# Presentation: 265865

## Science Ops Breakout	WBS 3.2

**Bill Carithers**
**LBNL**
**July 2002**

## Science Ops Outline

**Deal mostly with computing since Calibration and Simulation have dedicated breakout sessions**
**Work with  SNFactory**
**Joint study with STScI**
**Some details**
**Some “big pictures”**
**Assumptions, Issues, and Options**

- Warning label– this talk has a dangerous level of acronyms

## Work with SNFactory

**SNFactory in many ways is like a prototype SNAP**
**Working with SNFactory on pipeline and scheduler**
**Expect that some SNFactory analysis code will migrate to SNAP**
**Advantage of working with real data**

## Joint study with STScI

**Analyze SNAP mission operations and perform trade studies on alternative operational scenarios**
**Design a SNAP Ground System architecture based on existing STScI HST systems**
**Estimate the STScI development costs for STScI segments of the Ground System**
**Examine incremental cost of GO program**

## Simulation status

**Now have an end-to-end supernova simulation working**
**Starts with cosmological model and generates supernova fluxes in various filters at various times along the light curve**
**Includes astrophysical effects such as dust, lensing, k-corrections, etc.**
**Instrument emulation is parametric (fast) as opposed to pixel-level**
**Provides Hubble diagram of pseudo-mission as input to cosmological filter for mission optimization studies**
**Cosmological fitter working**
**Takes Hubble diagram and fits for cosmological parameters**

## Simulation R&D goals

**Complete *****SNAPfast***** simulation studies**
**Iterate ****mission requirements for CDR**
**Develop *****SNAPsim***
**Add full pixel-level instrument response emulation**
**Generate synthetic data**

## Calibration Overview

**Calibration is a big part of most missions**
**HST spends about 15% of program on calibration**
**On-going program**
**Some parts of calibration are straightforward**
**“****Gain” corrections [flat-fielding]**
**“****bias” corrections [darks]**
**But...**
**Some are difficult: **
** ****	****	****absolute spectral shape, **

- see Susana Deustua’s talk in SC1 breakout

## Calibration R&D Goals

**Carry out trade studies on telescope design/platforms**
**Design spectrophotometric calibration experiment**
**Further investigate and constrain sources of systematics**
**Develop calibration error matrix**
**Develop SNAP on-board calibration observing program**
**Study impact of  on-board calibration on **
**observatory management**
**observing strategy, and hence SN program**
**Study effect of calibration requirements on SNAP design**
**e.g. lamps, diffusers, filters **
**monitoring of observatory during operation**
**Develop realistic time, cost and manpower estimates for calibration program:**
**spectrophotometric**
**operational**

## Image cleaning data flow, step 1

- Down-link from spacecraft

- CCSDS error
- correction/format

- Multiple
- Ground
- Stations?

- Concatenate

- Remove
- duplicates

- Uncompress

- Re-format from
- Native to FITS

- yes

- no

- Calibration sequence

- CCSDS = Consultative Committee
- for Space Data Systems

**FITS = Flexible Image**
**Transport System**

## Image cleaning data flow, step 2

- Receive and
- reformat

- Calibration
- Database

- Gain corrections
- (flat-fielding)

- Cosmic ray
- removal

- Alignment with
- Fiducial stars

- Combine dithered
- images

- Science quality image

- darks

- flats

## Big picture

- Pipeline
- Manager

- Scheduler

- Exception
- Handler

*Normal *closed loop

- Lots of things can/will
- Go wrong

## Assumptions, Issues, and Options

**Data processing engine **
**Data model**
**Language(s)**
**Code repository**
**Supported platforms and Operating systems**
**Databases**
**Catalogued and archived data structure**
**Details, details, details, ...**

- Aka,   “what are you going to do about ___?”
- 	“have you thought about___?”

## Data processing engine

- Standard Model assumption:
- National Energy Research Scientific
- 	Computing Center(NERSC)

- Parallel Distributed Data
- System Facility (PDSF)

- High Performance Storage System(HPSS)

- Option: set up our own cluster with
- help from Mid-Range Computing Project

## PDSF

- Used for CDF, Babar, STAR, et al. analyses

## Data Model

**Flexible Image Transport System (FITS) is a natural assumption**
**Many available packages in the public domain for analyzing FITS data: **
**Image Reduction and Analysis Facility(IRAF)**
**IRAF does standard CCD image manipulation, photometry, etc.**
**Astronomers already know this. For the HEP people, think PAW, MINUIT**
**Astro-speak: “I did the photometry with APPHOT from IRAF”**
**Hep-speak: “These are MIGRAD errors from a MINUIT fit”**

## Language(s)

**Can be a hot-button issue**
**Modern object-oriented approach (C****++**** , Java) attractive for maintainability over long life of the project**
**	****	****	****but...**
**Legacy code issue**
**C, IDL, Fortran, LISP,...**
**Pipeline manager can support modules written in different languages**
**Interesting developments to watch**
**PyRAF project at STScI: re-writing IRAF in Python**

## Code repository

**Concurrent Versions System (CVS) is a natural assumption**
**CVS is a tool for managing code changes. When a developer wants to change a module, he/she must “check it out” of the repository before making any changes and “check it back in” after modifications have been made and QA tests have been run. CVS provides a complete history of all code changes.**
**Great deal of successful history in high energy physics experiments with diverse development environments**
**Examples of CVS users:**
**CDF**
**BaBar**
**SDSS**
**GEANT4**
**Mozilla**
**GNOME**

## Catalogued and Archived Data Structures

**Will save raw data but would like a more manageable data structure**
**“****Postage Stamp” concept is attractive**
**Example: save a 200 x 200 pixel piece of CCD image centered on the supernova**
**Save reference images, raw data in 9 filters plus dithers, calibrated data in 9 filters, raw and calibrated spectroscopic data**
**Save derived data such as light curve fit, peak brightness, all corrections, host galaxy information, ...**
**Other (i.e., non supernova) transients**
**Similar “postage stamp” for triggered transients**
**Derived data would include magnitudes in each filter, position coordinates, estimated period or rise/fall times for spatially-fixed transients, or estimate of velocity for moving sources**
**Co-adds for deep field**
**Full calibrated image co-added periodically**

## Platforms, OS

**Trends have been toward commodity PC’s running RedHat Linux**
**Option of Java Virtual Machine**

## Summary

**Software architecture and framework includes many issues and options that must be settled in the R&D phase**
**Natural assumptions related to maximum re-use of successful experience in both astronomical and high energy physics applications**