Local Volume, Milky Way, Stars, Planets, Solar System: L3 Requirements

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1 Local Volume, Milky Way, Stars, Planets, Solar System: L3 Requirements Anthony Brown Sterrewacht Leiden Sterrewacht Leiden /8

2 LSST data product levels Level 1 (nightly) data products will include images, difference images, catalogues of sources and objects detected in difference images, and catalogues of Solar System objects primary purpose is to enable rapid follow-up of time-domain events Level 2 (annual) data products will include well calibrated single-epoch images, deep co-adds, and catalogues of objects, sources, and forced sources enabling static sky and precision time-domain science Level 3 (user-created) data product services to enable science cases that greatly benefit from co-location of user processing and/or data within the LSST Archive L3 products can be converted to L2 (provided frequent and general use is foreseen) /8

3 LSST data product levels Level 3 capabilities provided/supported by LSST 1. Data Products and associated storage resources 2. Processing resources 3. Programming environment and framework /8

4 Example science cases for L3 data products 3D Galactic extinction mapping (S. Sale) Works on LSST photometric catalogue requires calibrated LSST pass-bands and stellar isochrones can incorporate parallax information Milky Way transients (J. Drew) concerns external (non-lsst) alerts use LSST archive as sky history automated retrieval of images around position of alert, light curves of surrounding sources, diagnostics to identify progenitor Latency requirements? How many requests expected? Image re-processing for diffuse sources (N. Walton) identify potential diffuse sources from standard L2 data products for example multiple points sources detected across large PN or galaxy re-process the raw images with algorithms optimized for diffuse sources apply also to known diffuse sources /8

5 Example science cases for L3 data products Combined analyses of LSST L1/L2 data and external data e.g., LSST-ized version of Gaia catalogue Retrieval of previous detections of solar system objects (M. Granvik) Use predicted positions of newly found SSOs (asteroids, NEOs) to retrieve imaging data and look for earlier detections Latency requirements? How many requests expected? Live-pixel server: was specific (α, δ) on active pixel at time t? check whether it worth querying for a cut-out image stand-alone version for de-biasing survey Crowded field photometry (G. Bono) re-process raw data with specialized crowded field algorithms (possibly including bespoke PSF estimation) process the various photometric bands together requires PSF model as function of epoch and position in field of view /8

6 Example science cases for L3 data products Bright star photometry (N. Walton, G. Bono) re-process bright star images with specialized algorithms PSF model required Variable star classification and characterization (L. Eyer) process L2 photometric time series treat all photometric bands simultaneously Cross-match tool Match LSST catalogue against external catalogue of choice Correctly account for epoch differences, proper motion, etc Allow user to provide match criteria in addition to positions /8

7 L3 requirements Requirements derived from above examples (Interface to) instrument model photometric pass-bands PSF model as function of time and field of view position was specific (α, δ) on active pixel at time t others (depending on L3 use case) Sky history retrieval at specified location refers to history as seen by LSST image cut-outs around specified position (can be from most recent co-adds, older co-adds, or epoch images) L2 data of sources around specified position (e.g. light curves) Manipulation of L1/L2 products to derive information needed to produce L3 Inter-operation with other catalogues and/or data archives support for development of cross-match facility /8

8 A few notes A cut-out server is planned for LSST most recent co-adds available instantly, older co-add or older epoch data could take minutes to retrieve from tape what are latency requirements on the image retrieval? how frequently can request for cut-out images be expected? Crowded fields are processed on best effort basis TBD what crowded means PSF model not optimal in crowded areas No variable star classification will be done by LSST photometric time series only treated per band LSST processing does the right thing for fast-moving (trailed) objects /8

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