25 YEARS AFTER THE DISCOVERY: SOME CURRENT TOPICS ON LENSED QSOs Santander (Spain), 15th-17th December 2004 Estimation of time delays from unresolved photometry.

Slides:



Advertisements
Similar presentations
DCSP-12 Jianfeng Feng
Advertisements

Optical monitoring and time delay determination in the gravitationally lensed quasar UM673 E. Koptelova (1,2), V. Oknyanskij (2), B. Artamonov (2), W.-P.
Simple Linear Regression and Correlation by Asst. Prof. Dr. Min Aung.
Statistics Review – Part II Topics: – Hypothesis Testing – Paired Tests – Tests of variability 1.
Copyright © Cengage Learning. All rights reserved. 14 Partial Derivatives.
DFT/FFT and Wavelets ● Additive Synthesis demonstration (wave addition) ● Standard Definitions ● Computing the DFT and FFT ● Sine and cosine wave multiplication.
Signal Spaces.
Challenge the future Delft University of Technology Blade Load Estimations by a Load Database for an Implementation in SCADA Systems Master Thesis.
STAT 497 APPLIED TIME SERIES ANALYSIS
Mixture Designs Simplex Lattice Simplex Centroid
Image processing. Image operations Operations on an image –Linear filtering –Non-linear filtering –Transformations –Noise removal –Segmentation.
Chapter 1 Ways of Seeing. Ways of Seeing the Atmosphere The behavior of the atmosphere is very complex. Different ways of displaying the characteristics.
UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering On-line Alert Systems for Production Plants A Conflict Based Approach.
25 YEARS AFTER THE DISCOVERY: SOME CURRENT TOPICS ON LENSED QSOs Santander (Spain), 15th-17th December 2004 Transverse velocities of QSOs from microlensing.
Some results from an optical monitoring of four quasars at Calar Alto Observatory (Almería, Spain) Aurora Ullán (UC) Jan-Erik Ovaldsen (University of Oslo)
25 YEARS AFTER THE DISCOVERY: SOME CURRENT TOPICS ON LENSED QSOs Santander (Spain), 15th-17th December 2004 FLUX RATIO ANOMALY by Luis J. Goicoechea (UC)
Universe Eighth Edition Universe Roger A. Freedman William J. Kaufmann III CHAPTER 17 The Nature of Stars CHAPTER 17 The Nature of Stars.
September 14, 2010Neural Networks Lecture 3: Models of Neurons and Neural Networks 1 Visual Illusions demonstrate how we perceive an “interpreted version”
25 YEARS AFTER THE DISCOVERY: SOME CURRENT TOPICS ON LENSED QSOs Santander (Spain), 15th-17th December 2004 Photometric monitoring of SBS : long-term.
Review of Matrix Algebra
25 YEARS AFTER THE DISCOVERY: SOME CURRENT TOPICS ON LENSED QSOs Santander (Spain), 15th-17th December 2004 Short-time scale variability in gravitationally.
Chapter 11 Multiple Regression.
25 YEARS AFTER THE DISCOVERY: SOME CURRENT TOPICS ON LENSED QSOs Santander (Spain), 15th-17th December 2004 Monitoring Projects: Introduction Dan Maoz,
Gravitational lensing of QSO spectra Luka Č. Popović Astronomical Observatory, Belgrade – AvH fellow, AIP, Potsdam George Chartas Astronomy and Astrophysics.
1 4. Multiple Regression I ECON 251 Research Methods.
Boyce/DiPrima 9th ed, Ch 11.2: Sturm-Liouville Boundary Value Problems Elementary Differential Equations and Boundary Value Problems, 9th edition, by.
1 Lecture 9: Diversity Chapter 7 – Equalization, Diversity, and Coding.
1 10. Joint Moments and Joint Characteristic Functions Following section 6, in this section we shall introduce various parameters to compactly represent.
7.1 Lecture 10/29.
Separate multivariate observations
