GOODS-Herschel data release 1 - readme
======================================

This is the readme for the first data release of the GOODS-Herschel project. A
more complete document, a PDF file named GOODS-Herschel_release.pdf, is
accompanying this readme. It is very important you read this document as it
gives instructions on how to use these data. The PDF file should be found
where you got the GOODS-Herschel data, or can be downloaded from the Herschel
Database in Marseille (HeDaM - http://hedam.oamp.fr/GOODS-Hersche).

GOODS-Herschel (Elbaz et al, 2011, A&A, 533, 119) is in ESA open time key
project consisting of the deepest Herschel observations of the two *Great
Observatories Origins Deep Survey* (GOODS) fields in the Northern and Southern
hemispheres.

A - Catalogues
--------------

The release contains two multi-lambda catalogues, one for each field:
GOODS-North and GOODS-South.

### 1. Column description

  Column         Unit   Description
  -------------- ------ -----------------------------------------------
  iau_name              GOODS IAU coded object identifier
  id                    Sequential id (specific to the catalogue)
  ra             deg    Right Ascension
  dec            deg    Declination
  f3p6           uJy    IRAC 3.6 um flux density
  err3p6         uJy    Error on IRAC 3.6 um flux density
  flag3p6               IRAC 3.6 um source extraction flag (see below)
  f4p5           uJy    IRAC 4.5 um flux density
  err4p5         uJy    IRAC 4.5 um flux density
  flag4p5               IRAC 4.5 um source extraction flag (see below)
  f5p8           uJy    IRAC 5.8 um flux density
  err5p8         uJy    Error on IRAC 5.8 um flux density
  flag5p8               IRAC 5.8 um source extraction flag (see below)
  f8p0           uJy    IRAC 8.0 um flux density
  err8p0         uJy    Error on IRAC 8.0 um flux density
  flag8p0               IRAC 8.0 um source extraction flag (see below)
  f24            uJy    MIPS 24 um flux density
  err24_ima      uJy    MIPS 24 um flux error on residual map
  err24_sim      uJy    MIPS 24 um flux error on Monte-Carlo simulations
  cov24                 MIPS 24 um coverage map value (equal to sec/pixel)
  f70            uJy    MIPS 70 um flux density
  err70_ima      uJy    MIPS 70 um flux error on residual map
  err70_sim      uJy    MIPS 70 um flux error on Monte-Carlo simulations
  cov70                 MIPS 70 um coverage map value (equal to sec/pixel)
  f100           uJy    PACS 100 um flux density
  err100_ima     uJy    PACS 100 um flux error on residual map
  err100_sim     uJy    PACS 100 um flux error on Monte-Carlo simulations
  cov100                PACS 100 um coverage map value (proportional to sec/pixel)
  f160           uJy    PACS 160 um flux density
  err160_ima     uJy    PACS 160 um flux error on residual map
  err160_sim     uJy    PACS 160 um flux error on Monte-Carlo simulations
  cov160                PACS 160 um coverage map value (proportional to sec/pixel)
  f250           uJy    SPIRE 250 um flux density
  err250_ima     uJy    SPIRE 250 um flux error on residual map
  err250_sim     uJy    SPIRE 250 um flux error on Monte-Carlo simulations
  cov250                SPIRE 250 um coverage map value (proportional to sec/pixel)
  f350           uJy    SPIRE 350 um flux density
  err350_ima     uJy    SPIRE 350 um flux error on residual map
  err350_sim     uJy    SPIRE 350 um flux error on Monte-Carlo simulations
  cov350                SPIRE 350 um coverage map value (proportional to sec/pixel)
  f500           uJy    SPIRE 500 um flux density
  err500_ima     uJy    SPIRE 500 um flux error on residual map
  err500_sim     uJy    SPIRE 500 um flux error on Monte-Carlo simulations
  cov500         uJy    SPIRE 500 um coverage map value (proportional to sec/pixel)
  clean_index           Index measuring flux contamination from nearby sources (see
                        below)

On GOODS-South, the catalogue has no SPIRE measurements (columns f250 to
cov500).

### 2. Notes on some column contents

#### a. error columns

Please, read the GOODS-Herschel release document for a complete description of
the two noise estimations: errNNN_ima based on the residual map and errNNN_sim
based on Monte-Carlo simulations. In particular, to be conservative, users
should always use the highest uncertainty but not the quadratic combination of
both since they are not independent. Also, the Monte-Carlo simulations were
made on regions with relatively homogeneous exposure time; therefore,
uncertainties derived from these simulations are not suitable and hence not
provided for sources situated outside these homogeneous exposure time regions.

#### b. IRAC source extraction flag

The IRAC source extraction flag come from the IRAC flag maps as described in
the GOODS project DR1 documentation. It's a composite flag based on the values
from the table below.

  Flag value   Condition
  ------------ -------------------------------------------
  0            > 50% of the modal exposure time
  1            < 50% of the modal exposure time
  2            < 20% of the modal exposure time
  16           Region with significant residual muxbleed
  64           No data (zero retained exposure time)

These values will often appear in combination. For example, regions with < 20%
of the modal exposure time (flag value 2) also have < 50% of the modal
exposure time (flag value 1). Therefore, those sources will have flag values
of 2 + 1 = 3. Regions with no data will have flag values 64 + 2 + 1 = 67.
Regions with residual muxbleed (flag 16) and also < 50% modal exposure time
(flag 1) will have flag 16 + 1 = 17.

#### c. clean_index

The clean_index measures the flux contamination by nearby sources. It is
computed as follows:

clean_index =
    Neib24 + Neib100 × 10 + Neib160 × 100 + Neib250 × 1.000 + Neib350 ×
    10.000 + Neib500 × 100.000

Where Neib24 (resp. Neib100, Neib160…) is the number of bright neighbours (see
the GOODS-Herschel release document) at 24 um (resp. 100 um 160 um…). On
GOODS-South, it is assumed that Neib250 = Neib350 = Neib500 = 0.

B - Maps
--------

For each Herschel filter (PACS100, PACS160, SPIRE250, SPIRE350 and SPIRE500 on
GOODS-North; only PACS bands on GOODS-South) we provide three four fits files:

 -   GH_(field)_(version)_(lambda)_sci.fits: the flux map
 -   GH_(field)_(version)_(lambda)_err.fits: the error map
 -   GH_(field)_(version)_(lambda)_cov.fits: the coverage map
 -   GH_(field)_(version)_(lambda)_psf.fits: the PSF of the map