CDIPS

Cluster Difference Imaging Photometric Survey (CDIPS)

Primary Investigator: Luke Bouma

HLSP Authors: Luke Bouma

Released: 2019-10-04

Updated: 2019-12-09

Primary Reference(s): Bouma et al. (2019)

DOI: 10.17909/t9-ayd0-k727

Citations: See ADS Statistics

Read Me

CDIPS star positions on the sky. — CDIPS target star positions (blue) and nominal TESS observing footprint (gray). Target stars are either candidate members of clusters, or else have other youth indicators. Most will be observed for one or two lunar months during the TESS Prime Mission.

Overview

The TESS mission has been releasing full-frame images recorded at 30 minute cadence. Using the TESS images, the CDIPS team has begun a Cluster Difference Imaging Photometric Survey (CDIPS), in which they are making light curves for stars that are candidate members of open clusters and moving groups. They have also included stars that show photometric indications of youth. Each light curve represents between 20 and 25 days of observations of a star brighter than Gaia Rp magnitude of 16. The precision of the detrended light curves is generally in line with theoretical expectations.

The pipeline is called "cdips-pipeline", and it is available for inspection as a GitHub repository, and should be cited as an independent software reference (Bhatti et al., 2019, http://doi.org/10.5281/zenodo.3370324).

Before using the light curves, the team strongly recommends that you become familiar with the TESS data release notes, and also consult the TESS Instrument Handbook, available at MAST (http://archive.stsci.edu/tess/).

DR1 CONTENTS

The first CDIPS data release (2019-10-02) contains 159,343 light curves of target stars that fell on silicon during TESS Sectors 6 and 7. They cover about one sixth of the Galactic plane. The target stars are described and listed in Bouma et al. 2019. They are stars for which a mix of Gaia and pre-Gaia kinematic, astrometric, and photometric information suggest either cluster membership or youth.

DR2 CONTENTS

The second CDIPS data release (2019-12-09) contains 355,380 light curves of target stars that fell on silicon during TESS Sectors 8, 9, 10 and 11. Combined with DR1, Galactic longitudes from ~190 to 320 degrees are covered, totalling about half a million stars brighter than Gaia-Rp of 16. The reduction methods used for the second release are identical to those from Bouma et al. 2019, except as noted in the CDIPS README file. Target stars have had claims of youth in the literature. Their light curves are amenable for studies in stellar and exoplanetary astrophysics.

Data Products

Each target is stored in a sub-directory based on the Sector it was observed in as a 4-digit zero-padded number. They are further divided into sub-directories based on the camera and chip number they are on. For example, "s0006/cam1_ccd1/" for Sector 6 light curves that are on CCD #1 on Camera #1.

The light curves are in a FITS format familiar to users of the Kepler, K2, and TESS-short cadence light curves made by the NASA Ames team. Their file names follow this convention:

hlsp_cdips_tess_ffi_gaiatwo<gaiaid>-<sectornum>-<cam-chip>_tess_v01_llc.fits

where:

<gaiaid> = full Gaia DR2 target id, e.g., "0003321416308714545920"
<sectornum> = 4-digit, zero-padded Sector number, e.g., "0006"
<cam-chip> = the camera and chip numbers, e.g., "cam2-ccd4"

Data file types:

_llc.fits	extracted light curve file

The primary header contains information about the target star, including the catalogs that claimed cluster membership or youth ("CDIPSREF"), and a key that enables back-referencing to those catalogs in order to discover whatever those investigators said about the object ("CDEXTCAT"). Membership claims based on Gaia-DR2 data are typically the highest quality claims. Cross-matches against TICv8 and Gaia-DR2 are also included.

The sole binary table extension contains the light curves. Three aperture sizes are used:

APERTURE1 = 1 pixel in radius
APERTURE2: = 1.5 pixels in radius
APERTURE3 = 2.25 pixels in radius

Three different types of light curves are available. The first is the raw "instrumental" light curve measured from differenced images. The second is a detrended light curve that regresses against the number of principal components noted in the light curve's header. The third is a detrended light curve found by applying TFA with a fixed number of template stars. The recommended time stamp is "TMID_BJD", which is the exposure mid-time at the barycenter of the solar system (BJD), in the Temps Dynamique Barycentrique standard (TDB). For further details, please see Bouma et al. 2019, or send emails to the authors.

