GaiaCal2014: Creating and Calibrating LSST Data Product
1. 1GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
Name
of
Mee)ng
•
Loca)on
•
Date
-‐
Change
in
Slide
Master
Crea%ng
and
Calibra%ng
the
Large
Synop%c
Survey
Telescope’s
Data
Products
Mario
Juric
LSST
Data
Management
Project
Scien5st
GAIACAL2014
July 9th, 2014
Robyn
Allsman,
Yusra
AlSayyad,
Tim
Axelrod,
Jacek
Becla,
Andrew
Becker,
Steve
Bickerton,
Jim
Bosch,
Bill
Chickering,
Andy
Connolly,
Greg
Daues,
Gregory
Dubois-‐
Fellsman,
Mike
Freemon,
Andy
Hanushevsky,
Fabrice
Jammes,
Lynne
Jones,
Jeff
Kantor,
Kian-‐Tat
Lim,
Dus5n
Lang,
Ron
Lambert,
Robert
Lupton
(the
Good),
Simon
Krughoff,
Serge
Monkewitz,
Jon
Myers,
Russell
Owen,
Steve
Pietrowicz,
Ray
Plante,
Paul
Price,
Andrei
Salnikov,
Dick
Shaw,
Schuyler
Van
Dyk,
Daniel
Wang
featuring
Chris
Stubbs
and
the
LSST
Project
Team
2. 2GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
Overview
− LSST
overview
and
status
− LSST
data
products:
what
will
be
measured
and
how
− Instrumental
and
Astrophysical
Calibra)on
− Dethroning
“the
catalog”
− Does
soccer
need
a
mercy
rule?
3. 3
GaiaCal
2014
•
Ringberg,
Germany
•
July
9,
2014
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)
4. 4GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
A
Dedicated
Survey
Telescope
− A
wide
(half
the
sky),
deep
(24.5/27.5
mag),
fast
(image
the
sky
once
every
3
days)
survey
telescope.
Beginning
in
2022,
it
will
repeatedly
image
the
sky
for
10
years.
− The
LSST
is
an
integrated
survey
system.
The
Observatory,
Telescope,
Camera
and
Data
Management
system
are
all
built
to
support
the
LSST
survey.
There’s
no
PI
mode,
proposals,
or
)me.
− The
ul%mate
deliverable
of
LSST
is
not
the
telescope,
nor
the
instruments;
it
is
the
fully
reduced
data.
• All
science
will
be
come
from
survey
catalogs
and
images
Telescope
è
Images
è
Catalogs
5. 5GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
Open
Data,
Open
Source:
A
Community
Resource
− LSST
data,
including
images
and
catalogs,
will
be
available
with
no
proprietary
period
to
the
astronomical
community
of
the
United
States,
Chile,
and
Interna%onal
Partners
− Alerts
to
variable
sources
(“transient
alerts”)
will
be
available
world-‐wide
within
60
seconds,
using
standard
protocols
− LSST
data
processing
stack
will
be
free
soKware
(licensed
under
the
GPL,
v3-‐or-‐later)
− All
science
will
be
done
by
the
community
(not
the
Project!),
using
LSST’s
data
products
6. 6GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
LSST
Survey
Themes
Time
Domain
Science
Census
of
the
Solar
System
Mapping
the
Milky
Way
Understanding
the
Nature
of
Dark
MaVer
and
Dark
Energy
and
everything
in
between
…
7. 7GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
Current
Status
− December
6th,
2013:
Passed
the
NSF
Final
Design
Review;
declared
ready
for
Construc<on.
− January
17th,
2014:
FY2014
budget
signed,
with
NSF
appropria<on
allowing
for
LSST
start.
− May
8th,
2014:
NSB
authorizes
NSF
Director
to
start
the
project.
− Expec5ng
the
signing
of
coopera5ve
agreement
and
start
of
construc5on
this
month.
