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# SPHEREx Archive at IRSA User Guide
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## Document Purpose and Scope
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The purpose of this document is to facilitate science with SPHEREx data by providing users with an overview of the SPHEREx data that are available at the NASA/IPAC Infrared Science Archive (IRSA) at Caltech, as well as instructions for accessing and downloading these data.
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We also provide tips for exploring the data and getting help with any questions you may have.
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This User Guide will evolve as the SPHEREx project delivers new data products and tools to IRSA to make available to the public.
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More information is available in the "Mission Overview" section of the SPHEREx Explanatory Supplement.
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## Additional Resources
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[SPHEREx archive at IRSA](https://irsa.ipac.caltech.edu/Missions/spherex.html)
Copy file name to clipboardExpand all lines: documentation/spherex_data_access.md
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These layers include:
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***Browsable Directories:** SPHEREx on-premises data products are laid out in directories that can be navigated with standard web browsers.
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These data products are mirrored on AWS.
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These data products are mirrored on AWS.
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***Application Program Interfaces:** IRSA provides program-friendly Application Program Interfaces (APIs) to access SPHEREx Spectral Image data.
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The on-prem and cloud-hosted Quick Release 2 Spectral Images that have been released thus far are accessible via the [Simple Image Access V2 protocol](https://ivoa.net/documents/SIA/20151223/) defined by the International Virtual Observatory Alliance ([IVOA](https://ivoa.net)).
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Cutouts of the Spectral Image data held on-prem are available via IRSA's Cutout Service.
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The on-prem and cloud-hosted Quick Release 2 Spectral Images that have been released thus far are accessible via the [Simple Image Access V2 protocol](https://ivoa.net/documents/SIA/20151223/) defined by the International Virtual Observatory Alliance ([IVOA](https://ivoa.net)).
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Cutouts of the Spectral Image data held on-prem are available via IRSA's Cutout Service.
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***Python Packages:** SPHEREx data at IRSA are accessible via the Python packages [pyvo](https://pyvo.readthedocs.io/en/latest/) and [astroquery](https://astroquery.readthedocs.io/en/latest/ipac/irsa/irsa.html).
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***SPHEREx Data Explorer:** IRSA provides a web-based Graphical User Interface (GUI) that makes it easy to search for, visualize, and download SPHEREx data.
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This tool provides access to the on-prem copy of the data.
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This tool provides access to the on-prem copy of the data.
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Each of these data access layers is described in greater detail in the subsections below.
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IRSA provides API access to SPHEREx Spectral Image Multi-Extension FITS files (MEFs) and associated calibration files through [version 2 of the VO Simple Image Access (SIA2) protocol](https://ivoa.net/documents/SIA/20151223/).
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SIA2 allows users to query for a list of images that satisfy constraints based on position(s) on the sky, band, time, ID, and instrument.
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The list returned by the service includes a data access URL for each image.
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These can be used to retrieve the on-prem-hosted images using `wget` or `curl`. The returned list also returns cloud access information.
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These can be used to retrieve the on-prem-hosted images using `wget` or `curl`.
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The returned list also returns cloud access information.
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A brief summary of SIA2 for accessing SPHEREx data for IRSA is given below.
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Additional [documentation on IRSA’s SIA2 service](https://irsa.ipac.caltech.edu/ibe/sia.html) can be found on the IRSA website.
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: This module provides access to IRSA's public astrophysics data from projects such as SPHEREx, Euclid, Spitzer, WISE/NEOWISE, SOFIA, IRTF, 2MASS, Herschel, IRAS, and ZTF.
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Examples of data queries using both of these libraries can be found in [IRSA’s Python Notebook Tutorial Repository](https://caltech-ipac.github.io/irsa-tutorials/). For example:
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Examples of data queries using both of these libraries can be found in [IRSA’s Python Notebook Tutorial Repository](https://caltech-ipac.github.io/irsa-tutorials/).
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For example:
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The notebook titled [Introduction to SPHEREx Spectral Images](https://caltech-ipac.github.io/irsa-tutorials/spherex-intro/) shows how to use the Astroquery library to execute an IVOA Simple Image Access (SIA2) query for SPHEREx spectral images that cover the specified coordinates and collection.
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This summary includes filenaming conventions, for which we adopt the following definitions:
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-`Planning Period` designates the survey plan uploaded to the spacecraft, e.g. `2025W18_2B`.
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Each planning period covers approximately 3.5 days of operation.
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Each planning period covers approximately 3.5 days of operation.
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-`Observation ID` includes the survey planning period and the large and small slew counters.
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For example, `2025W18_2B_0001_1` contains the planning period (`2025W18_2B`), the large slew counter (`0001`), and the small slew counter (`1`).
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Each large slew has a maximum of 4 small slews, so the allowed small slew counter range is 1 to 4.
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Some large slews will have fewer than 4 small slews.
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For example, `2025W18_2B_0001_1` contains the planning period (`2025W18_2B`), the large slew counter (`0001`), and the small slew counter (`1`).