The full set of available time-series vectors is as follows:

TTYPE1 = 'BGE ' / Background measurement error
TTYPE2 = 'BGV ' / Background value (after bkgd surface subtrxn)
TTYPE3 = 'FDV ' / Measured D value (see Pal 2009 eq 31)
TTYPE4 = 'FKV ' / Measured K value (see Pal 2009 eq 31)
TTYPE5 = 'FSV ' / Measured S value (see Pal 2009 eq 31)
TTYPE6 = 'IFE1 ' / Flux error in aperture 1 (ADU)
TTYPE7 = 'IFE2 ' / Flux error in aperture 2 (ADU)
TTYPE8 = 'IFE3 ' / Flux error in aperture 3 (ADU)
TTYPE9 = 'IFL1 ' / Flux in aperture 1 (ADU)
TTYPE10 = 'IFL2 ' / Flux in aperture 2 (ADU)
TTYPE11 = 'IFL3 ' / Flux in aperture 3 (ADU)
TTYPE12 = 'IRE1 ' / Instrumental mag error for aperture 1
TTYPE13 = 'IRE2 ' / Instrumental mag error for aperture 2
TTYPE14 = 'IRE3 ' / Instrumental mag error for aperture 3
TTYPE15 = 'IRM1 ' / Instrumental mag in aperture 1
TTYPE16 = 'IRM2 ' / Instrumental mag in aperture 2
TTYPE17 = 'IRM3 ' / Instrumental mag in aperture 3
TTYPE18 = 'IRQ1 ' / Instrumental quality flag ap 1, 0/G OK, X bad
TTYPE19 = 'IRQ2 ' / Instrumental quality flag ap 2, 0/G OK, X bad
TTYPE20 = 'IRQ3 ' / Instrumental quality flag ap 3, 0/G OK, X bad
TTYPE21 = 'RSTFC ' / Unique frame key
TTYPE22 = 'TMID_UTC' / Exp mid-time in JD_UTC (from DATE-OBS,DATE-END)
TTYPE23 = 'XIC ' / Shifted X coordinate on CCD on subtracted frame
TTYPE24 = 'YIC ' / Shifted Y coordinate on CCD on subtracted frame
TTYPE25 = 'CCDTEMP ' / Mean CCD temperature S_CAM_ALCU_sensor_CCD
TTYPE26 = 'NTEMPS ' / Number of temperatures avgd to get ccdtemp
TTYPE27 = 'TMID_BJD' / Exp mid-time in BJD_TDB (BJDCORR applied)
TTYPE28 = 'BJDCORR ' / BJD_TDB = JD_UTC + TDBCOR + BJDCORR
TTYPE29 = 'TFA1 ' / TFA Trend-filtered magnitude in aperture 1
TTYPE30 = 'TFA2 ' / TFA Trend-filtered magnitude in aperture 2
TTYPE31 = 'TFA3 ' / TFA Trend-filtered magnitude in aperture 3
TTYPE32 = 'PCA1 ' / PCA Trend-filtered magnitude in aperture 1
TTYPE33 = 'PCA2 ' / PCA Trend-filtered magnitude in aperture 2
TTYPE34 = 'PCA3 ' / PCA Trend-filtered magnitude in aperture 3

Note: a very small number of targets fall on more than one camera-chip combination in a given Sector. In these cases, there are multiple files produced. One example is Gaia DR2 3041652034662522752 in Sector 7, which falls on both Camera 1 CCD 1 and Camera 2 CCD4, and thus has two files:

/s0007/cam1_ccd1/hlsp_cdips_tess_ffi_gaiatwo0003041652034662522752-0007-cam1-ccd1_tess_v01_llc.fits
/s0007/cam2_ccd4/hlsp_cdips_tess_ffi_gaiatwo0003041652034662522752-0007-cam2-ccd4_tess_v01_llc.fits

Data Access

CDIPS data products are available in the MAST Portal and astroquery.mast. For those who want to download all the light curves for a given Sector, the following Python code example below will query for all the available CDIPS observations for a given Sector (should take a few minutes) and then download the light curve FITS files. NOTE: There are tens of thousands of light curves for a given Sector, thus downloading all of the products can take the better part of a day, even with good internet connections. By default, the light curve files will be downloaded under a folder called "mastDownload" in the same working directory that your run the Python script from.

from astroquery.mast import Observations
sector_num = '6'
print('Querying for CDIPS Sector ' + sector_num + " Observations...")
obsTable = Observations.query_criteria(provenance_name = "CDIPS",
                                       sequence_number = sector_num)
print("Found a total of " + str(len(obsTable)) + " CDIPS targets.")
print('Downloading data products for these observations...')
for obs in obsTable:
    data_products = Observations.get_product_list(obs)
    Observations.download_products(data_products)

NOTE: The above query can timeout for some users, due to internet bandwidth or traffic on the database at MAST. If so, an alternative is to use the bulk download scripts, which will download products via cURL commands given the complete list of CDIPS targets for a given Sector.