1 to Q do repeat { generate j from {1,..., Q} with weights w (t 1) 1,..., w (t 1) generate π (t) i set δ (t) i until δ (t) i set w (t) i from N ( π (t 1) j = D ( x, x obs) ɛ (t) P ( π (t) i ) / Q j=1 w(t 1) ) Q, C (t 1)) ( and x from P π (t) i j K ( π (t) i end for ( ) ( ) set ɛ (t+1) = median δ (t) i and C (t) = cov π (t) i, w (t) i end while π (t 1) j, C (t 1)) } ) ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th, 2016 B 11
36 Peaks v.s. power spectrum linc.tw Taken from Liu J. et al. (2015) ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th, 2016 B 12
37 Public code linc.tw Fast weak-lensing peak counts modelling in C with PMC ABC Camelus@GitHub ABC: an application to weak-lensing peak counts SCMA6, Pittsburgh Jun 6th, 2016 B 13
Cosmology with weak-lensing peak counts
Durham-Edinburgh extragalactic Workshop XIV IfA Edinburgh Cosmology with weak-lensing peak counts Chieh-An Lin January 8 th, 2018 Durham University, UK Outline Motivation Why do we study WL peaks? Problems
More informationShear Power of Weak Lensing. Wayne Hu U. Chicago
Shear Power of Weak Lensing 10 3 N-body Shear 300 Sampling errors l(l+1)c l /2π εε 10 4 10 5 Error estimate Shot Noise θ y (arcmin) 200 100 10 6 100 1000 l 100 200 300 θ x (arcmin) Wayne Hu U. Chicago
More informationCross-Correlation of CFHTLenS Galaxy Catalogue and Planck CMB Lensing
Cross-Correlation of CFHTLenS Galaxy Catalogue and Planck CMB Lensing A 5-month internship under the direction of Simon Prunet Adrien Kuntz Ecole Normale Supérieure, Paris July 08, 2015 Outline 1 Introduction
More informationWeak Lensing: Status and Prospects
Weak Lensing: Status and Prospects Image: David Kirkby & the LSST DESC WL working group Image: lsst.org Danielle Leonard Carnegie Mellon University Figure: DES Collaboration 2017 for LSST DESC June 25,
More informationXiuyuan Yang (Columbia University and BNL) Jan Kratochvil (University of Miami)
Xiuyuan Yang (Columbia University and BNL) Jan Kratochvil (University of Miami) Advisors: Morgan May (Brookhaven National Lab) Zoltan Haiman (Columbia University) Introduction Large surveys such as LSST
More informationMultimodal Nested Sampling
Multimodal Nested Sampling Farhan Feroz Astrophysics Group, Cavendish Lab, Cambridge Inverse Problems & Cosmology Most obvious example: standard CMB data analysis pipeline But many others: object detection,
More informationLSST Cosmology and LSSTxCMB-S4 Synergies. Elisabeth Krause, Stanford
LSST Cosmology and LSSTxCMB-S4 Synergies Elisabeth Krause, Stanford LSST Dark Energy Science Collaboration Lots of cross-wg discussions and Task Force hacks Junior involvement in talks and discussion Three
More informationFast Likelihood-Free Inference via Bayesian Optimization
Fast Likelihood-Free Inference via Bayesian Optimization Michael Gutmann https://sites.google.com/site/michaelgutmann University of Helsinki Aalto University Helsinki Institute for Information Technology
More informationWhat Gravitational Lensing tells us
What Gravitational Lensing tells us Catherine Heymans Institute for Astronomy, University of Edinburgh Six Six things lensing has has told told us us about Dark Matter How How lensing works First First
More informationCross-Correlation of Cosmic Shear and Extragalactic Gamma-ray Background
Cross-Correlation of Cosmic Shear and Extragalactic Gamma-ray Background Masato Shirasaki (Univ. of Tokyo) with Shunsaku Horiuchi (UCI), Naoki Yoshida (Univ. of Tokyo, IPMU) Extragalactic Gamma-Ray Background
More informationConstraining Dark Energy and Modified Gravity with the Kinetic SZ effect