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Each large slew has a maximum of 4 small slews, so the allowed small slew counter range is 1 to 4.
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Some large slews will have fewer than 4 small slews.
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-`Detector` is an integer from 1 through 6.
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-`Version` is the version of this file, e.g. 'l2' for "level 2" data products.
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-`Processing Date` includes the year and the number of days into the year, e.g. `2025-164`.
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## Main Science Data Product: Spectral Image Multi-Extension FITS Files (MEF)
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The main Quick Release data product is the Level 2 Spectral Image MEF, as described in Section 2.1 of the [Explanatory Supplement](https://irsa.ipac.caltech.edu/data/SPHEREx/docs/SPHEREx_Expsupp_QR.pdf).
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: Calibrated surface brightness flux density in units of MJy/sr, stored as a 2040 x 2040 image.
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No zodiacal light subtraction is applied.
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The SPHEREx focal plane is split with a dichroic to three short-wavelength and three long-wavelength detector arrays.
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Two focal plane assemblies (FPAs) simultaneously image the sky through a dichroic beam splitter.
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Each FPA contains three 2K x 2K detector arrays placed behind a set of linear variable filters (LVFs), providing narrow-band response with a band center that varies along one axis of the array.
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: Spectral World Coordinate System (WCS) FITS-compliant lookup table that maps spectral image pixel coordinates to central wavelengths and bandwidths.
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The lookup table consists of 1 row with 3 columns (X, Y, VALUES).
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X and Y are each arrays defining a grid of control points in spectral image pixel space.
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VALUES is an array of two-element arrays: at each (X, Y) control point, the two-element array contains the central wavelength and the corresponding bandwidth.
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VALUES is an array of two-element arrays: at each (X, Y) control point, the two-element array contains the central wavelength and the corresponding bandwidth.
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:::{note}
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Adopted to support the unique nature of the SPHEREx LVF filters, this rarely-used part of the FITS standard has yet to be implemented by all readers.
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IRSA's Cutout Service provides spatial subsets of the SPHEREx Spectral Image MEFs.
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Information on how to use the Cutout Service is provided in the {ref}`access-spectral-image-cutouts` section of this User Guide.
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The cutout MEFs returned from this service contain the same HDUs as the original Spectral Images (IMAGE, FLAGS, VARIANCE, ZODI, PSF, WCS-WAVE). However, the IMAGE, FLAGS, VARIANCE, AND ZODI HDUs have been modified to include only those pixels within the specified cutout size.
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The cutout MEFs returned from this service contain the same HDUs as the original Spectral Images (IMAGE, FLAGS, VARIANCE, ZODI, PSF, WCS-WAVE).
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However, the IMAGE, FLAGS, VARIANCE, AND ZODI HDUs have been modified to include only those pixels within the specified cutout size.
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The WCS-WAVE HDU has also modified to provide the correct mapping between the pixels in the cutout to wavelength.
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The PSF HDU from the original spectral image is included unmodified in the cutout MEF.
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The spatially-varying PSF is represented as an image cube with 121 planes.
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Each plane is a 101x101 pixel image representing a PSF for a different region of the detector. Users interested in performing photometry on a cutout using the information in the cutout PSF HDU will need to understand how to find the most applicable PSF cube plane for each pixel in the cutout.
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Each plane is a 101x101 pixel image representing a PSF for a different region of the detector.
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Users interested in performing photometry on a cutout using the information in the cutout PSF HDU will need to understand how to find the most applicable PSF cube plane for each pixel in the cutout.
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The basic steps are described below, and a [Python notebook tutorial](https://caltech-ipac.github.io/irsa-tutorials/spherex-psf/) is provided to help users get started with a simple implementation.
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1. Determine the 0-based pixel coordinates of the position of interest in the IMAGE HDU of the cutout.
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The Absolute Gain Matrix products are ~16 MB FITS image files (one per detector) with dimensions 2,040 × 2,040 and units of (MJy/sr) / (e−/s).
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-`dichroic_D1_spx_base-2025-158.fits`
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## Calibration Product: Electronic Gain Factors
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The Electronic Gain Factor product is a single YAML file that includes the provenance information for the detectors and a list of 32 gain values per detector.
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*Filename Format:*
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-`gain_factors_spx_base-[Processing Date].yaml`
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## Additional Product: Nonfunctional Pixels
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The Nonfunctional Pixel products are ~32 MB FITS image files (one per detector) with dimensions 2,040 x 2,040.
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Pixel values are 1 for pixels known to be permanently non-functioning and 0 otherwise.
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## Additional Product: Nonlinearity Parameters
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The Nonlinearity Parameter products are ~79 MB multi-extension FITS files (one per detector).
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Each file contains 5 extensions (Q_nl, b1, b2, b3, Qmax), each of which is an image with dimensions 2,040 x 2,040.
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These extensions are described in Section 3.2.1 of the [SPHEREx Explanatory Supplement](https://irsa.ipac.caltech.edu/data/SPHEREx/docs/SPHEREx_Expsupp_QR.pdf).
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