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A Systematic Exploration of the Time Domain

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1 A Systematic Exploration of the Time Domain
S. G. Djorgovski With M. Graham, A. Mahabal, A. Drake, C. Donalek, M. Turmon, and many collaborators world-wide Hotwiring the Transient Universe III, Santa Fe, Nov. 2013

2 Data-Intensive Science in the 21st Century
The exponential growth of data volumes, complexity, and quality has several important consequences: The value shifts from the ownership of data to the ownership of expertise and creativity There is much more latent science in the data than can be done by any individual or a group (especially in real time) } Open Data Philosophy You can do a great science without expensive observational facilities “The computer is the new telescope” Data farming, data mining, and informatics are the key new scientific skills (because the human intelligence and bandwidth do not follow the Moore’s law) And remember - nobody ever over estimated the cost of software

3 From “Morphological Box” to the Observable Parameter Spaces
Zwicky’s concept: explore all possible combinations of the relevant parameters in a given problem; these correspond to the individual cells in a “Morphological Box” Fritz Zwicky Example: Zwicky’s discovery of the compact dwarfs

4 Expanding the Observable Parameter Space
Technology advances  Expanded domain of measurements  Discovery of new types of phenomena M. Harwit As we open up the time domain, we are bound to discover some new things!

5 Systematic Exploration of the Observable Parameter Space (OPS)
Every observation, surveys included, carves out a hypervolume in the OPS Its axes are defined by the observable quantities Technology opens new domains of the OPS New discoveries

6 Measurements Parameter Space
Physical Parameter Space Colors of stars and quasars Fundamental Plane of hot stellar systems SDSS E dSph GC Dimensionality ≤ the number of observed quantities Both are populated by objects or events

7 Measurements Parameter Space
Physical Parameter Space Color-magnitude diagram H-R diagram Theory + Other data Not filled uniformly: clustering indicates different families Clustering + dimensionality reduction _correlations High dimensionality poses analysis challenges

8 Parameter Spaces for the Time Domain
(in addition to everything else: flux, wavelength, etc.) For surveys: Total exposure per pointing Number of exposures per pointing How to characterize the cadence? AWindow function(s) AInevitable biases For objects/events ~ light curves: Significance of periodicity, periods Descriptors of the power spectrum (e.g., power law) Amplitudes and their statistical descriptors … etc. − over 70 parameters defined so far, but which ones are the minimum / optimal set?

9 Characterizing Synoptic Sky Surveys
Define a measure of depth (roughly ~ S/N of indiv. exposures): D = [ A  texp   ]1/2 / FWHM where A = the effective collecting area of the telescope in m2 texp = typical exposure length = the overall throughput efficiency of the telescope+instrument FWHM = seeing Define the Scientific Discovery Potential for a survey: SDP = D  tot  Nb  Navg where tot = total survey area covered Nb = number of bandpasses or spec. resolution elements Navg = average number of exposures per pointing Transient Discovery Rate: TDR = D  R  Ne where R = d/dt = area coverage rate Ne = number of passes per night

10 Towards the Automated Event Classification
(because human time/attention does not scale) Data are heterogeneous and sparse: incorporation of the contextual information (archival, and from the data themselves) is essential Automated prioritization of follow-up observations, given the available resources and their cost A dynamical, iterative system A very hard problem!

11 Contextual Information is Essential
Visual context contains valuable information about the reality and classification of transients So does the temporal context, from the archival light curves And the multi-λ context Initial detection data contain little information about the transient: α, δ, m, Δm, (tc). Almost all of the initial information is archival or contextual; follow-up data trickle in slowly, if at all Artifact SN CV not SN Visible Radio Gamma

12 Harvesting the Human Pattern Recognition (and Domain Expertise)
Human-annotated images (via  Semantic descriptors  Machine processing  Evolving novel algorithms … and iterate Challenges: Optimizing for different levels of user expertise; optimal input averaging; encoding contextual information; etc. (Lead: M. Graham)

13 A Hierarchical Approach to Classification
Different types of classifiers perform better for some event classes than for the others We use some astrophysically motivated major features to separate different groups of classes Proceeding down the classification hierarchy every node uses those classifiers that work best for that particular task

14 From Light Curves to Feature Vectors
We compute ~ 70 parameters and statistical measures for each light curve: amplitudes, moments, periodicity, etc. This turns heterogeneous light curves into homogeneous feature vectors in the parameter space Apply a variety of automated classification methods

15 Optimizing Feature Selection
RR Lyrae Eclipsing binary (W U Ma) Rank features in the order of classification quality for a given classification problem, e.g., RR Lyrae vs. WUMa In k-fold cross-validation, the original sample is randomly partitioned into k subsamples. Of the k subsamples, a single subsample is retained as the validation data for testing the model, and the remaining k − 1 subsamples are used as training data. The cross-validation process is then repeated k times (the folds), with each of the k subsamples used exactly once as the validation data. The k results from the folds then can be averaged (or otherwise combined) to produce a single estimation. The advantage of this method over repeated random sub-sampling is that all observations are used for both training and validation, and each observation is used for validation exactly once. (Lead: C. Donalek)

16 Metaclassification: An optimal combining of classifiers
Exploring a variety of techniques for an optimal classification fusion: Markov Logic Networks, Diffusion Maps, Multi-Arm Bandit, Sleeping Expert…

17 The Follow-Up Crisis Follow-up observations are essential, especially spectroscopy. We are already limited by the available resources. This is a key bottleneck now, and it will get much worse “Exciting” transients are no longer rare – the era of ToO observations may be ending, we need dedicated follow-up facilities … and most of the existing spectrographs are not suitable for this A hierarchical elimination of less interesting objects: iterative classification, photometric observations with smaller telescopes Coordinated coverage by multi-wavelength surveys would produce a first order, mutual “follow-up” We will always follow the brightest transients first (caveat LSST) Coordinated observations by surveys with different cadences can probe more of the observable parameter space

18 Real-Time vs. Non-Time-Critical
Transients may be overemphasized; there is a lot of good science in the archival studies, and that can only get better in time

19 It Is NOT All About the LSST! (or LIGO, or SKA…)
NOW is the golden era of time-domain astronomy

20 Conclusions Time domain astronomy is here now (CRTS, PTF, PS1, SkyMapper, ASCAP, Kepler, Fermi, …), and it is a vibrant new frontier Lots of exciting and diverse science already under way, from the Solar system to cosmology – something for everyone! CRTS data stream is open – use it! (and free ≠ bad) It is astronomy of telescope and computational systems, requiring a strong cyber-infrastructure (VO, astroinformatics) Automated classification is a core problem; it is critical for a proper scientific exploitation of synoptic sky surveys Data mining of Petascale data streams both in real time and archival modes is important well beyond astronomy Surveys today are science and methodology precursors and testbeds for the LSST, and they are delivering science now CRTS II consortium now forming – join us!

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