8. 8GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
Combined
Primary/Ter%ary
Mirror
Thin
Meniscus
Secondary
− Primary-‐Ter)ary
was
cast
in
the
spring
of
2008.
− Fabrica)on
underway
at
the
Steward
Observatory
Mirror
Lab
-‐
comple)on
by
the
end
of
2014.
− Secondary
substrate
fabricated
by
Corning
in
2009.
− Currently
in
storage
wai)ng
for
construc)on.
9. 9GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
LSST
Camera
Parameter
Value
Diameter
1.65
m
Length
3.7
m
Weight
3000
kg
F.P.
Diam
634
mm
1.65 m
5’-5”
– 3.2 Gigapixels
– 0.2 arcsec pixels
– 9.6 square degree FOV
– 2 second readout
– 6 filters
10.
11. 11GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
Bandpasses:
u,g,r,i,z,y
R
~
0.2
spectrograph
12. 12GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
LSST
Observatory
(cca.
late
~2018)
13. 13GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
HQ
Site
Science
Opera)ons
Observatory
Management
Educa)on
and
Public
Outreach
Archive
Site
Archive
Center
Alert
Produc)on
Data
Release
Produc)on
Calibra)on
Products
Produc)on
EPO
Infrastructure
Long-‐term
Storage
(copy
2)
Data
Access
Center
Data
Access
and
User
Services
Summit
and
Base
Sites
Telescope
and
Camera
Data
Acquisi)on
Crosstalk
Correc)on
Long-‐term
storage
(copy
1)
Chilean
Data
Access
Center
Dedicated
Long
Haul
Networks
Two
redundant
40
Gbit
links
from
La
Serena
to
Champaign,
IL
(exis)ng
fiber)
14. 14GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
LSST
Data
Products:
Images
and
Catalogs
15. 15GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
Why
We
Create
Catalogs?
Model
ß
inference
–
Data
And
metadata!
Model
ß
inference
–
Catalog
ß
Data
Processing
–
Data
Project
Scien<sts
Scien<sts
Scien)sts
Project
Project
Project
Scien)sts
Computa)onally
(and
cogni)vely)
expensive,
science-‐case
speciific
Computa)onally
cheaper,
Easier
to
understand,
Science-‐case
speciific
• Computa)onally
expensive,
general
• Reprojec)on;
may
or
may
not
involve
compression
• Almost
always
introduces
some
informa)on
loss
• Data
Processing
==
Instrumental
Calibra)on
+
Measurement
16. 16GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
Guiding
Principles
for
LSST
Products
− There
are
virtually
infinite
op)ons
on
what
quan))es
one
can
measure
on
images
− But
if
catalog
genera)on
is
understood
as
a
(generalized)
cost
reduc<on
tool,
the
guiding
principles
become
easier
to
define
− Defining
principles
for
the
LSST
data
products:
1. Maximize
science
enabled
by
the
catalogs
- Working
with
images
takes
)me
and
resources;
a
large
frac)on
of
LSST
science
cases
should
be
enabled
by
just
the
catalog.
2. Minimize
informa%on
loss
- Provide
(as
much
as
possible)
es)mates
of
likelihood
surfaces,
not
just
single
point
es)mators
3. Provide
and
document
the
transforma%on
(the
soKware)
17. 17GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
LSST
From
the
User’s
Perspec%ve
− A
stream
of
~10
million
)me-‐domain
events
per
night,
detected
and
transmiped
to
event
distribu)on
networks
within
60
seconds
of
observa)on.
− A
catalog
of
orbits
for
~6
million
bodies
in
the
Solar
System.
− A
catalog
of
~37
billion
objects
(20B
galaxies,
17B
stars),
~7
trillion
single-‐epoch
detec)ons
(“sources”),
and
~30
trillion
forced
sources,
produced
annually,
accessible
through
online
databases.
− Deep
co-‐added
images.