Constraining Dark Energy and Modified Gravity with the Kinetic SZ effect Eva-Maria Mueller Work in collaboration with Rachel Bean, Francesco De Bernardis, Michael Niemack (arxiv 1408.XXXX, coming out tonight)
More informationWeak Lensing. Alan Heavens University of Edinburgh UK
Weak Lensing Alan Heavens University of Edinburgh UK Outline History Theory Observational status Systematics Prospects Weak Gravitational Lensing Coherent distortion of background images Shear, Magnification,
More informationMass and Hot Baryons from Cluster Lensing and SZE Observations
SZX HUNTSVILLE 2011 Mass and Hot Baryons from Cluster Lensing and SZE Observations Keiichi Umetsu Academia Sinica IAA (ASIAA), Taiwan September 19, 2011 Lensing collaborators: Collaborators T. Broadhurst
More informationStrong Lens Modeling (II): Statistical Methods
Strong Lens Modeling (II): Statistical Methods Chuck Keeton Rutgers, the State University of New Jersey Probability theory multiple random variables, a and b joint distribution p(a, b) conditional distribution
More informationGalaxy Bias from the Bispectrum: A New Method
Galaxy Bias from the Bispectrum: A New Method Jennifer E. Pollack University of Bonn Thesis Supervisor: Cristiano Porciani Collaborator: Robert E. Smith Motivation 1) Main Objective of state-of-the-art
More informationSampling versus optimization in very high dimensional parameter spaces
Sampling versus optimization in very high dimensional parameter spaces Grigor Aslanyan Berkeley Center for Cosmological Physics UC Berkeley Statistical Challenges in Modern Astronomy VI Carnegie Mellon
More informationApproximate Bayesian Computation
Approximate Bayesian Computation Michael Gutmann https://sites.google.com/site/michaelgutmann University of Helsinki and Aalto University 1st December 2015 Content Two parts: 1. The basics of approximate
More informationProbing Dark Matter Halos with Satellite Kinematics & Weak Lensing
Probing Dark Matter Halos with & Weak Lensing Frank C. van den Bosch (MPIA) Collaborators: Surhud More, Marcello Cacciato UMass, August 2008 Probing Dark Matter Halos - p. 1/35 Galaxy Formation in a Nutshell
More informationWeak Lensing: a Probe of Dark Matter and Dark Energy. Alexandre Refregier (CEA Saclay)
Weak Lensing: a Probe of Dark Matter and Dark Energy Alexandre Refregier (CEA Saclay) SLAC August 2004 Concordance ΛCDM Model Outstanding questions: initial conditions (inflation?) nature of the dark matter
More informationDiving into precision cosmology and the role of cosmic magnification
Diving into precision cosmology and the role of cosmic magnification Jose Luis Bernal Institute of Cosmos Science - Barcelona University ICC Winter Meeting 2017 06/02/2017 Jose Luis Bernal (ICCUB) ICC
More informationCosmology and Large Scale Structure
Cosmology and Large Scale Structure Alexandre Refregier PASCOS13 Taipei 11.22.2013 Matter Baryons Dark Matter Radiation Inflation Dark Energy Gravity Measuring the Dark Universe Geometry Growth of structure
More informationCosmological Constraints from a Combined Analysis of Clustering & Galaxy-Galaxy Lensing in the SDSS. Frank van den Bosch.
Cosmological Constraints from a Combined Analysis of Clustering & Galaxy-Galaxy Lensing in the SDSS In collaboration with: Marcello Cacciato (Leiden), Surhud More (IPMU), Houjun Mo (UMass), Xiaohu Yang
More informationThe Galaxy Dark Matter Connection
The Galaxy Dark Matter Connection constraining cosmology & galaxy formation Frank C. van den Bosch (MPIA) Collaborators: Houjun Mo (UMass), Xiaohu Yang (SHAO) Marcello Cacciato, Surhud More (MPIA) Kunming,
More informationNew techniques to measure the velocity field in Universe.
