# Presentation: 314957

## Great Surveys and the 

- Great Surveys and the
- Culture of Astronomy:
- an optically biased view

- Josh Frieman

## Astronomical Surveys have a long history

- Suzhou Astronomical Chart (1247), China

## 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

## William, Caroline, & John Herschel

- Catalogue of Nebulae and Clusters
- Hunting for binary stars for parallax measurements

## Palomar Sky Survey

- Edwin
- Hubble

**Notes:**
U. Chicago undergrad and PhD

Studied law at Oxford (Rhodes scholar)

Hobnobbed with hollywood celebs

## Fritz Zwicky

- Early surveys:
- Lone wolves or
- very small teams

**Notes:**
Zwicky:

Spherical bastards

Fritz: the bastards are here!

Rifle shot to smooth the turbulence

Swiss: confined to Mt. Wilson during the war; 
Good observing during LA blackouts

## 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 @ 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)

- SNAP collaboration

## 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

## (How) are surveys changing the culture of Astronomy?

## 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)

## 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

## 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 don’t get rewarded
- How to mentor students/postdocs/jr. faculty in
-     large, long-term projects? Alienation.

## 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

## 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

## 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

## 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

## The Rise of the Survey Astronomer

- Old astronomy classification:
-      instrument builder||observer||theorist
- Survey astronomy classification:
-     instrument builder|software 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?

## 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