− Services
and
compu)ng
resources
at
the
Data
Access
Centers
to
enable
user-‐specified
custom
processing
and
analysis.
− Soqware
and
APIs
enabling
development
of
analysis
codes.
Level
3
Level
1
Level
2
18. 18GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
LSST
Catalog
Contents
(Level
2)
− Object
characteriza%on
(models):
• Moving
Point
Source
model
• Double
Sérsic
model
(bulge+disk)
- Maximum
likelihood
peak
- Samples
of
the
posterior
(hundreds)
− Object
characteriza%on
(non-‐parametric):
• Centroid:
(α,
δ),
per
band
• Adap)ve
moments
and
ellip)city
measures
(per
band)
• Aperture
fluxes
and
Petrosian
and
Kron
fluxes
and
radii
(per
band)
− Colors:
• Seeing-‐independent
measure
of
object
color
− Variability
sta%s%cs:
• Period,
low-‐order
light-‐curve
moments,
etc.
LSST
Science
Book,
Fig.
9.3
19. 19GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
Never
measure
off
the
coadds!
− Impossible
to
combine
mul)-‐epoch
data
taken
in
different
seeings/
exposure
)mes
without
informa)on
loss
• Subop)mal
S/N
− Warping
and
resampling
correlates
pixel
values
and
noise;
correla)on
matrices
are
(prac)cally)
impossible
to
carry
forward.
• Source
of
systema)c
error
− Detector
effects
have
to
be
taken
out
at
the
pixel
level
• Further
correlates
the
noise
− The
effec)ve
bandpass
changes
from
exposure
to
exposure.
• Coadding
different
sorts
of
apples…
− Stars
move!
20. 20GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
Op%mal
Mul%-‐Epoch
Measurement
Exposure
1
Exposure
2
Exposure
3
Coadd
Galaxy
/
Star
Models
Fiyng
Warp,
Convolve
Coadd
Measurement
Exposure
1
Exposure
2
Exposure
3
Galaxy
/
Star
Models
Transformed
Model
1
Transformed
Model
2
Transformed
Model
2
Warp,
Convolve
Fiyng
Mul)Fit
(Simultaneous
Mul)-‐Epoch
Fiyng)
Hard,
but
we
only
have
to
do
it
once.
Easy;
rela)vely
few
data
points.
Easier
(depends
on
model),
but
we
have
to
do
it
every
itera5on!
Same
number
of
parameters,
but
with
orders
of
magnitude
more
data
points.
21. 21GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
Sampling
and
retaining
the
posterior
(likelihood)
Perform
importance
sampling
from
a
proposal
distribu<on
determined
on
the
coadd.
Plan
to
characterize
(and
keep!)
the
full
posterior
for
each
object.
(Unexplored)
possibili5es
for
compression.
22. 22GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
Op%mal
Mul%epoch
Source
Measurements
Op)mal
measurement
of
proper)es
of
objects
imaged
in
mul)ple
epoch.
Leq:
extrac)on
of
a
moving
point
source
(Lang
2009).
Individual
exposures:
objects
are
undetected
or
marginally
detected
Moving
point-‐source
and
galaxy
models
are
indis)nguishable
on
the
coadd
23. 23GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
Where
Does
Calibra%on
Come
In?
− With
mul)-‐epoch
data,
(rela)ve)
instrument
calibra)on
becomes
inextricably
connected
with
the
measurement
process.
− Instrumental:
• Photometric
- Provide
above-‐the-‐atmosphere
flux
es)mate
through
a
standard
passband
(rela)ve
and
absolute).
• Astrometric
- Provide
the
posi)on
of
each
object
with
respect
to
an
external
(Gaia)
reference
frame.
• Shapes
- Provide
an
unbiased
es)mate
of
some
measure
of
the
PSF-‐deconvolved
shape
of
each
object.
− Astrophysical:
• No
unique
answer,
as
it
depends
on
(subjec)ve)
externally
imposed
priors.