New techniques to measure the velocity field in Universe. Suman Bhattacharya. Los Alamos National Laboratory Collaborators: Arthur Kosowsky, Andrew Zentner, Jeff Newman (University of Pittsburgh) Constituents
More informationApproximate Bayesian Computation and Particle Filters
Approximate Bayesian Computation and Particle Filters Dennis Prangle Reading University 5th February 2014 Introduction Talk is mostly a literature review A few comments on my own ongoing research See Jasra
More informationRefining Photometric Redshift Distributions with Cross-Correlations
Refining Photometric Redshift Distributions with Cross-Correlations Alexia Schulz Institute for Advanced Study Collaborators: Martin White Introduction Talk Overview Weak lensing tomography can improve
More informationCatherine Heymans. Observing the Dark Universe with the Canada- France Hawaii Telescope Lensing Survey
Observing the Dark Universe with the Canada- France Hawaii Telescope Lensing Survey Catherine Heymans Institute for Astronomy, University of Edinburgh The intervening dark matter lenses the light from
More informationDark Energy. Cluster counts, weak lensing & Supernovae Ia all in one survey. Survey (DES)
Dark Energy Cluster counts, weak lensing & Supernovae Ia all in one survey Survey (DES) What is it? The DES Collaboration will build and use a wide field optical imager (DECam) to perform a wide area,
More informationITP, Universität Heidelberg Jul Weak Lensing of SNe. Marra, Quartin & Amendola ( ) Quartin, Marra & Amendola ( ) Miguel Quartin
ITP, Universität Heidelberg Jul 2013 Measuring σ8 with Weak Lensing of SNe Marra, Quartin & Amendola (1304.7689) Quartin, Marra & Amendola (1307.1155) Miguel Quartin Instituto de Física Univ. Federal do
More informationStatistics and Prediction. Tom
Statistics and Prediction Tom Kitching tdk@roe.ac.uk @tom_kitching David Tom David Tom photon What do we want to measure How do we measure Statistics of measurement Cosmological Parameter Extraction Understanding
More informationAre VISTA/4MOST surveys interesting for cosmology? Chris Blake (Swinburne)
Are VISTA/4MOST surveys interesting for cosmology? Chris Blake (Swinburne) Yes! Probes of the cosmological model How fast is the Universe expanding with time? How fast are structures growing within it?
More informationBaryon Acoustic Oscillations (BAO) in the Sloan Digital Sky Survey Data Release 7 Galaxy Sample
Baryon Acoustic Oscillations (BAO) in the Sloan Digital Sky Survey Data Release 7 Galaxy Sample BOMEE LEE 1. Brief Introduction about BAO In our previous class we learned what is the Baryon Acoustic Oscillations(BAO).
More informationScience with large imaging surveys
Science with large imaging surveys Hiranya V. Peiris University College London Science from LSS surveys: A case study of SDSS quasars Boris Leistedt (UCL) with Daniel Mortlock (Imperial) Aurelien Benoit-Levy
More informationThe State of Tension Between the CMB and LSS
The State of Tension Between the CMB and LSS Tom Charnock 1 in collaboration with Adam Moss 1 and Richard Battye 2 Phys.Rev. D91 (2015) 10, 103508 1 Particle Theory Group University of Nottingham 2 Jodrell
More informationCMB Lensing Combined with! Large Scale Structure:! Overview / Science Case!
CMB Lensing Combined with! Large Scale Structure:! Overview / Science Case! X Blake D. Sherwin Einstein Fellow, LBNL Outline! I. Brief Introduction: CMB lensing + LSS as probes of growth of structure II.
