1) The document discusses plans for an upcoming LSST kick-off meeting on February 7-8, 2019. 2) It provides technical specifications for the LSST telescope including an 8.4m primary mirror, 3.2 billion pixel camera, and plans to collect 20TB of data per day. 3) It outlines the challenges of storing, processing and providing access to the enormous amount of data to be collected by LSST, including 500PB of raw data and catalogs containing tens of billions of objects.

1. Kick-Off Meeting
February 7-8, 2019
LSST
Dominique Boutigny

2. 2Dominique Boutigny
The LSST project
Modified Paul-Baker optical formula
D = 8.4 m (6.7m effective)
f/d = 1.23
350 t mobile structure
Étendue = surface X field of view
→ LSST: 319 m².deg²
3.2 Gpixels – 0.2” / pixel
189 CCD (4k x 4k) deep depleted
3.2 Gpixels – 0.2” / pixel
189 CCD (4k x 4k) deep depleted

3. 3Dominique Boutigny
Summit Site at Cerro Pachón
© Wil O’Mullane Octobre 2018
Février 2017

4. 4Dominique Boutigny
Asturfeito, S.A., Spain

5. 5Dominique Boutigny
The size of the LSST challenge
●
2.75 10 visits (x2 snaps x189 CCD x16 Mpixels)⁶
●
20 TB / 24h including calibrations
●
60 PB raw data / year
●
Final dataset : 500 PB total
●
Peak computing power: 1.8 PFlops
50% of the processing done in France:
●
Full copy of the data in France
●
Catalogs
●
Raw images
●
+ some science derived products

6. 6
LSST Data Products
− A stream of ~10 million time-domain events per night, detected and
transmitted to event distribution networks within 60 seconds of
observation.
− A catalog of orbits for ~6 million bodies in the Solar System.
− A catalog of ~37 billion objects (20B galaxies, 17B stars),
~7 trillion
observations (“sources”), and ~30 trillion measurements (“forced
sources”), produced annually, accessible through online databases.
− Deep co-added images.
− Services and computing resources at the Data Access Centers to
enable user-specified custom processing and analysis.
− Software and APIs enabling development of analysis codes.
Level3Level3Level1Level1Level2Level2
Slide borrowed and adapted from Mario Jurić
Promptdata
products
DataRelease
products
User
Generated
dataproducts

7. 8
The database : a key component
The challenge is to design an SQL database system
able to store trillions of objects while keeping a the
access time at a reasonable value
Qserv : developed at SLAC – Design optimized for
astronomical queries
Massively parallel – distributed –
shared nothing – relational database
Massively parallel – distributed –
shared nothing – relational database
Final relelease :
~15 PB
Final relelease :
~15 PB

8. 9Dominique Boutigny
Qserv@CC-IN2P3
CC-IN2P3 has a partnership with Dell
Setup Qserv test bench
●
50 servers (2 test benches x 25 servers)
●
400 cores
●
0.5 PB disk storage

9. 11Dominique Boutigny
Data Access Centers (DACs)
In LSST: officially 2 DACs in Chile and at NCSA
●
Sized to provide data access to ~7500 potential users
●
Currently evaluating the possibility to have a few (3-4) International DAC
●
The European LSST community is getting organized
In France there will be an LSST DAC
●
At minimum to serve the French LSST community
●
May be extended to the European / Worldwide partners

10. Firefly ServersFirefly Servers
Jupyter Servers
(managed by
JupyterHub)
Jupyter Servers
(managed by
JupyterHub)
DAXDAX
Web
Portal
Web
Portal
Jupyter
Client
Jupyter
Client
External
Users of
LSST APIs
External
Users of
LSST APIs
Science Platform
Firefly
Widgets
Firefly
Widgets
BrowserBrowser
BrowserBrowser
https
User StorageUser Storage
iPython
Kernel
iPython
Kernel
Firefly Python
Server
Firefly Python
Server
Parallel/Batch
Computing
Parallel/Batch
Computing
User
Computing
Image
Storage
Image
Storage
L3L3
DB
(QServ,
Other)
DB
(QServ,
Other)
L3L3
Identity ProviderIdentity Provider
file system
WebDAV
VOSpace
sftp/WebDAV/
VOSpace
12
© G. Dubois-Felsmann
IPAC

11. 13Dominique Boutigny
Dark Energy Science Collaboration
Data Challenge
DESC will produce
thousands of such images
+ the corresponding
catalogs
Nice datasets to test catalog
production pipelines and
science interface

12. 14Dominique Boutigny
Plans
●
Ingest DESC DC2 data into Qserv
●
Test usability on realistic science use cases
●
Deploy Data Access Center / Science Platform components
●
Test usability on realistic science use cases
●
Investigate how to connect the Science platform with HTC and (remote) HPC
resources
●
Investigate how to interconnect / inter-operate LSST catalogs with other
datasets