It
is
not
a
data
product
of
the
LSST
project.
• Short
answer
(in
the
Galac)c
context):
Gaia!
24. 24GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
LSST
Photometric
Calibra%on
(In
Brief)
− Goal:
es)mate
(PSF)
flux
above
the
atmosphere
through
a
standard
bandpass
that
does
not
change.
− Approach:
• Directly
measure
the
system
bandpass
(monochroma)c
flats)
• Measure
and
model
the
atmospheric
bandpass
- A
calibra)on
telescope
will
take
spectra
of
standard
stars
as
the
observing
unfolds.
These
will
be
used
to
fit
a
nightly,
slowly
changing,
atmosphere
model
(MODTRAN).
- Addi)onal
measurements
of
precipitable
water
vapor
will
be
collected
with
a
GPS
system
and
a
co-‐boresighted
microwave
radiometer.
Needed
to
accurately
calibrate
the
y
band.
- Idea
being
explored:
It
is
possible
that
the
atmospheric
bandpasses
could
be
derived
from
the
imaging
data
alone.
• Run
self-‐calibra)on
(Ubercal)
to
determine
the
rela)ve
zero-‐points.
• Tie
to
an
external
system
(DA
WD
standards
or
Gaia).
25. 25GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
Astrometry:
Tree
Rings
Above:
PRNU
(Photo-‐response
non-‐
uniformity)
of
an
LSST
sensor
segment.
One
sees
tree
rings,
sensi)vity
varia)ons
at
at
a
~percent
level.
Due
to
varying
dopant
density
in
silicon
boules,
which
creates
parasi)c
lateral
E
fields.
The
gotcha:
these
DO
NOT
behave
as
QE
varia)ons.
If
you
try
to
flat
field
this,
you
make
the
problem
WORSE
by
~2x.
27. 27GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
Astrometry/Photometry:
The
“Brighter-‐Fafer
PSF”
Effect
Most
of
today’s
devices
(DES,
HSC,
LSST,
GPC1)
are
thick.
The
photon
conver)ng
at
the
top
has
a
long
way
to
go
to
reach
the
bopom
− Tree
rings
(and
related
effects)
− “Brighter-‐fafer”
effect
As
the
poten)al
wells
fill
up
with
electrons,
the
bias
voltage
drops
making
it
easier
for
electrons
to
be
diverted
to
neighboring
pixels.
Correlates
the
values
of
neighboring
pixels;
results
in
an
intensity-‐dependent
PSF.
HSC
LSST
28. 28GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
Astrophysical
Calibra%on:
Gaia,
the
“Great
Calibrator”
− Will
provide
a
network
of
astrometric
standards
covering
the
en)re
sky
− Similarly,
will
provide
an
internally
consistent
photometric
catalog
to
aid
calibra)on
− Scien)fically,
it
will
determine
to
unprecedented
precision
a
number
of
astrophysical
rela)ons
that
will
directly
enable
LSST
science:
• Directly
calibrate
color-‐luminosity
(photometric
parallax)
rela)ons
for
MS
and
other
stars
• Calibrate
period-‐luminosity
rela)ons
for
a
wide
range
of
variables
− Enable
the
LSST
to
extend
Galac)c
census/maps
4-‐7
magnitudes
deeper
Eyer,
Ivezic
&
Monet
Sec<on
6.12,
LSST
Science
Book
hVp://ls.st/sb
29. 29GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
Chapter 6: Stellar Populations
Chapter 6: Stellar Populations
6: A comparison of photometric, proper motion and parallax errors for SDSS, Gaia and LSST, as a function
Table 6.6: Adopted Gaia and LSST Performance
Quantity Gaia LSST
Sky Coverage whole sky half sky
Mean number of epochs 70 over 5 yrs 1000 over 10 yrs
Mean number of observations 320a over 5 yrs 1000b over 10 yrs
Wavelength Coverage 320–1050 nm ugrizy
Depth per visit (5 , r band) 20 24.5; 27.5c
Bright limit (r band) 6 16-17
Point Spread Function (arcsec) 0.14⇥0.4 0.70 FWHM
Pixel count (Gigapix) 1.0 3.2
Syst. Photometric Err. (mag) 0.001, 0.0005d 0.005, 0.003e
Syst. Parallax Err. (mas) 0.007f 0.40f
Syst. Prop. Mot. Err. (mas/yr) 0.004 0.14
nsit includes the G-band photometry (data collected over 9 CCDs), BP and RP spec-
metry, and measurements by the SkyMapper and RVS instruments.