More informationData Analysis Methods
Data Analysis Methods Successes, Opportunities, and Challenges Chad M. Schafer Department of Statistics & Data Science Carnegie Mellon University cschafer@cmu.edu The Astro/Data Science Community LSST
More informationDetection of hot gas in multi-wavelength datasets. Loïc Verdier DDAYS 2015
Detection of hot gas in multi-wavelength datasets Loïc Verdier SPP DDAYS 2015 Loïc Verdier (SPP) Detection of hot gas in multi-wavelength datasets DDAYS 2015 1 / 21 Cluster Abell 520; Credit: X-ray: NASA/CXC/UVic./A.Mahdavi
More informationarxiv: v1 [astro-ph.co] 3 Apr 2019
Forecasting Cosmological Bias due to Local Gravitational Redshift Haoting Xu, Zhiqi Huang, Na Zhang, and Yundong Jiang School of Physics and Astronomy, Sun Yat-sen University, 2 Daxue Road, Tangjia, Zhuhai,
More informationThe ultimate measurement of the CMB temperature anisotropy field UNVEILING THE CMB SKY
The ultimate measurement of the CMB temperature anisotropy field UNVEILING THE CMB SKY PARAMETRIC MODEL 16 spectra in total C(θ) = CMB theoretical spectra plus physically motivated templates for the
More informationTutorial on Approximate Bayesian Computation
Tutorial on Approximate Bayesian Computation Michael Gutmann https://sites.google.com/site/michaelgutmann University of Helsinki Aalto University Helsinki Institute for Information Technology 16 May 2016
More informationGravitational Lensing of the CMB
Gravitational Lensing of the CMB SNAP Planck 1 Ω DE 1 w a.5-2 -1.5 w -1 -.5 Wayne Hu Leiden, August 26-1 Outline Gravitational Lensing of Temperature and Polarization Fields Cosmological Observables from
More informationCosmic shear analysis of archival HST/ACS data
1 Patrick Simon 2,1 Thomas Erben 1 Peter Schneider 1 Jan Hartlap 1 Catherine Heymans 3 Phil Marshall 4,5 Chris Fassnacht 6 Eric Morganson 4 Marusa Bradac 4,5 Hendrik Hildebrandt 1 Marco Hetterscheidt 1
More informationWeak lensing measurements of Dark Matter Halos around galaxies
Weak lensing measurements of Dark Matter Halos around galaxies Rachel Mandelbaum Carnegie Mellon University 1 Image credits: NASA, ESA, S. Beckwith (STScI), the HUDF Team 2 Image credit: ESA/Planck 3 The
More informationMapping Dark Matter with the Dark Energy Survey
Mapping Dark Matter with the Dark Energy Survey Tim Eifler On behalf of many people in the DES collaboration DaMaSC IV: Beyond WIMP Dark Matter Aug. 30, 2017 Disclaimer DES has recently published Year
More informationDRAGON ADVANCED TRAINING COURSE IN ATMOSPHERE REMOTE SENSING. Inversion basics. Erkki Kyrölä Finnish Meteorological Institute
Inversion basics y = Kx + ε x ˆ = (K T K) 1 K T y Erkki Kyrölä Finnish Meteorological Institute Day 3 Lecture 1 Retrieval techniques - Erkki Kyrölä 1 Contents 1. Introduction: Measurements, models, inversion
More informationCosmology on small scales: Emulating galaxy clustering and galaxy-galaxy lensing into the deeply nonlinear regime
Cosmology on small scales: Emulating galaxy clustering and galaxy-galaxy lensing into the deeply nonlinear regime Ben Wibking Department of Astronomy Ohio State University with Andres Salcedo, David Weinberg,
More informationGravitational lensing
Gravitational lensing Martin White UC Berkeley Collaborators: Alexandre Amblard Henk Hoekstra Masahiro Takada Shirley Ho Dragan Huterer Ludo van Waerbeke Chris Vale Outline What and why? Background and
More informationCosmology with Galaxy bias
