Archives at NExScI

Before any code, it helps to know what you are actually querying.

nexsci-rv is a client for the radial velocity (RV) archives hosted at NExScI (the NASA Exoplanet Science Institute at IPAC/Caltech). Each archive is exposed as its own class with the same set of query and download methods. Two archives are supported today.

NEID

NEID is a high-precision radial velocity (RV) spectrograph on the WIYN 3.5-m telescope at Kitt Peak. It records starlight spectra precisely enough to detect the tiny wobble a planet induces on its host star.

EXPRES

EXPRES, the EXtreme PREcision Spectrograph, sits on the Lowell Discovery Telescope. This client exposes only its solar-feed data — the Sun observed as a star — which NExScI hosts on the same IPAC TAP service as NEID, and only at the solar L1 and L2 levels.

The solar feed

Both instruments have a small telescope that feeds sunlight into the spectrograph during the day, observing the Sun as if it were a distant star. That data lives in a parallel set of solar feed data levels (solarl0…solareng for NEID; solarl1/solarl2 for EXPRES). It is a dense, high-cadence RV time series, used to study solar activity and to validate each instrument’s long-term stability.

What a query gives you

An archive query returns a table of metadata describing matching observations — times, targets, conditions, RV values, file names. The actual spectra are FITS files you fetch separately with download. See Querying vs downloading, and proprietary data for that two-step workflow.

The default and recommended server is production (neid.ipac.caltech.edu); EXPRES tables exist only there, so leave the default unless you have a specific reason to change it (see production vs test server).

The mental model

One paragraph to carry through the rest of the docs:

Pick an archive (for example NEIDArchive() or EXPRESArchive()) → pick a data level → filter rows with a query_* method → get back an astropy Table → optionally download the FITS files.

Next: Radial velocities and the CCF (what the numbers mean), Data levels (picking a level), and Choosing a query (picking a method).