over all six bands (taken at di↵erent times).
dded data, assuming 230 visits.
ransit and the end-of-mission values for the G band (from SkyMapper; integrated BP
hotometry will be more than about 3 times less precise).
e visit and co-added observations, respectively.
tric errors depend on source color. The listed values correspond to a G2V star.
Gaia
and
LSST:
The
Science
LSST
Science
Book:
hVp://ls.st/sb
Gaia:
hVp://sci.esa.int/gaia
30. 30GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
Figure
3.13,
LSST
Science
Book
hVp://ls.st/sb
The
volume
number
density
(stars/kpc3/mag,
log
scale
according
to
legend)
of
∼2.8
million
SDSS
stars
with
14
<
r
<
21.5
and
b
>
70◦,
as
a
func)on
of
their
distance
modulus
(distances
range
from
100
pc
to
25
kpc)
and
their
g
−
i
color.
The
sample
is
dominated
by
color-‐selected
main
sequence
stars.
31. 31
GaiaCal
2014
|
Ringberg,
Germany
|
July
9,
2014
LSST:
Mapping
the
Milky
Way
Halo
RR
Lyrae
limit
MS
stars
limit
Dwarf
Galaxies
32. 32GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
LSST:
Extended
Mapping
of
the
Milky
Way
− LSST
will
enable
extended
mapping
of
the
Milky
Way
because
of
a
unique
combina)on
of
capabili)es:
• The
existence
of
the
u
band,
allowing
the
measurement
of
stellar
metallici<es
of
near
turn-‐off
stars
and
its
mapping
throughout
the
observed
disk
and
halo
volume.
• The
near-‐IR
y
band,
allowing
the
mapping
of
stellar
number
densi<es
and
proper
mo<ons
even
in
regions
of
high
ex<nc<on.
• Well
sampled
<me
domain
informa<on,
allowing
for
the
unambiguous
iden<fica<on
and
characteriza<on
of
variable
stars
(e.g.,
RR
Lyrae),
facilita<ng
their
use
as
density
and
kinema<c
tracers
to
large
distances.
• Proper
mo<on
measurements
for
stars
4
magnitudes
fainter
than
will
be
obtained
by
Gaia
(see
LSST
Science
Book;
§
3.6).
• The
depth
and
wide-‐area
nature
of
the
survey,
which
combined
with
the
characteris<cs
listed
above,
permits
a
uniquely
uniform,
comprehensive,
and
global
view
of
all
luminous
Galac<c
components.
Juric
&
Bullock
Sec<on
7.2
LSST
Science
Book
hVp://ls.st/sb
33. 33GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
Discussion
Point:
Can
We
Skip
the
Catalogs?
− As
our
measurements
become
more
and
more
systema)cs
limited,
what
occurs
in
the
“Data
Processing”
box
above
becomes
incredibly
important.
− Some)mes,
an
assump)on
or
an
algorithmic
choice
that’s
been
made
there
may
introduce
a
systema)c
that
drowns
out
the
signal
(or
eliminates
it).
• Different
deblending
algorithm
(or
no
deblending)
• Extremely
crowded
field
photometry
(e.g.,
globular
clusters)
• Searching
for
SNe
light
echos
• Characteriza)on
of
diffuse
structures
(e.g.,
ISM)
− For
op)mal
inference,
one
would
always
construct
a
measurement
that
directly
forward-‐models
the
aspect
of
the
imaging
data
they’re
interested
in,
and
not
the
catalog.