Cosmology with Galaxy bias Enrique Gaztañaga, M.Eriksen (PhD in progress...) www.ice.cat/mice Figure of Merit (FoM): Expansion x Growth w(z) -> Expansion History (background metric) we will use w0 and
More informationPATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 13: SEQUENTIAL DATA
PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 13: SEQUENTIAL DATA Contents in latter part Linear Dynamical Systems What is different from HMM? Kalman filter Its strength and limitation Particle Filter
More informationProbabilistic photometric redshifts in the era of Petascale Astronomy
Probabilistic photometric redshifts in the era of Petascale Astronomy Matías Carrasco Kind NCSA/Department of Astronomy University of Illinois at Urbana-Champaign Tools for Astronomical Big Data March
More informationDr Carolyn Devereux - Daphne Jackson Fellow Dr Jim Geach Prof. Martin Hardcastle. Centre for Astrophysics Research University of Hertfordshire, UK
Millennium simulation of the cosmic web MEASUREMENTS OF THE LINEAR BIAS OF RADIO GALAXIES USING CMB LENSING FROM PLANCK Dr Carolyn Devereux - Daphne Jackson Fellow Dr Jim Geach Prof. Martin Hardcastle
More informationEfficient Likelihood-Free Inference
Efficient Likelihood-Free Inference Michael Gutmann http://homepages.inf.ed.ac.uk/mgutmann Institute for Adaptive and Neural Computation School of Informatics, University of Edinburgh 8th November 2017
More informationarxiv: v1 [astro-ph.co] 5 Jul 2017
Astronomy & Astrophysics manuscript no. shear_ c ESO 2018 April 29, 2018 Shear measurement : dependencies on methods, simulation parameters and measured parameters Arnau Pujol 1, Florent Sureau 1, Jerome
More informationRCSLenS galaxy cross-correlations with WiggleZ and BOSS
RCSLenS galaxy cross-correlations with WiggleZ and BOSS Chris Blake, 24 June 2013 1 Scope The scope of this investigation is to measure the cross-correlation between RCSLenS shapes (two-thirds of which
More informationDetermining neutrino masses from cosmology
Determining neutrino masses from cosmology Yvonne Y. Y. Wong The University of New South Wales Sydney, Australia NuFact 2013, Beijing, August 19 24, 2013 The cosmic neutrino background... Embedding the
More informationThe Besançon Galaxy Model development
The Besançon Galaxy Model development Annie C. Robin and collaborators Institut UTINAM, OSU THETA, Université Bourgogne-Franche-Comté, Besançon, France Outline Population synthesis principles New scheme
More informationStatistical Tools and Techniques for Solar Astronomers
Statistical Tools and Techniques for Solar Astronomers Alexander W Blocker Nathan Stein SolarStat 2012 Outline Outline 1 Introduction & Objectives 2 Statistical issues with astronomical data 3 Example:
More informationState Space Models for Wind Forecast Correction
for Wind Forecast Correction Valérie 1 Pierre Ailliot 2 Anne Cuzol 1 1 Université de Bretagne Sud 2 Université de Brest MAS - 2008/28/08 Outline 1 2 Linear Model : an adaptive bias correction Non Linear
More informationCosmoSIS Webinar Part 1
CosmoSIS Webinar Part 1 An introduction to cosmological parameter estimation and sampling Presenting for the CosmoSIS team: Elise Jennings (Fermilab, KICP) Joe Zuntz (University of Manchester) Vinicius
More informationDusty Starforming Galaxies: Astrophysical and Cosmological Relevance
Dusty Starforming Galaxies: Astrophysical and Cosmological Relevance Andrea Lapi SISSA, Trieste, Italy INFN-TS, Italy INAF-OATS, Italy in collaboration with F. Bianchini, C. Mancuso C. Baccigalupi, L.
