LSST Solar System Science: MOPS Status, the Science, and Your Questions

1LSST SOLAR SYSTEM SCIENCE DM PLANS | SOLAR SYSTEM SCIENCE COLLABORATION | AUGUST 9TH, 2017.Name of Meeting • Location • Date - Change in Slide Master
LSST Solar System Science:
MOPS Status, the Science, and Your Questions
Mario Juric,
University of Washington
LSST Data Management Subsystem Scientist
for the LSST Data Management Team.
LSST SOLAR SYSTEM SCIENCE DM PLANS
August 9th, 2017
These slides: http://ls.st/aak

2LSST SOLAR SYSTEM SCIENCE DM PLANS | SOLAR SYSTEM SCIENCE COLLABORATION | AUGUST 9TH, 2017.
The Real Heros Here
The LSST Solar System Products & Software Team
Lynne Jones (UW; LSST Performance Scientist)
Joachim Moeyens (UW; Research Associate)
Colin Slater (UW; DM System Science)
Eric Bellm (UW; Level 1 Lead)
+ many, many, others from the overall LSST DM effort

3DPS 47• Washington, D.C. • November 12th, 2015
LSST: A Deep, Wide, Fast, Optical Sky Survey
8.4m telescope 18000+ deg2 10mas astrom. r<24.5 (<27.5@10yr)
ugrizy 0.5-1% photometry
3.2Gpix camera 30sec exp/4sec rd 15TB/night 37 B objects
Imaging the visible sky, once every 3 days, for 10 years (825 revisits)

LSST Site (April 14th, 2015)

The Summit Today
August 3rd 2017

LSST Observatory (cca. late ~2018)
We are a year away from the observatory building being close to complete!

Overview
− The status and plans for MOPS
− Science expectations
− Addressing your questions

Tracklets
Tracks
LSST’s Asteroid Discovery Method: “2+2+2”
Requirement for reportable discovery: at least three pairs taken over three nights in
a short (e.g., ~two week) period, fitting a Keplerian orbit (heliocentric).
Initial and Differential Orbit Determination.
Publication and Reporting to MPC.
Terminology:
• tracklets: potential linkages in the same night (linear extrapolation)
• tracks: potential linkages over three nights (quadratic fit)
• reportable discovery: a track that unambiguously fits a Keplerian orbit within
the astrometric uncertainties
• MOPS: the software system that links detections into reportable discoveries
Note: some
asteroids will also
be immediately
recognizable due to
measurable trailing.
This is the well known MOPS algorithm; e.g., Kubica (2007), Denneau (2006)

LSST Observing Strategy

Key Challenge: Night-to-night Linking
• If the density of detections is high
(irrespective of whether they’re real or
not), there are multiple plausible
linkages for each pair (left).
• Those linkages cascade into generating
multiple plausible tracks when linked
from night to night.
• Those tracks must be tested using orbit
fits (“orbit determination”; OD) to reject
false linkages, which is computationally
expensive (but tractable; more later).
• If the density of detections is
exceptionally high, OD may not be able
to reject them all. More observations
would be needed to confirm the
identification.
• A major issue on PanSTARRS PS1 (up
to 50:1 FP rates). Can one do better?
Figure 1. from Denneau et al. (2006), ADASS XVI
Conceptual diagram of intra-night linking. Open and
solid circles are detections in night #1 and #2,
respectively. The arrows indicate plausible true and
false tracklet links (black and blue, respectively).

False-positive Rates
− Reduction of false-positive rates in image differencing has been a hot topic
of research over the past few years, especially in the transient community.
− Improvements to hardware
• CCDs with significantly fewer artifacts (e.g. DECam; see next slide)
• Optical systems designed to minimize ghosting and internal reflections
− Improvements to the software
• Improved theoretical understanding of the algorithms (e.g., Zackay, Ofek &
Gal-Yam 2017)
• Improved image differencing pipelines (e.g., PTFIDE for the Palomar Transient
Factory and the Zwicky Transient Facility, LSST Software Stack for the LSST)
• Machine learning classifiers for filtering of false positives (see next slide)
− These advances have reduced false positive rates to manageable levels

State of the Art Today: Dark Energy Survey/Camera
− The Dark Energy Survey (DES) is an optical/near-infrared survey that aims
to probe the dynamics of the expansion of the universe and the growth of
large scale structure. Is imaging 5,000 deg2 of the southern sky.
− Developed improved image differencing pipelines
− Developed machine learning (ML) classifiers to recognize and remove
false detections.

Key DES result: false positive detections can be
effectively minimized, identified, and removed.
Key conclusions:
• Raw false detection rates of 13:1
• Filtered rates of 1:3
• Already within the acceptable range for
LSST
Goldstein al. (2015), AJ, 150, 82

Instrumental improvements clearly visible
Figure 1, Goldstein al. (2015), AJ, 150, 82
Note the marked qualitative differences in the morphology and nature of false
detections compared to PanSTARRS due to improvements to CCDs, readout
electronics, and the optical system.