Or
derive
a
more
appropriate
catalog.
Can
we
do
this?
Model
ß
inference
–
Data
Model
ß
inference
–
Catalog
ß
Data
Processing
–
Data
34. 34GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
Discussion
Point:
Can
We
Skip
the
Catalogs?
− Some
reasons
we
don’t
do
this:
1. Computa)onally
(and
I/O
!!)
intensive
2. Conceptually
difficult
- Exper)ze
in
sta)s)cs,
applied
math,
and
soqware
engineering
is
oqen
not
there
- Catalogs
are
too
oqen
taken
as
“$DEITY
given”,
fundamental,
result
of
a
survey
− Things
are
changing
• Big
data
problems
are
becoming
increasingly
computa)onally
tractable
- Most
of
the
cost
of
LSST
DM
is
not
in
the
hardware,
it’s
in
the
people
wri)ng
the
soqware.
LSST
compu)ng
hardware
in
~2020
==
~$3-‐5M/yr
(just
~200
TFLOPS!).
• The
basic,
modular,
soqware
components
are
being
made
available
by
big
surveys,
lowering
the
barrier
to
entry.
Average
astronomer
in
the
2020s
will
grow
up
with
an
expecta)on
of
being
well
versed
in
Stats,
SE,
Appl.
Math.
Model
ß
inference
–
Data
Model
ß
inference
–
Catalog
ß
Data
Processing
–
Data
35. 35GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
Discussion
Point:
Can
We
Skip
the
Catalogs?
− LSST
“Level
3”
concept
is
the
first
step
in
that
direc)on:
Enabling
the
community
to
create
new
products
using
LSST’s
soqware,
services,
or
compu)ng
resources.
This
means:
• Providing
the
soKware
primi%ves
to
construct
custom
measurement/inference
codes
• Enabling
the
users
to
run
those
codes
at
the
LSST
data
center,
leveraging
the
investment
in
I/O
(piggyback
onto
LSST’s
data
trains).
− Looking
ahead:
Right
now,
we
see
the
data
releases
as
the
key
product
of
a
survey.
By
the
end
of
LSST,
I
wouldn’t
be
surprised
if
we
saw
the
soKware
as
the
key
product,
with
hundreds
specialized
(and
likely
ephemeral)
catalogs
being
generated
by
it.
− The
“data
releases”
will
just
be
some
of
those
catalogs,
designed
to
be
more
broadly
useful
than
others,
and
retained
for
a
longer
period
of
)me.
− LSST
soKware
soKware
and
hardware
is
being
engineered
to
make
this
possible.
36. 36GAIACAL 2014 | RINGBERG, GERMANY | JULY 9, 2014
Summary
− LSST
will
provide
photometric
(ugrizy)
and
shape
characteriza)on
for
~40B
objects
(to
r=27.5)
over
~half
the
sky
(southern
hemisphere),
with
~800-‐1000
epochs
per
object
(to
r=24.5).
− LSST
products
will
consist
of
images,
catalogs,
and
the
soqware
used
to
produce
them.
We
try
to
minimize
informa)on
loss
by
retaining
the
informa)on
about
the
likelihoods
(extended
sources
only
(for
now)).
We
will
report
above-‐the-‐atmosphere
fluxes
calibrated
to
a
standard
band.
− Astrophysical
calibra)on
of
LSST
is
not
a
formal
part
of
the
project;
the
community
(Science
Collabora)ons)
is
beginning
to
think
about
it.
In
the
Galac)c
context,
Gaia
will
provide
crucial
calibra)ons
for
LSST.
− Going
forward,
we
expect
that
enabling
user-‐generated
soqware
(“Level
3”)
will
be
increasingly
important.