Cluster Difference Imaging Photometric Survey (CDIPS)
Primary Investigator: Luke Bouma
HLSP Authors: Luke Bouma
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 as a GitHub repository, and should be cited as an independent software reference (Bhatti et al., 2019, ).
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://weng1234.cn/tess/).
篮球竞猜盈利|湖南体育职业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.
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.
篮球竞猜盈利|湖南体育职业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:
- <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|
篮球竞猜盈利|湖南体育职业 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.
篮球竞猜盈利|湖南体育职业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
篮球竞猜盈利|湖南体育职业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:
CDIPS data products are available in the MAST Portal and astroquery.mast. For those who want to download light curves for a single target, or all light curves for a given Sector, see the following Python code example below. NOTE:篮球竞猜盈利|湖南体育职业 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 # Search for CDIPS light curves within 0.001 degrees of V684 Mon. obs_table = Observations.query_criteria(objectname="V684 Mon", radius=".001 deg", provenance_name="CDIPS") print("Found " + str(len(obs_table)) + " CDIPS light curves.") # Get list of available products for this Observation. cdips_products = Observations.get_product_list(obs_table) # Download the products for this Observation. manifest = Observations.download_products(cdips_products) # Search for CDIPS light curves directly based on TIC ID. ticid = '220314428' obs_table = Observations.query_criteria(target_name=ticid, provenance_name="CDIPS") print("Found " + str(len(obs_table)) + " CDIPS light curves.") # Get list of available products for this Observation. cdips_products = Observations.get_product_list(obs_table) # Download the products for this Observation. manifest = Observations.download_products(cdips_products) print("Done") # Get all CDIPS light curves for a given Sector, may not work # depending on bandwidth and traffic, we suggest you use bulk # download scripts instead. 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.
Please remember to cite the appropriate paper(s) below and the if you use these data in a published work.
Note: These HLSP data products are licensed for use under .