DELVE (DECam Local Volume Exploration) Survey
Description
The DECam Local Volume Exploration Survey (DELVE) combines archival DECam data with 126 nights of novel observations (PropID 2019A-0305) to study dwarf satellite galaxies over a wide range of luminosities and environments. DELVE is divided into three survey components (WIDE, MC, and DEEP), each of which have their own observational strategy and data reduction pipeline. By combining novel observations with existing data from DES, DECaLS, and other community programs, DELVE will provide complete contiguous DECam coverage of the entire high-Galactic-latitude southern sky. By the end of the survey, DELVE is expected to cover >17,000 deg² to a depth of griz ∼ 23.5 mag, ∼2,200 deg² around the Magellanic Clouds to a depth of gri ∼ 24.5 mag, and ∼135 deg² around isolated Magellanic analogs in the Local Volume to a depth of gi ∼ 25.5 mag.
For more complete information about DELVE, please visit: https://delve-survey.github.io. For a full list of results from the DELVE Collaboration or using the DELVE data, visit https://delve-survey.github.io/#papers.
The delve_dr2.objects table and delve_dr1.objects table have been crossmatched against our default reference datasets within a 1.5 arcsec radius, nearest neighbor only. These tables will appear with x1p5 in their name in our table browser. Example: delve_dr2.x1p5__objects__gaia_dr3__gaia_source.
| DELVE at a Glance | |||
|---|---|---|---|
| Survey | Area | Bands | Depth |
| DELVE-WIDE | ∼17,000 deg² | griz | ∼23.5 mag |
| DELVE-MC | ∼2,200 deg² | gri | ∼24.5 mag |
| DELVE-DEEP | ∼135 deg² | gi | ∼25.5 mag |
DELVE DR2 Coverage Map
Scientific Goals
DELVE seeks to understand the faintest and most dark-matter-dominated galaxies in a range of environments. The DELVE data enable the detection and characterization of dwarf galaxies over a broad range of luminosity, from the ultra-faint satellites of the Milky Way (L ∼ 10³L⊙), to the Magellanic Clouds (L ∼ 10⁹⁻¹⁰ L⊙), and analogs of the Magellanic Clouds in the Local Volume. DELVE consists of:
- DELVE-WIDE: a high-Galactic-latitude wide-area survey focused on resolved stellar systems (e.g., ultra-faint satellite galaxies and stellar streams) in the Milky Way halo.
- DELVE-MC: a contiguous Magellanic Cloud periphery survey to study the structure, evolution, star formation history, and satellite population of the Magellanic Clouds.
- DELVE-DEEP: a deep targeted survey of four Magellanic Cloud analogs (Sextans B, NGC 55, NGC 300, IC 5152) in the Local Volume to study the satellite populations of isolated low-mass hosts.
These three survey components will provide complementary insights into the abundance and properties of satellite galaxies around hosts at different mass scales and in a variety of environments. Combined with a unified theoretical framework, DELVE will explore the dependence of satellite properties on effects of reionization, host halo properties, and environment, offering essential insights into the galaxy–halo connection. More information about DELVE can be found at: https://delve-survey.github.io/.
Data Releases
Second Data Release (DELVE DR2)
The second DELVE data release (DELVE DR2; Drlica-Wagner et al. 2022) is hosted at the Astro Data Lab and consists of catalog-level coadds in griz from the DELVE-WIDE processing. DELVE DR2 covers >20,000 deg² in each individual band and ∼17,000 deg² in all bands simultaneously. The DELVE DR2 catalog contains ∼2.5 billion unique objects, and ∼618 million unique objects that were observed in all four bands.
| DELVE DR2 Summary | |
|---|---|
| Bands | griz |
| Area Covered (all bands) | 16,972 deg² |
| Area Covered (griz) | 24663, 22939, 21283, 22866 deg² |
| Depth (5σ PSF; griz) | 24.3, 23.9, 23.5, 22.8 mag |
| Depth (5σ AUTO; griz) | 23.9, 23.5, 23.0, 22.4 mag |
| Seeing (griz) | 1.24, 1.10, 1.02, 1.00 |
| Number of Objects | 2,500,247,752 |
| Photometric Uniformity | 7.3 mmag |
| Astrometric Accuracy | 22 mas |
DELVE DR2 MDN Photometric Redshifts
We derive a sample of photometric redshift estimates for objects in the DELVE DR2 catalog using a Mixture Density Network (MDN) trained on griz magnitudes and colors. This network outputs the weights, means, and standard deviations of 20 Gaussian distributions, which are then combined into a single probability density function (PDF) from which samples of the photo-z can be drawn. The resulting photo-z information can be accessed using the interfaces described in the Data Access section below, and the delve_dr2.photoz table can be browsed using the Data Lab table browser.