HELSINKI UNIVERSITY OF TECHNOLOGY LABORATORY OF COMPUTER AND INFORMATION SCIENCE NEURAL NETWORKS RESEACH CENTRE Variability of Independent Components.
Building Adders & Sub tractors Dr Ahmed Telba. Introducing adder circuits Adder circuits are essential inside microprocessors as part of the ALU, or arithmetic.
1 CSI5388 Data Sets: Running Proper Comparative Studies with Large Data Repositories [Based on Salzberg, S.L., 1997 “On Comparing Classifiers: Pitfalls.
Two and a half problems in homogenization of climate series concluding remarks to Daily Stew Ralf Lindau.
MIMO Multiple Input Multiple Output Communications © Omar Ahmad
ElectroScience Lab IGARSS 2011 Vancouver Jul 26th, 2011 Chun-Sik Chae and Joel T. Johnson ElectroScience Laboratory Department of Electrical and Computer.
RMTD 404 Lecture 8. 2 Power Recall what you learned about statistical errors in Chapter 4: Type I Error: Finding a difference when there is no true difference.
Introduction to Statistical Quality Control, 4th Edition
Measuring the properties of QSO broad- line regions with the GMOS IFU. Randall Wayth with Matt O'Dowd & Rachel Webster.
GRAVITATIONAL LENSING
Chapter 10: Analyzing Experimental Data Inferential statistics are used to determine whether the independent variable had an effect on the dependent variance.
ECE 8443 – Pattern Recognition LECTURE 10: HETEROSCEDASTIC LINEAR DISCRIMINANT ANALYSIS AND INDEPENDENT COMPONENT ANALYSIS Objectives: Generalization of.
Stars Stellar radii –Stefan-Boltzman law Measuring star masses.
1 Nonparametric Statistical Techniques Chapter 17.
Images Similarity by Relative Dynamic Programming M. Sc. thesis by Ady Ecker Supervisor: prof. Shimon Ullman.
1 Artificial Intelligence: Vision Stages of analysis Low level vision Surfaces and distance Object Matching.
Nyquist barrier - not for all! Jaan Pelt Tartu Observatory Monday, 7. October 2013 Information and computer science forum.
Query Sensitive Embeddings Vassilis Athitsos, Marios Hadjieleftheriou, George Kollios, Stan Sclaroff.
Basic Time Series Analyzing variable star data for the amateur astronomer.
On the reliability of using the maximum explained variance as criterion for optimum segmentations Ralf Lindau & Victor Venema University of Bonn Germany.
Gravitational Lensing and Quasars “A man should look for what is, and not what he thinks should be.” – Albert Einstein Arvind Haran and David Johnston.
2D Fourier Transform.
ECE DIGITAL LOGIC LECTURE 15: COMBINATIONAL CIRCUITS Assistant Prof. Fareena Saqib Florida Institute of Technology Fall 2015, 10/20/2015.
CHAPTER- 3.2 ERROR ANALYSIS. 3.3 SPECIFIC ERROR FORMULAS  The expressions of Equations (3.13) and (3.14) were derived for the general relationship of.
Measuring shear using… Kaiser, Squires & Broadhurst (1995) Luppino & Kaiser (1997)
Fundamentals of Data Analysis Lecture 10 Correlation and regression.
The normal distribution
Inference for Least Squares Lines
CS 591 S1 – Computational Audio
Chapter 8 Experiments.
Break and Noise Variance
Chapter 9 Hypothesis Testing.
Adjustment of Temperature Trends In Landstations After Homogenization ATTILAH Uriah Heat Unavoidably Remaining Inaccuracies After Homogenization Heedfully.
The normal distribution
Overview Part 1 – Design Procedure Part 2 – Combinational Logic
Interpretation of Similar Gene Expression Reordering
CHAPTER – 1.2 UNCERTAINTIES IN MEASUREMENTS.
Lecturer Dr. Veronika Alhanaqtah
Geology 491 Spectral Analysis
CHAPTER – 1.2 UNCERTAINTIES IN MEASUREMENTS.
Presentation transcript:

