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Exploring Observable Parameter Spaces in Astroinformatics

This project aims to bridge the gap in understanding vast data from sky surveys using data mining techniques and specialized tools in a collaborative effort. Key goals include feasibility study, exploration of new object types, and real-time analysis. Various experts are involved, focusing on time-domain data and utilizing Bayesian networks and boosting methods. The project aims to enhance data visualization, storage, and scalability in the field of astroinformatics. Key activities include pattern search, clustering, and outlier detection for improved data interpretation.

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Exploring Observable Parameter Spaces in Astroinformatics

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  1. Data 2 Knowledge study project Ashish Mahabal (aam@astro.caltech.edu) Ciro Donalek Matthew Graham Ray Plante George Djorgovski VAO-LSST Meeting, NOAO, 24 March 2011

  2. Goals • Feasibility study • What is out there • What is needed • Milestones • What can be done

  3. Exploration of observable parameter spaces and searches for rare or new types of objects Djorgovski

  4. Overview – many connections Astroinformatics (next meeting in Sep. 2011) VOStat and other R/Statistics tools Data challenges Various sky surveys Related issues Semantics Classification/characterization Distributed data GPUs Focus on time domain

  5. Focus on time-domain Expertise, and it encompasses all aspects of data mining (save one) Plus, real-time forces us to be fast. Portfolio building – growing columns of tables Bayesian networks utilizing auxiliary information Lightcurve techniques for characterizing objects

  6. Missing stat and CS tools

  7. Missing stat and CS tools Bootstrap aggregating Mixture of experts Boosting Simulated annealing Semi-supervised learning …. From IVOA KDD User guide for Data Mining (Nick Ball)

  8. Science goal: to solve the growing gap between the huge generation of data and our understandingof it • Data Gathering (e.g., new generation instruments …) • Data Farming: • Storage/Archiving • Indexing, Searchability • Data Fusion, Interoperability, ontologies, etc. • Data Mining (or Knowledge Discovery in Databases): • Pattern or correlation search • Clustering analysis, automated classification • Outlier / anomaly searches • Hyperdimensional visualization • Data visualization and understanding • Computer aided understanding • KDD • Etc. • New Knowledge Data storage , Pbytes Data access >103 access Scalability: Petaflops, Exaflops Computing power (multicore) Algorithm: parallelism Visualization: N-dimensional

  9. Currently on the plate • DAME • Knime (Konstanz Information Miner) • Orange (Visual/python) • Weka (ML/Java) • Rapidminer (standalone)

  10. Comparison matrix for DM/Viz tools Accuracy Scalability Interpretability Usability Robustness Versatility Speed Popularity

  11. Related activities Skyalert integration (Graham) – adding data and methods Solicitation of examples from community WD, Blazars’ example Making R more astronomy friendly Various datasets Differing number of rows, columns For supervised/unsupervised classification TA on GPUs – incorporate in pipeline

  12. Slide from Budavari CUDA zone, PyCUDA, …

  13. VAO People working on this • Ashish Mahabal, Ciro Donalek, Matthew Graham, George Djorgovski (Caltech) • Ray Plante (NCSA) • But we are in touch with many others in astro/CS/stats and relying on many groups including LSST transients and informatics working groups

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