More informationThe Kalman Filter ImPr Talk
The Kalman Filter ImPr Talk Ged Ridgway Centre for Medical Image Computing November, 2006 Outline What is the Kalman Filter? State Space Models Kalman Filter Overview Bayesian Updating of Estimates Kalman
More informationExtending Robust Weak Lensing Masses to z~1. Douglas Applegate, Tim Schrabback & the SPT-Lensing Team
Extending Robust Weak Lensing Masses to z~1 Douglas Applegate, Tim Schrabback & the SPT-Lensing Team 1 SPT Lensing Team Bonn Tim Schrabback Douglas Applegate Fatimah Raihan Chicago Brad Benson Lindsay
More informationImage Processing in Astrophysics
AIM-CEA Saclay, France Image Processing in Astrophysics Sandrine Pires sandrine.pires@cea.fr NDPI 2011 Image Processing : Goals Image processing is used once the image acquisition is done by the telescope
More informationLecture 2: From Linear Regression to Kalman Filter and Beyond
Lecture 2: From Linear Regression to Kalman Filter and Beyond Department of Biomedical Engineering and Computational Science Aalto University January 26, 2012 Contents 1 Batch and Recursive Estimation
More informationMCMC Sampling for Bayesian Inference using L1-type Priors
MÜNSTER MCMC Sampling for Bayesian Inference using L1-type Priors (what I do whenever the ill-posedness of EEG/MEG is just not frustrating enough!) AG Imaging Seminar Felix Lucka 26.06.2012 , MÜNSTER Sampling
More information2D Image Processing (Extended) Kalman and particle filter
2D Image Processing (Extended) Kalman and particle filter Prof. Didier Stricker Dr. Gabriele Bleser Kaiserlautern University http://ags.cs.uni-kl.de/ DFKI Deutsches Forschungszentrum für Künstliche Intelligenz
More informationEvolution of galaxy clustering in the ALHAMBRA Survey
Evolution of galaxy clustering in the ALHAMBRA Survey Pablo Arnalte-Mur (Observatori Astronòmic - Univ. de València) with L. Hurtado-Gil (OAUV/IFCA), V.J. Martínez (OAUV), A. Fernández-Soto (IFCA) & The
More informationThe Degeneracy of Dark Energy and Curvature
The Degeneracy of Dark Energy and Curvature Department of Physics and Astronomy, UWC, Cape Town Department of MAM, UCT, Cape Town PhD student: Amadeus Witzemann Collaborators: Philip Bull, HIRAX coll.
More informationApproximate Bayesian Computation for the Stellar Initial Mass Function
Approximate Bayesian Computation for the Stellar Initial Mass Function Jessi Cisewski Department of Statistics Yale University SCMA6 Collaborators: Grant Weller (Savvysherpa), Chad Schafer (Carnegie Mellon),
More informationAdvanced Statistical Methods. Lecture 6
Advanced Statistical Methods Lecture 6 Convergence distribution of M.-H. MCMC We denote the PDF estimated by the MCMC as. It has the property Convergence distribution After some time, the distribution
More informationLarge-scale structure as a probe of dark energy. David Parkinson University of Sussex, UK
Large-scale structure as a probe of dark energy David Parkinson University of Sussex, UK Question Who was the greatest actor to portray James Bond in the 007 movies? a) Sean Connery b) George Lasenby c)
More informationF & B Approaches to a simple model
A6523 Signal Modeling, Statistical Inference and Data Mining in Astrophysics Spring 215 http://www.astro.cornell.edu/~cordes/a6523 Lecture 11 Applications: Model comparison Challenges in large-scale surveys
More informationWeighing the Giants:
Weighing the Giants: Accurate Weak Lensing Mass Measurements for Cosmological Cluster Surveys Anja von der Linden Tycho Brahe Fellow DARK Copenhagen + KIPAC, Stanford IACHEC, May 14, 2014 1 Hello! Copenhagen