LSST Study of NEO-discovery Efficiency
A paper produced in response
to NASA inquiries about LSST’s
capabilities as an NEO
discovery machine.
Though focused on NEOs, its
conclusions about MOPS
efficiency are valid across most
SSO populations.
Soon to be resubmitted to
Icarus (addressing reviewer’s
comments).

Status of LSST’s Pipeline
− We tested the performance of LSST pipelines
• LSST image differencing pipeline tested using DECam
• MOPS prototypes tested on simulations informed by the differencing tests
− Image differencing:
• Using conservative (worst case) assumptions, we find ~450 detections/deg2 at 5σ for LSST
- This assumes no improvements to existing pipeline performance
- This includes the expected ~60 deg-2 irreducible background
- Also includes real astrophysical sources (and is likely dominated by them, judging by the SNR
distribution)
- No machine learning afterburners applied
• We find the density of correlated false positives is negligible (~2 deg-2)
• These numbers are
− MOPS:
• Tested with the LSST prototype at 500 deg-2 (Jones et al)
• Tested with the Pan-STARRS prototype at 300 deg-2 (Veres & Chesley)

Independent study by Veres & Chesley
http://iopscience.iop.org/article/10.3847/1538-3881/aa73d0/pdf

Status of MOPS
− Over the past year, we’ve largely been focused on the image differencing
problem (reducing the false positive rates). We’re now confident we have
that under control, and that the MOPS algorithm will work.
• Note: we still wish to validate that, end-to-end (ZTF)
− Going forward, we’re focusing on turning the MOPS prototype into
production-ready code.
• Fix design issues, improve the algorithm, add missing pieces, validate
− The nominal plan is below, but will be significantly refreshed (with a lot
more details added) over the coming months.

MOPS Development Scenario (!! DRAFT !!)
− 2017:
• Develop an automated end-to-end
pipeline using the existing MOPS
prototype (findTracklets, linkTracklets)
• Develop MOPS test suite (test data,
performance metrics)
• MOPS dev. plan update
− 2018:
• Validate MOPS by running on ZTF alert
stream
• Incorporate IOD, diffOD (JPL code)
• Develop agreements with the MPC
• Finalize Solar System data product
definitions and operational protocols
− 2019:
• Rewrite the linkTracklets stage
• Incorporate attribution
• Add orbit merging
• Rewrite findTracklets
• Computation of derived quantities
• Integrate into the Level 1 processing
system
− 2020+:
• Validate on ComCam data
• Add precovery
• (use trail information in orbit fits)
• Integration with the MPC
• Iterative improvements
Core team:
Joachim Moeyens, Chris Morrison (2019), Mario Juric, Lynne Jones
+ the LSST DM Team at UW
Note: two B612 Asteroid Institute Fellows with Solar System and
software experience arrive to UW this fall:
Bryce Bolin, Sarah Greenstreet

Overview

LSST Data Products: Level 1, 2, and 3
− 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.
− Reduced single-epoch, deep co-added images.
− User-produced added-value data products (deep KBO/NEO catalogs,
variable star classifications, shear maps, …)
Level3Level1Level2
For more details, see the “Data Products Definition Document”, http://ls.st/lse-163

Key LSST Deliverables for Solar System Science
− Within 60 seconds of each observation: A real-time stream of
observation reports (alerts) with information about astrometry,
photometry, and shape including trailing, direction of motion.
− Every day: A stream of linked tracks reported to the Minor Planet
Center.
− Every day: A catalog of (heliocentric) orbits for LSST-discovered
objects.
− Annually: Precisely calibrated photometric catalog (ugrizy bands)
accurate to 5mmag (systematics limited), with every data release.
Details: The Data Products Definition Document, http://ls.st/dpdd

Solar System Science Goals
Table 1 from the (draft) Solar System Science Requirements note; http://ls.st/xcv

Expected Completeness
Table 3.2 from the LSST Observing Strategy Whitepaper; http://ls.st/3y1

Expected Solar System Object Yields
Lynne Jones, priv comm (results of analysis of minion_1016 OpSim run)

Solar System Data Products
− The LSST Solar System Objects Catalog concept has been defined in the
Data Products Definition document (http://ls.st/lse-163).
− Example:

These definitions need to be reviewed
and updated if necessary, adding more
detail where possible.
I would like to begin this work
(conversation!) next week at LSST 2017.
By the end of 2018, we aim to
have an updated definition of
the Solar System Data products.
In the same time-frame, we’d
want to nail down the
operational details (e.g.,
connection to MPC, attribution
of known object observations,
etc.)