Training Data
The MDN photo-z estimator was trained on a sample of objects with measured spectroscopic redshifts. This spec-z catalog was assembled by cross-matching sources from DELVE DR2 with several spectroscopic catalogs: SDSS,
2DF,
6DF,
VIPERS,
GAMA,
2dFLens,
DES_AAOMEGA,
DES_IMACS,
WIGGLEZ,
DEEP2,
3D-HST,
VVDS,
CLASH-VLT,
ACES,
N17B331,
SAGA,
SPT_GMOS,
UDS,
C3R2,
ATLAS,
VANDELS,
SPARCS,
GLASS,
CDB,
ELG FIGS,
VUDS,
ZFIRE,
and
MOSFIRE.
We selected the matched objects that remained after a set of cuts.
- SNR > 5 in the g band and SNR > 3 in the riz bands (when each band was available).
- g < 22.5
- 0.01 < z-spec < 2
- We removed objects with 178 < RA < 182 and Dec < 5 due to issues in a preliminary version of the DELVE catalog.
A set of color cuts were applied to both the training and testing datasets.
- Objects with g, r, i and z measurements available were required to have: (g-r) < 4, -1 < (r-i) < 4, and -1 < (i-z) < 4
- Objects with just g, r and i measurements available were required to have: (g-r) < 4 and -1 < (r-i) < 4
The magnitude ranges of the data used to train the network were:
- 13.478 < g < 22.500
- 12.634 < r < 23.297
- 12.270 < i < 22.751
- 12.016 < z < 22.859
Approximately 1.4 million matched objects had measured spectroscopic redshifts. After cuts, we end up with ~1.1 million objects. Every object in this final set has griz photometry measured by DELVE. Below we show the magnitude distributions of the objects used to train and evaluate the MDN model.
Below we show the spec-z distribution (bin size = 0.01) of the training and testing samples. The training sample was selected to be as uniform as possible in redshift to avoid biases. In practice, this resulted in a flat distribution up to z = 0.6.
Performance Metrics
We used point estimates (taking the most probable value from the PDF) to build our metrics ("point-like metrics"). We define the following metrics:
- The photo-z bias is defined as Δz = zphot - zspec.
- The median bias, median(Δz), is useful for identifying possible systematic effects in the redshift estimation
- The outlier fraction is defined as |Δz|/( 1 + zspec) > 0.15 within a given redshift bin.
- The normalized median absolute deviation measures the dispersion of the bias and is defined as σNMAD = 1.48 × median(| (Δz - median(Δz)) / (1 + zspec) |)
- The relative errors are defined as mean( Δz / (1 + z) ) within photo-z intervals of 0.05.
These metrics were calculated using a spec-z interval of 0.05. Below we show the normalized median absolute deviation, median bias, outlier fraction, and relative error for the MDN photo-z sample evaluated on our test sample of objects.
-- The DELVE Team
First Data Release (DELVE DR1)
The first DELVE data release (DELVE DR1; Drlica-Wagner et al. 2021) is hosted at the Astro Data Lab and consists of catalog-level coadds in griz from the DELVE-WIDE processing. DELVE DR1 covers >5,000 deg² in each individual band and ∼4,000 deg² in all bands simultaneously. The DELVE DR1 catalog contains ∼520 million unique objects.
| DELVE DR1 Summary | |
|---|---|
| Bands | griz |
| Area Covered (all bands) | 4,075 deg² |
| Area Covered (griz) | 5599, 5106, 5065, 5153 deg² |
| Depth (5σ PSF; griz) | 24.3, 23.9, 23.3, 22.8 mag |
| Depth (5σ AUTO; griz) | 23.9, 23.4, 22.9, 22.4 mag |
| Seeing (griz) | 1.28, 1.16, 1.07, 1.04 |
| Number of Objects | 519,737,142 |
| Photometric Uniformity | 9.1 mmag |
| Astrometric Accuracy | 22 mas |
Data Reduction
Each subcomponent of DELVE uses slightly different data reduction pipelines. While DELVE DR1 and DR2 come solely from the WIDE survey, we provide brief descriptions of each pipeline below. More detailed descriptions of the DELVE data processing can be found in Drlica-Wagner et al. (2021).