25 YEARS AFTER THE DISCOVERY: SOME CURRENT TOPICS ON LENSED QSOs Santander (Spain), 15th-17th December 2004 Estimation of time delays from unresolved photometry Jaan Pelt Tartu Observatory Tartumaa 61602, Estonia

2 Long time monitoring of the gravitational lens systems often proceeds using telescopes and recording equipment with modest resolution. From high resolution images we know that the obtained quasar images are often blends and the corresponding time series are not pure shifted replicas of the source variability (let us forget about microlensing for a moment). It occurs, that using proper statistical methods, we can still unscramble blended light curves and compute correct time delays. We will show how to use dispersion spectra to compute two independent delays from A,B1 + B2 photometry and in the case of high quality photometry even more delays from truly complex systems. In this way we can significantly increase the number of gravitational lens systems with multiple images for which a full set of time delays can be estimated. Abstract

3 Relevant persons: Sjur, Rudy, Jan and Jan-Erik (author’s photo)

4 An I-filter direct image of PG showing QSO components A1, A2, B, and C circa 28 authors, ApJ 475:L85-L88 Unresolved photometry, a problem for many systems

5 Dispersion spectra Weighted sums of the two signals can be computed using similar expressions for pairs

6 Simple (oversimplified) algorithm to analyze unresolved photometry. 1.Assume that A signal is a pure source signal. ( A(t) = g(t) ). 2. Assume that B signal is a sum of two pure source signals, both of them shifted in time by certain, but different, amounts. B = B1+B2 = g(t-Delay)+g(t-Delay+Shift). 3. To seek proper values for the Delay and Shift we can build for certain trial value of Shift a matching curve M(t) from A(t). M(t) = A(t)+A(t+Shift). The value for the Delay can now be estimated (for this particular Shift) by using standard methods with M(t) and B(t) as input data. For every trial Shift we will have a separate dispersion spectrum and putting all together we will have a two- dimensional dispersion surface.

7 Two model curves computed from a single random walk sequence. Upper curve is a sum of shifted and original sequence.

8 Model data with Delay = 12 and Shift = -5 (or Delay = 17 and Shift = 5), Scatter = 0%

9 Model curve with only Delay = 12, Scatter = 0%

10 Sign for the delay can be easily seen. The significant minima occur only on the right side of the diagram. Delay = 12 and Shift = -5, Scatter = 0%, Dispersion is on logarithmic scale.

11 Which of the curves is a blend can be also detected by exchanging A and B curves. Now there are four strong minima on the left side of the dispersion surface. Delay = -17 and Shift = -5, Scatter = 0%.

12 Scatter = 2%Scatter = 5% Scatter = 10% Scatter = 20% The two dimensional spectra depend on noise level of the input curves. However, as seen from the plots, there is a quite large “working” region for the method.

13 Real data. Celebrated double quasar. PSF analysis by Ovaldsen from Schild data. B curve is shifted left by 417 days and down by 0.1 mag.

14 Double quasar data. Delay = 424 Shift = 32. Dispersion = B = B1+B2

15 Double quasar data. Delay = -415 Shift = 24. Dispersion = A = A1+A2

16 Simplifications. 1.We ignored different levels of noise for the true signals and for the combined signals. 2.We assumed that B1 and B2 (or A1 and A2) are of equal strength. 3. Only the case A, B1+B2 (or B, A1+A2) was considered, but what about A1+A2, B1+B2 case (if physically feasible)?

17 Conclusions 1.The methods based on dispersion spectra can be applied to unresolved photometry. 2.The amount of computations is raised significantly, especially if we analyze blends with unequal strength. In these cases the spectra will have already three dimensions. 3.To validate proposed methodology we need photometric data sets with good time coverage, low noise level and, if possible, well established geometry (from high resolution imaging). 4.The real data sets (in our case the celebrated double quasar) can show complex behavior which can not be yet easily interpreted. 5.Proposals are welcome:

18 If all this seemed to be too messy, then I can tell you, it was as messy as Mother Nature in Estonia!