MULTIPLE EXPOSURES IN LARGE SURVEYS
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1 MULTIPLE EXPOSURES IN LARGE SURVEYS / Johns Hopkins University
2 Big Data? Noisy Skewed Artifacts
3 Big Data? Noisy Skewed Artifacts
4 Serious Issues Significant fraction of catalogs is junk GALEX 50% PS1 3PI 50-80% PS1 MDS >95% Textbook methods often fail due to artifacts What are the good techniques?
5 Time Domain
6 Time Series of Faint Sources? Co-add images and do forced photometry Ideal if we have all observations but we never do Independent catalogs as we go Need to dig in the noise to build good timeseries Goal is an incremental strategy to weed out noise Otherwise catalogs are overwhelmed by junk
7 Detection Probability Measured flux is true + normal error Probability of detection
8 Detection Probability Measured flux is true + normal error Probability of detection As a function of the true flux Thresholds at 2-, 3-, 4- & 5 Sharper for 9-way stacks
9 Detection Probability Multiple exposures Binomial
10 Detection Probability Multiple exposures Binomial
11 What is a Real Source? Is it real or just noise? Bayesian hypothesis testing
12 What is a Real Source? Is it real or just noise? Bayesian hypothesis testing Out of k observations n detections of f i fluxes
13 Apparent Flux Distribution Galaxy number-counts as fn of magnitude Empirical relation approximately shows More and more fainter and fainter sources! But there is a limit, cf. Olbers paradox
14 Distribution of Noise Peaks Local maxima of continuous Gaussian random field Cf., P(k) by Barden, Bond, Kaiser, Szalay (BBKS; 1986) Now in 2D:
15 Something Like LSST Simulation Sky at 5 is 24 mag Object limit is at 28 Bayes factor Considering only fluxes
16 Adding Directions Bayes factor from cross-id As TB & Szalay (2008) Faint sources can be distinguished based on their celestial coordinates Always at same place!
17 Cross-Identification Hard problem Computationally, Scientifically & Statistically Need symmetric n-way solution Need reliable quality measure Same or not? Distance threshold? Maximum likelihood?
18 NOT SAME OR Same or Not? The Bayes factor H: all observations of the same object K: might be from separate objects
19 NOT SAME OR Same or Not? The Bayes factor H: all observations of the same object Same properties, e.g., coordinates, brightness K: might be from separate objects Properties could be different
20 Works in General Analytic results for Gaussian errors Incremental n-way strategy We can find moving stars With unknown velocities Matching events in time E.g., supernovae
21 SkyQuery the new generation! Dynamic federation of astronomy databases Query the collection as if they were one The 3 rd generation tool coming in December Cluster of machines running partitioned jobs Proper probabilistic exec with variable errors
22 SkyQuery the new generation! Dynamic federation of astronomy databases Query the collection as if they were one The 3 rd generation tool coming in December Cluster of machines running partitioned jobs Proper probabilistic exec with variable errors
23 Only the first steps Resolved shapes: radio morphology (Fan, TB+ 2014) Colors to augment matches (Marquez, TB, Sarro 2014)
24 Galaxy Light Linear Combination Principal Components Analysis (PCA) SDSS galaxy type On a big memory machine
25 Principal Component Analysis Principal directions Directions of largest variations Eigenproblem of covariances Singular Value Decomposition Problems Needs lots of memory Only need largest ones Very sensitive to outliers
26 Science is Interactive Too much to be accurate By the time you do the calculations, the answer may have changed
27 Streams of Data Mean
28 Streams of Data Mean Covariance Iterative evaluation!
29 Streaming PCA Initialization Eigensystem of a small, random subset Truncate at p largest eigenvalues Incremental updates Mean and the low-rank A matrix SVD of A yields new eigensystem Randomized algorithm!
30 Streaming PCA 3D Gaussian rotated into 50D Stretches: 7, 6, 5 Total Var = 110
31 With Outliers Adding 0.1% outliers = 100 in each bin Outliers take over the PCs Instability, no convergence
32 Robust Algorithm Outliers under control Marked on top Initialized with SVD On a set of 100 vectors
33 Summary Plan for the junk Proper statistics save money and gain speed Incremental randomized strategies scale Crossmatching, embeddings, ML, etc. Not there, yet Need new methods and tools
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