Caltech 20090903 Talk on T.C.P. for LSST/PTF workshop
•
1 gefällt mir•269 views
Caltech 20090903 Talk on T.C.P. for LSST/PTF workshop
Empfohlen
Weitere ähnliche Inhalte
Was ist angesagt?
Was ist angesagt? (6)
Andere mochten auch
Ähnlich wie Caltech 20090903 Talk on T.C.P. for LSST/PTF workshop
Ähnlich wie Caltech 20090903 Talk on T.C.P. for LSST/PTF workshop (13)
Kürzlich hochgeladen
Kürzlich hochgeladen (20)
Caltech 20090903 Talk on T.C.P. for LSST/PTF workshop
- 1. Interesting near galaxy sources • identified by TCP in the last 2 days • (last epoch observed 1 week ago) • Classification triggered by latest epoch added to the source
- 3. Overview • TCP Software & Data Architecture • Classifiers & Cutting out “Junk” • Continuing work... PTF spectroscopically confirmed SN, subsequently classified by TCP as SN
- 5. Parallelized source correlation and classification • Difference objects are retrieved from LBL • Each difference-object is passed to an IPython client • Each parallel IPython client performs: • Source creation or correlation with existing sources • “Feature” generation (or re-generation) for that source source • Classification of that source generation feature generation source classification
- 6. Parallelized source correlation and classification • Realtime TCP runs on 22 dedicated cores • LCOGT’s 96 core beowulf • non run-time tasks • Classifier generation • Additional resources • To be used for future timeseries classification work source generation • Yahoo’s 4000 core Hadoop academic cluster • Amazon EC2 cluster feature generation source classification
- 7. Warehouse of light-curves • Need representative light-curves for all science • With these we can model each science class • We’ve built a warehouse of example light-curves TCP-TUTOR DotAstro.org internal interface public interface
- 11. Confusion Matrix different ways of quantifying effeciencies - using original good training set, and train/evaluate efficencies via folding - using “noisified”, simulated sources matching sur vey shedule, cadences, limits • C
- 12. “Noisification” (resampling light-curves) • For PTF, the Noisification code references: • 1000s of PTF pointing and survey observing plans • This allows simulation of PTF cadenced light-curves • Occasionally PTF observes using a faster cadence: • 7.5 minutes between revisiting an RA, Dec • This requires a separate set of noisified light-curves and classifiers. • Other pointing and observing plans could be used. • This means we can easily generate noisified light-curves for any survey. • Thus we can generate science classifiers for any survey.
- 14. Constructing Light Curves from subtractions ain’t easy true mag reference [assumes template doesn’t update] time
- 15. Constructing Light Curves from subtractions ain’t easy true mag reference [assumes template doesn’t update] = 3 σ limiting mag time
- 16. Constructing Light Curves from subtractions ain’t easy true mag reference [assumes template doesn’t update] = 3 σ limiting mag detected in: pos_sub? neg_sub? time
- 17. Constructing Light Curves from subtractions ain’t easy 5σ exclusion band true mag reference [assumes template doesn’t update] = 3 σ limiting mag detected in: pos_sub? neg_sub? time
- 18. Constructing Light Curves from subtractions ain’t easy 5σ exclusion band true mag reference [assumes template doesn’t update] = 3 σ limiting mag detected in: pos_sub? neg_sub? time
- 19. for some source at Constructing Light Curves RA,DEC & ti, determine from subtractions ain’t easy best ref_mag at t=ti total mag = TM+ yes [detection] detection in positive sub? total mag = limit_mag no [upper limit] no limit_mag fainter than ref_mag? total mag = ref_mag [detection] yes no detection in the total mag = TM- negative sub? [detection] s yes ye mag in negative sub < total mag = limit_mag limit_mag - ref_mag? no [upper limit] TM+ = 2.5 log10( f_aper × 10-0.4(sub_zp-ref_zp) + flux_aper ) + ub1_ref_zp TM- = 2.5 log10( -f_aper × 10-0.4(sub_zp-ref_zp) + flux_aper ) + ub1_ref_zp
- 20. Classifiers • General Classifier • Filter out: poorly subtracted sources • Filter out: minor planets / rocks • Filter out: long-time sampled (periodic & nonperiodic) • Identify interesting sources near known galaxies • Identify periodic variable science class when confidence is high • Timeseries Classifier • Weighted combination of machine learning classifiers • Astronomer crafted classifiers for specific science types • Microlens, Super Nova
- 21. (Source) General Classification • Three general classification groups. • Periodic variables are contained within the “uninteresting” group, although more specific Interesting with sub-classifications are known. nearby galaxy context Poor subtraction JUNK class SN, AGN of Uninteresting various quality classes Rock class (general) Periodic variable class Interesting without context information Nicely subtracted, non-galaxy, non-periodic variable classes
- 22. (Source) General Classification • Applied to ~80 spectroscopically confirmed user classified (SN, AGN, galaxy) sources. • SN lightcurve classifier is needed when galaxy Interesting with context is not available, and to improve confidence nearby galaxy context in SN classification. SN, AGN, galaxy Uninteresting (58 SN) faint, poorly subtracted (11 SN) Interesting without context information
- 23. General Classifier: components & cuts • Crowd source modeled “RealBogus” metric • Cut on: average RealBogus, derivatives of RB components • Cut on: % epochs in source with good RealBogus • PSF statistics • Cuts on: PSF symmetry, eccentricity (averages) • Neighboring object comparisons • Cuts on significance of above metrics when compared to neighboring pixels • Minor Planet check PyEphem • Does an epoch intersect a Minor Planet? (PyMPChecker) PyMPChecker • Well sampled source • Cuts on: well sampled periodic & nonperiodic sources
- 24. Evaluating and Combining Classifiers The “Netflix Prize” was won using a combination of ~1000 different classifiers. • Issues when using multiple classifiers: • How to combine Classifiers using weights or tree-hierarchy • How to generate final classification “probabilities” when using: • Widely varying types of classifiers • Each classifier may contain sub-classifications with their own class probabilities. • Evaluate the final combination of classifiers • We classify PTF09xxx user classified sources • We display success / failure cases for each general class • Update classifier weights & cuts, try again. • OR: Iteratively & algorithmically find best weights.
- 25. Periodic variable classifiers • Currently, science classes are determined by combining the weighted probabilities generated by different classification models, for a source. ~0.4 day period ~0.14 day period RR Lyrae using • Each machine-learned classification model is trained using RR Lyrae using 10 epoch 20 epoch “noisified” lightcurves which were generated using different parameters. noisification noisification ...shows highest classification Clicking on a class for one probability sources for that of dozens of ML models... model::class Overplotting of period-fold plotting period-folded model probably failed here still needs work 0.1 - 0.17 day period RR Lyrae using 15 epoch noisification
- 26. Continuing Work • Test, improve general classifier cuts • Push general classifications to Followup Marshal • Push specific variable science class identified sources to Followup Marshal • Explore other timeseries classifiers for periodic variable classification.
- 28. TCP Explorer