More informationarxiv:astro-ph/ v1 11 Oct 2002
DRAFT VERSION MARCH 5, 2008 Preprint typeset using L A TEX style emulateapj v. 14/09/00 THE THREE-POINT CORRELATION FUNCTION FOR SPIN-2 FIELDS MASAHIRO TAKADA AND BHUVNESH JAIN Department of Physics and
More informationData Analysis I. Dr Martin Hendry, Dept of Physics and Astronomy University of Glasgow, UK. 10 lectures, beginning October 2006
Astronomical p( y x, I) p( x, I) p ( x y, I) = p( y, I) Data Analysis I Dr Martin Hendry, Dept of Physics and Astronomy University of Glasgow, UK 10 lectures, beginning October 2006 4. Monte Carlo Methods
More informationNew Probe of Dark Energy: coherent motions from redshift distortions Yong-Seon Song (Korea Institute for Advanced Study)
New Probe of Dark Energy: coherent motions from redshift distortions Yong-Seon Song (Korea Institute for Advanced Study) 1 Future wide-deep surveys Photometric wide-deep survey Spectroscopic wide-deep
More informationSimulations and the Galaxy Halo Connection
Simulations and the Galaxy Halo Connection Yao-Yuan Mao (Stanford/SLAC PITT PACC) @yaoyuanmao yymao.github.io SCMA6 @ CMU 6/10/16 My collaborators at Stanford/SLAC Joe DeRose Ben Lehmann ( UCSC) Vincent
More informationThe shapes of faint galaxies: A window unto mass in the universe
Lecture 15 The shapes of faint galaxies: A window unto mass in the universe Intensity weighted second moments Optimal filtering Weak gravitational lensing Shear components Shear detection Inverse problem:
More informationCosmic shear and cosmology
Cosmic shear and cosmology Overview Second-order cosmic shear statistics Shear tomography (2 1/2 D lensing) Third-order cosmic shear statistics 3D lensing Peak statistics Shear-ratio geometry test (Flexion)
More informationDurham Lightcones: Synthetic galaxy survey catalogues from GALFORM. Jo Woodward, Alex Merson, Peder Norberg, Carlton Baugh, John Helly
Durham Lightcones: Synthetic galaxy survey catalogues from GALFORM Jo Woodward, Alex Merson, Peder Norberg, Carlton Baugh, John Helly Outline 1. How are the lightcone mocks constructed? 2. What mocks are
More informationCosmology with high (z>1) redshift galaxy surveys
Cosmology with high (z>1) redshift galaxy surveys Donghui Jeong Texas Cosmology Center and Astronomy Department University of Texas at Austin Ph. D. thesis defense talk, 17 May 2010 Cosmology with HETDEX
More informationDETECTION OF HALO ASSEMBLY BIAS AND THE SPLASHBACK RADIUS
DETECTION OF HALO ASSEMBLY BIAS AND THE SPLASHBACK RADIUS Surhud More (Kavli-IPMU) Collaborators: Hironao Miyatake (JPL), Masahiro Takada (Kavli IPMU), David Spergel (Princeton), Rachel Mandelbaum (CMU),
More informationAges of stellar populations from color-magnitude diagrams. Paul Baines. September 30, 2008
Ages of stellar populations from color-magnitude diagrams Paul Baines Department of Statistics Harvard University September 30, 2008 Context & Example Welcome! Today we will look at using hierarchical
More informationConstraints on dark matter annihilation cross section with the Fornax cluster
DM Workshop@UT Austin May 7, 2012 Constraints on dark matter annihilation cross section with the Fornax cluster Shin ichiro Ando University of Amsterdam Ando & Nagai, arxiv:1201.0753 [astro-ph.he] Galaxy
More informationThree data analysis problems
Three data analysis problems Andreas Zezas University of Crete CfA Two types of problems: Fitting Source Classification Fitting: complex datasets Fitting: complex datasets Maragoudakis et al. in prep.
