leveraging LSST
Tony Tyson
Director, LSST Project
University of California, Davis
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7.5 arcminutes
DSS: digitized photographic plates
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Sloan Digital Sky Survey
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LSST -- almost
2800
galaxies
i<25 mag
×200 for one DESpec FOV
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LSST Observing Cadence
Pairs of 15 second exposures (to 24.5 mag) per visit to a given
position in the sky.
Visit this position again within the hour with another pair of
exposures.
Number of 9.6 sq.deg FOV visits per night: 900
Deep-Drilling: 1 hour per night on a selected field.
Continuous 15 sec exposures
Detection of transients announced within 60 seconds.
Expect ~1 million per night
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Number of visits per field in Deep Wide Survey
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Two planned LSST surveys
MAIN SURVEY
Deep Wide Survey: 20,000 square degrees to a uniform depth of
u: 26.7 g: 27.4 r: 27.7 i: 26.9 z: 26.1 y: 24.9
DEEP DRILLING
10% of time:
~30 selected fields. 300 square degrees
Continuous 15 sec exposures. 1hour/night
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LSST Wide-Fast-Deep survey
4 billion galaxies with photometric
redshifts
20 trillion photometric measurements
of 20 billion objects
70PB database
Immediate transient alerts
LSST Science Book v2.0 written by LSST Collaboration
 245 authors
 598 pages
 Living document
(on lsst.org)
http://www.lsst.org/lsst/scibook
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LSST Science Charts New Territory
Probing Dark Matter
And Dark Energy
Mapping the Milky Way
Finding Near Earth Asteroids
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Three classes of useful LSST spectroscopy
1. Calibration samples for quantities that can be derived from
photometric data: photometric redshifts for galaxies,
photometric metallicity for stars
2. Supplemental data that cannot be obtained from LSST data:
radial velocity, emission and absorption line strengths
3. Identification spectra for transient, weird and unusual
objects (SNe, GRB followup, high-z quasars, brown dwarfs)
These differ by the needed sample size, sample depth, required
spectral resolution, and the time delay relative to imaging data.
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The vast majority of LSST science uses LSST
multi-band time domain photometry data alone
Some exceptions:
1. Photo-z needs spec for r<24 mag over 10 sq.deg -> 10meter
2. TRANSIENTS
• Rare bright needs 2-4meter
• Faint (22-24mag) needs 10-30meter
3. Strong lensing: magnified source spectroscopy
4. SNe z<1.2
5. Stellar
needs 10meter
mostly hi-res
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190 sq deg
DESpec on Blanco*
to 24.5
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Alert Rate
In ten minutes time the LSST transient pipeline is
likely to issue ~10,000 alerts at 5σ.
While most of these will be moving objects,
perhaps several thousand will be flaring objects
or bursts. Possibly new kinds of objects!
Clearly any feasible spectroscopic followup at 23
mag will lag behind ~1 hour per hour. What is
needed then is highly trusted event classification.
FAST
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DESpec coverage of LSST surveys
 ¼ of main 20,000 sq.deg to 22.5 mag at S/N=5
0.7% of gold sample of i<25 mag galaxies
useful for correlation calibration of photo-z
 deep spectroscopy of deep drilling fields
20 fields, 20 hours each, 50 nights
~24 mag @ S/N=10 per 10Å bin
transient host spectra
 co-observing spectroscopy of deep drilling fields
1 hour / night
~22 mag @ S/N=5 per 1Å bin
transient spectra
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CLASSIFICATION
DATA PRODUCTS
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Science at the Limit
 Much of the breakthrough science using
surveys (imaging or spectroscopy) occurs
at the limits of the surveys


Noise, Sample incompleteness
Subtle systematic errors
Statistical studies must be corrected
for these errors
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large spectroscopic samples are useful
Example: even an incomplete spectroscopic sample
can help photo-z in two ways
1. angular cross correlation with faint photometric
sample will calibrate photo-z statistically (Newman)
2. large spectroscopic samples can improve
knowledge of evolution of galaxy SEDs.
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There are currently 20 telescopes
larger than 3m with spectrographs
that can reach the LSST survey area.
Even in the ELT era, wide field multiobject spectroscopy on 4-10m class
telescopes will be useful
Statistics matters. Calibration of the
70PB LSST database, and massively
parallel follow-up of a million
transients will be complementary to
selected faint object spectroscopy
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LSST science deliverables do
not require followup
spectroscopy
But we can and should pursue a
range of followup programs,
from co-observing highly parallel
spectroscopy, to individual
object followup.
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Assuring accurate classification
 Characterize the known
clustering)
 Assign the new
(classification)
 Discover the unknown
(outlier detection)
Tom Vestrand
Benefits of very large data sets:
• best statistical analysis of “typical” events
• automated search for “rare” events
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The dimension reduction problem:
Finding correlations and “fundamental planes”
of parameters
• The Curse of High
Dimensionality !
– Are there combinations
(linear or non-linear
functions) of observational
parameters that correlate
strongly with one another?
– Are there basis sets that
represent the full set of
properties?