Great Surveys and the

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Great Surveys and the Culture of Astronomy an
optically biased view
Josh Frieman
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Astronomical Surveys have a long history
Suzhou Astronomical Chart (1247), China
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Astronomical Surveys have a long history
Charles Messier (1730-1817) Federally supported
researcher (French Navy) In the course of his
search for comets, catalogued 110 nebulae and
star clusters Lesson 1 unforeseen collateral
science payoffs often more interesting than the
original survey goals
M101
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William, Caroline, John Herschel
Catalogue of Nebulae and Clusters Hunting for
binary stars for parallax measurements
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Palomar Sky Survey Edwin Hubble
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Fritz Zwicky Early surveys Lone wolves or
very small teams
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What has changed in surveys?

• Scientific motivation cataloging vs. addressing
  specific questions?
• Growing scale and technical complexity
• Growing pixel count computational scale
• Concomitant increases in size of survey teams
• SDSS-II 300 astronomers _at_ 25
  institutions
• Role of the individual division of labor
• Fraction of community resources going into
  surveys as opposed to other projects
• Cross-fertilization with alien cultures (e.g.,
  HEP)

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SNAP collaboration
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Why surveys?

• Opening discovery space vs. addressing specific
  
• questions
• Survey design often driven by specific science
• questions, but unforeseen collateral science
• payoffs often more interesting/varied than the
  
• original goals scientific landscape evolves
• between survey design execution
• Growing issue as project timescales escalate
• Are we designing too many surveys to address
• too few/narrow questions? Anticipate
  evolution?
• Competition/obsolescence/redundancy

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(How) are surveys changing the culture of
Astronomy?
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The Two Cultures a Caricature

• Astronomy
• NSF
• Observatories
• Charismatic lone wolf or
• casually organized small groups
• Study of diverse objects to
• understand their structure
• evolution the fox
• Short-timescale projects,
• quick dirty analysis
• the hare
• HST (NASA)
• Cf. C.P. Snow, S.D.M. White, M.
  Weber, I. Berlin

• High-energy physics
• DOE
• Experiments
• Large collaborations rules
• bureaucratic structures
• Large surveys to
• address few questions the
• hedgehog
• Long-timescale projects,
• lengthy analysis
• the tortoise
• WMAP (NASA)

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The Two Cultures

• NSF science-driven proposals
• Faster, cheaper
• Less oversight, more flexibility,
• less likely to reach design
• goals but deploy and do science
• anyway (the hare)
• More groups/projects
• supported
• Lean mean
• Example ground-based CMB
• experiments
• When projects reach a certain scale
  complexity, the
• small science approach simply breaks
  down. Cf. SDSS

• DOE cradle to grave
• support for people
• Enables build-up of long-
• term expertise in hardware
• software teams, espec. in
• DOE and NASA labs
• Slower, more expensive
• greater funding stability
• comes with more intrusive
• management oversight
• Greater emphasis on meeting
• (but not exceeding) science
• requirements best vs. good

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Sociological Evolution

• Growing ambition complexity have led to greater
  
• preponderance of large-scale, large-team
  surveys
• Is this bad for Astronomy?
• Concerns raised
• Too few resources left over for small astronomy
• Big-project bureaucracies crush creative
• entrepreneurship, innovative ideas, individuals
• Hardware/software builders dont get rewarded
• How to mentor students/postdocs/jr. faculty in
• large, long-term projects? Alienation.

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Benefits of Surveys

• Vastly increased science reach/discovery power
• Efficient engines of discovery economy of scale
• Expense is rewarded by greater science return
• Public availability of data levels the playing
  field
• between rich and poor institutions,
  redressing the
• historical public/private imbalance in
  optical
• astronomy
• Public availability of data encourages greater
• entrepreneurship data mining is
  easier/faster/
• cheaper than having to go out and obtain new
  data
• Surveys enable broad range of science,
• research opportunities for students,
  postdocs,
• Learn to play well with others

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The Structure of Survey Collaborations

• The shared data rights model
• The collaborative experiment model
• Institutional buy-in vs. technical contributions
• Relation between project builders science
  groups
• Size of collaborations and hierarchy of project
• management should be scaled to
  size/complexity/
• science richness of the survey
• Surprisingly, making data public on a fairly
  rapid
• timescale is not a deterrent to joining
  collaborations
• benefits of being part of the team. What
  happens
• when team boundaries are layered? LSST

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Formalizing Collaboration Structures

• Professional project management w/ clear lines of
  responsibility WBS, etc
• Collaboration governing policies
• Define collaboration membership
• Define members rights responsibilities
• Publication policy
• Mechanisms to maintain vertical
• horizontal lines of communication
• Mechanisms to resolve disputes
• Mechanisms to encourage collaboration
  across
• institutional and other boundaries
• Mechanisms to reward infrastructure work

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Remember those in the trenches

• How to encourage/mentor junior people to engage
• in survey infrastructure work without
  exploiting
• them?
• Increasing challenge as timescales for project
  design/construction increase
• Need clear paths/expectations
• Recognizing/rewarding their contributions
• data access
• co-authorship rights
• freeing up time to pursue science
• partnering with those focused on science
  analysis
• The Builder concept
• SDSS was by and large successful in this regard

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The Rise of the Survey Astronomer

• Old astronomy classification
• instrument builderobservertheorist
• Survey astronomy classification
• instrument buildersoftware pipeline
  developer -- data analyst/data miner -- theorist
• Surveys have created a new class of astronomers
  who understand and analyze a lot of data but who
  seldom/never go to telescopes. Is that bad?

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Publications/Authorship

• Mechanisms for recognizing scientific
  contributions are evolving (Cf. HEP)
• First-authorship for primary science analyzer is
  traditional, but it weights science analysis work
  more than infrastructure contributions fairness
  issues
• Does alphabetical authorship discourage science
  analysis/entrepreneurship?
• Hybrid approach? DES
• Have a clear policy well before data flows