- DELVE-WIDE: The DELVE-WIDE data are processed with the DES Data Management (DESDM) pipeline (Morganson et al. 2018). SourceExtractor is used to create catalogs from the reduced ("Final Cut") images, and Gaia DR2 (Gaia Collaboration 2018) is used for astrometric calibration. Photometric calibration is performed with the forward global calibration module (Burke et al. 2018) within the DES footprint and against ATLAS Refcat2 (Tonry et al. 2018) outside the DES footprint. The best references for the DELVE-WIDE processing are the DELVE DR1 paper (Drlica-Wagner et al. 2021) and the DESDM paper (Morganson et al. 2018).
- DELVE-MC: The DELVE-MC images are reduced with the DECam Community Pipeline (Valdes et al. 2014), which is based on an early versions of the DESDM pipeline. The DELVE-MC processing is performed using DELVERED, a modified version of the DAOPhot-based PHOTRED (Nidever et al. 2017), an automated and robust PSF photometry pipeline based on the DAOPHOT suite of programs (Stetson 1987, 1994). Astrometric calibration is performed against Gaia DR2 and photometric calibration is performed against ATLAS Refcat2 (Tonry et al. 2018). The best current reference for the DELVE-MC processing is Nidever et al. (2017).
- DELVE-DEEP: The DELVE Collaboration is currently exploring various options for processing the DELVE-DEEP data. To date, the DELVE-DEEP data have been processed by both the DESDM pipeline for image coaddition and the DELVERED pipeline for crowded-field photometry.
Data Access
The DELVE data are accessible by a variety of means:
Data Lab Table Access Protocol (TAP) service
TAP provides a convenient access layer to the DELVE catalog database. TAP-aware clients (such as TOPCAT) can point to https://datalab.noirlab.edu/tap, select the delve_dr2 database, and see the database tables and descriptions. You can also view the DELVE tables and descriptions in the Data Lab table browser.
Data Lab Query Client
The Query Client is available as part of the Data Lab software distribution. The Query Client provides a Python API to Data Lab database services. These services include anonymous and authenticated access through synchronous or asynchronous queries of the catalog made directly to the database. Additional Data Lab services for registered users include personal database storage and storage through the Data Lab VOSpace.
The Query Client can be called from a Jupyter Notebook on the Data Lab Notebook server. Example notebooks are provided to users upon creation of their user account (register here), and are also available to browse on GitHub at https://github.com/astro-datalab/notebooks-latest.
Image Cutouts
The Data Lab Simple Image Access (SIA) service provides a fast way to retrieve cutouts from DELVE images processed with the DECam Community Pipeline (Valdes et al. 2014). For an example of how to use the SIA service, see this Jupyter notebook. Note that DELVE process images with the DES Data Management Pipeline (Morganson et al. 2018), which differs slightly from the DECam Community Pipeline.
Jupyter Notebook Server
The Data Lab Jupyter Notebook server (authenticated service) contains examples of how to access and visualize the DELVE catalog:
Acknowledgments
The DECam Local Volume Exploration Survey (DELVE; NOAO Proposal ID 2019A-0305, PI: Drlica-Wagner) is partially supported by Fermilab LDRD project L2019-011 and the NASA Fermi Guest Investigator Program Cycle 9 No. 91201.
This project used data obtained with the Dark Energy Camera (DECam), which was constructed by the Dark Energy Survey (DES) collaboration. Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana–Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministério da Ciência, Tecnologia e Inovação, the Deutsche Forschungsgemeinschaft and the Collaborating Institutions in the Dark Energy Survey.
The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Enérgeticas, Medioambientales y Tecnológicas–Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the University of Edinburgh, the Eidgenössische Technische Hochschule (ETH) Zürich, Fermi National Accelerator Laboratory, the University of Illinois at Urbana-Champaign, the Institut de Ciències de l'Espai (IEEC/CSIC), the Institut de Física d'Altes Energies, Lawrence Berkeley National Laboratory, the Ludwig-Maximilians Universität München and the associated Excellence Cluster Universe, the University of Michigan, the National Optical Astronomy Observatory, the University of Nottingham, the Ohio State University, the OzDES Membership Consortium, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, and Texas A&M University.
Based in part on observations at Cerro Tololo Inter-American Observatory, National Optical Astronomy Observatory, which is operated by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation.
Database access and other data services are hosted by the Astro Data Lab at the Community Science and Data Center (CSDC) of the National Science Foundation National Optical Infrared Astronomy Research Laboratory, operated by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation.