Overview

Cadence
− I am interested in nearby moving objects detectable only during dawn/twilight
hours. Will there be data on these from LSST?
• Cadences including twilight are scheduled to be simulated by the end of the year
(assuming OpSim V4 is accepted on schedule)
− How can the discovery of outer solar system objects affected by cadence
decisions and how is their scientific importance included in the calculus for
determining the final cadence?
• The community proposes survey strategies and metrics by which the science returns can
be quantified
• The Project executes simulations attempting to optimize for those metrics
• The Science Advisory Committee makes the final recommendation of which strategy to
select. The criteria by which the SAC will weight various science cases have not yet been
defined.
− More information:
• Cadence Optimization Presentation: http://ls.st/ot2
• Observing Strategy Whitepaper: http://ls.st/smg

Catalog
− What will be in the catalog? Can we still change what’s going to be measured for SSOs?
• The DPDD defines the outline of what the catalog will contain, but adding more detail will be helpful. We’d like to start
that at LSST 2017.
• We can make changes as long as they don’t affect budget and schedule.
− How do we draw the line on what the project delivers, and what the community delivers?
• The guiding principle is that the Project tries to deliver universally useful products whose generation is non-
controversial (e.g., orbit catalog).
− How can the community contribute code (or should we work separately)?
• We’d definitely prefer collaboration!
• All LSST DM code is on github, but the MOPS components are not easy to install or run, at the moment. This should
get better in ~6 months or so.
− What is currently planned for the SSSC to get from MOPS before sunset each night? What
will we get in 24 hours? What do we get in 3 months? What do we get in the Data Release
database version?
• See next slide
− Will we distribute links to known objects earlier in the night?
• Yes (see next slide)
− Will we link to MPC designations / submit the data to the MPC?
• Yes, though the details need to be worked out.

Key LSST Deliverables for Solar System Science
− Within 60 seconds of each observation: A real-time stream of
observation reports (alerts) with information about astrometry,
photometry, and shape including trailing, direction of motion.
− Every day: A stream of linked tracks reported to the Minor Planet
Center.
− Every day: A catalog of (heliocentric) orbits for LSST-discovered
objects.
− Annually: Precisely calibrated photometric catalog (ugrizy bands)
accurate to 5mmag (systematics limited), with every data release.
Details: The Data Products Definition Document, http://ls.st/dpdd

Algorithms
− What will you do with trailed sources & non-linear motion (nearby/geocentric)
• Trailed sources will be detected and characterized, but we will not attempt to fit
anything other than heliocentric fits.
• N.b. trailed sources should be easy to pick out of the alert stream, in real-time
− MOPS Specific to population (Inner/Outer/NEO)?
• Not unless we have to to meet requirements
− Will the MOPS design document be updated (LDM-156)?
• Yes, as we revisit the design in (2018)
− How does MOPS centroid on comet comae and does it avoid astrometry biases
due to coma and tail asymmetries?
• Our centroiding algorithm derives from the SDSS one, that’s been designed to
appropriately centroid galaxies that may also exhibit asymmetries.
• I don’t have the information on algorithm details at the moment; will look that up.
− Will we do forced photometry of SSOs?
• No

Management
− Plans and timelines for testing and commissioning MOPS.
• See one of the previous slides
• The most important early test will be to run it on ZTF next year
- Note: ZTF expects to transmit 1 million alerts/night (10% of LSST)!
− How should the SSSC contribute to commissioning of MOPS (or
commissioning plans)? What aspects of MOPS development do you think
will need input from the SSSC?
• Not much to do in the near-term; we have to clean up our code first.
• Starting in 2018: developing test datasets, adding to the MOPS validations
suite, perhaps running MOPS with your data
• Provide feedback, explore, demonstrate & contribute alternative algorithms
− What is the level of commitment by the project to deliver Solar System
object science?
• Solar System science continues to be one of the four key areas we’re required
to deliver on in our top-level Science Requirements Document.

Communications
− I’d like us to form stronger Project<->SSSC connections.
− Will want feedback on the definition of the Solar System data
products, operational ideas, etc.
− Will want to stay up-to-date with what the collaboration is
planning
− Maybe have a quarterly status meeting like this?
• Also in-person gatherings at LSST summer meetings
• We’re also always at the DPS meeting

Communications: http://community.lsst.org
− http://community.lsst.org

Summary
− So far, we were focused on improving image differencing performance.
We’re now reasonably confident it will work.
− Restarting MOPS development this fall
− Our goal is to validate an early MOPS prototype by running it on the ZTF
alert stream (by the end of 2018).
− Updating MOPS development roadmap, the definition of the Solar System
Data Products, and operational assumptions in 2018. We’ll need your
feedback on all of these.
− Strong desire to increase Project <-> SSSC communication. I (Mario Juric)
will serve as the liaison.

LSST Solar System Science: MOPS Status, the Science, and Your Questions

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LSST Solar System Science: MOPS Status, the Science, and Your Questions