More informationApproximate Bayesian computation: methods and applications for complex systems
Approximate Bayesian computation: methods and applications for complex systems Mark A. Beaumont, School of Biological Sciences, The University of Bristol, Bristol, UK 11 November 2015 Structure of Talk
More informationFisher Matrix Analysis of the Weak Lensing Spectrum
Fisher Matrix Analysis of the Weak Lensing Spectrum Manuel Rabold Institute for Theoretical Physics, University of Zurich Fisher Matrix Analysis of the Weak Lensing Spectrum Manuel Rabold Aarhus University,
More informationA5682: Introduction to Cosmology Course Notes. 11. CMB Anisotropy
Reading: Chapter 8, sections 8.4 and 8.5 11. CMB Anisotropy Gravitational instability and structure formation Today s universe shows structure on scales from individual galaxies to galaxy groups and clusters
More informationSome issues in cluster cosmology
Some issues in cluster cosmology Tim McKay University of Michigan Department of Physics 1/30/2002 CFCP Dark Energy Workshop 1 An outline Cluster counting in theory Cluster counting in practice General
More informationProbing the covariance matrix
Probing the covariance matrix Kenneth M. Hanson Los Alamos National Laboratory (ret.) BIE Users Group Meeting, September 24, 2013 This presentation available at http://kmh-lanl.hansonhub.com/ LA-UR-06-5241
More informationAddressing the Challenges of Luminosity Function Estimation via Likelihood-Free Inference
Addressing the Challenges of Luminosity Function Estimation via Likelihood-Free Inference Chad M. Schafer www.stat.cmu.edu/ cschafer Department of Statistics Carnegie Mellon University June 2011 1 Acknowledgements
More informationMapping the z 2 Large-Scale Structure with 3D Lyα Forest Tomography
Mapping the z 2 Large-Scale Structure with 3D Lyα Forest Tomography Intergalactic Matters Meeting, MPIA Heidelberg Max Planck Institut für Astronomie Heidelberg, Germany June 16, 2014 Collaborators: Joe
More informationLarge Imaging Surveys for Cosmology:
Large Imaging Surveys for Cosmology: cosmic magnification AND photometric calibration Alexandre Boucaud Thesis work realized at APC under the supervision of James G. BARTLETT and Michel CRÉZÉ Outline Introduction
More informationWeak gravitational lensing of CMB
Weak gravitational lensing of CMB (Recent progress and future prospects) Toshiya Namikawa (YITP) Lunch meeting @YITP, May 08, 2013 Cosmic Microwave Background (CMB) Precise measurements of CMB fluctuations
More informationLecture 2: From Linear Regression to Kalman Filter and Beyond
Lecture 2: From Linear Regression to Kalman Filter and Beyond January 18, 2017 Contents 1 Batch and Recursive Estimation 2 Towards Bayesian Filtering 3 Kalman Filter and Bayesian Filtering and Smoothing
More informationShape Measurement: An introduction to KSB
Shape Measurement: An introduction to KSB Catherine Heymans Institute for Astronomy, University of Edinburgh, UK DUEL Weak Lensing School September 2009 at the end of the week you ll be able to... Use
More informationCosmology with Planck clusters. Nabila Aghanim IAS, Orsay (France)
Cosmology with Planck clusters Nabila Aghanim IAS, Orsay (France) Starting point Planck results March 2013 ~3σ sigma tension between Planck CMB and cluster counts Planck results February 2015 extensions:
More informationStatistical Data Analysis Stat 3: p-values, parameter estimation
Statistical Data Analysis Stat 3: p-values, parameter estimation London Postgraduate Lectures on Particle Physics; University of London MSci course PH4515 Glen Cowan Physics Department Royal Holloway,
More informationMeasuring BAO using photometric redshift surveys.
Measuring BAO using photometric redshift surveys. Aurelio Carnero Rosell. E. Sanchez Alvaro, Juan Garcia-Bellido, E. Gaztanaga, F. de Simoni, M. Crocce, A. Cabre, P. Fosalba, D. Alonso. 10-08-10 Punchline
More informationPhotometric Redshifts, DES, and DESpec
Photometric Redshifts, DES, and DESpec Huan Lin, Photo-z s, DES, and DESpec, DESPec Workshop, KICP, Chicago, 30 May 2012 Outline DES photo-z calibrations: spectroscopic training set fields DES photo-z
More information