juliensimon/k2-observations

K2 Observation Catalog

Credit: NASA/Ames/JPL-Caltech

Part of a dataset collection on Hugging Face.

Dataset description

The K2 Observation Catalog indexes every target observed by NASA's K2 mission — the repurposed Kepler spacecraft that flew between 2014 and 2018 after two of Kepler's four reaction wheels failed. Unable to hold its original Cygnus–Lyra field, K2 used solar radiation pressure as a "virtual third reaction wheel," rolling every ~80 days to observe a new ecliptic-plane field ("campaign"). 20 campaigns (C0–C19) were executed before the spacecraft exhausted its hydrazine fuel.

Each row is one K2 target observation. The dataset is drawn from MAST's dbo.caomobservation (collection = 'K2') and merges four observationid flavors that K2 uses to distinguish target-selection programs: ktwo (624K rows — the main per-target list from Guest Observer proposals), polar (121K rows — alternate-roll pointings that give slightly different sky coverage in the same campaign), k2gap (Kepler/K2 Galactic Archaeology Program — asteroseismology and stellar-population targets), and k2-c<NN> (20 rows of campaign-composite metadata). Each obs_id is parsed into its EPIC ID (the K2 equivalent of KIC), campaign number, and cadence (long or short).

K2 is the second-most prolific transit-discovery mission after Kepler prime, finding over 500 confirmed exoplanets across unusual hosts — M dwarfs, young stars in clusters, bright and easily-followed nearby systems — that the original Kepler field could not reach. It also produced rich stellar variability, asteroseismology, and solar-system (moving-target) archives.

This dataset is designed for cross-matching with exoplanet catalogs (K2 confirmed planets, K2 candidates, TESS TOI), for campaign-by-campaign target lists, for identifying long-baseline targets observed across multiple campaigns, and for selecting stellar-population samples via the k2gap prefix. It complements the Kepler prime-mission observations dataset in this collection. Raw and de-trended light curves for each target can be retrieved from MAST using the obs_id.

The catalog is derived from MAST's CAOM table dbo.caomobservation and is refreshed quarterly — K2 operations ended in 2018 but reprocessing (e.g., EVEREST, K2SFF detrended products) continues to land in the archive.

This dataset is suitable for tabular classification tasks.

Schema

Column	Type	Description	Sample	Null %
obs_id	string	MAST observation identifier (e.g., 'ktwo201121245-c01_lc'); encodes target type, EPIC ID, campaign, and cadence. Primary key.	k2-c00	0.0%
obstype	string	CAOM observation type code: 'S' (simple) or 'C' (composite)	C	0.0%
intent	string	Observation intent: 'science' or 'calibration'	science	0.0%
target_ra	float64	Target right ascension in decimal degrees (ICRS). Each K2 campaign covers roughly 100 sq. deg. along the ecliptic.	97.88560925269856	0.0%
target_dec	float64	Target declination in decimal degrees (ICRS). All K2 campaigns fall within ±30° of the ecliptic plane.	21.86601254301791	0.0%
target_type	string	Category inferred from obs_id prefix: 'standard' (ktwo, main K2 target list), 'polar' (alternate-roll pointings with different sky coverage), 'k2gap' (Kepler/K2 Galactic Archaeology Program — asteroseismology / stellar populations), or 'campaign' (20 campaign-metadata rows)	campaign	0.0%
epic_id	Int64	Ecliptic Plane Input Catalog (EPIC) identifier (9-digit integer) for the target; null for campaign-composite rows	201121245	2.5%
campaign	Int64	K2 campaign number (0–19); each campaign observed a different patch of the ecliptic for ~80 days. Null for some campaign-composite rows.	0	2.5%
cadence	string	Cadence type: 'lc' (long cadence, 29.4-minute integration) or 'sc' (short cadence, 58.9-second integration). Null for campaign composites.	lc	2.5%

Quick stats

• 764,820 K2 observations (2014–2018, campaigns C0–C19)
• Target types: standard (624,135), polar (120,641), unknown (18,762), k2gap (1,262), campaign (20)
• 24 distinct campaigns
• 743,405 long cadence (29.4 min), 2,633 short cadence (58.9 s)
• 562,900 distinct EPIC (Ecliptic Plane Input Catalog) targets

Usage

from datasets import load_dataset

ds = load_dataset("juliensimon/k2-observations", split="train")
df = ds.to_pandas()

from datasets import load_dataset

ds = load_dataset("juliensimon/k2-observations", split="train")
df = ds.to_pandas()

# Standard K2 targets observed across ≥5 campaigns (long baseline)
import pandas as pd
std = df[df["target_type"] == "standard"]
multi_campaign = std.groupby("epic_id").size()
long_baseline = multi_campaign[multi_campaign >= 5]
print(f"EPIC targets observed in ≥5 campaigns: {len(long_baseline):,}")

# K2 sky coverage (each campaign = different patch of ecliptic)
import matplotlib.pyplot as plt
sci = df[df["target_type"].isin(["standard", "polar", "k2gap"])]
plt.figure(figsize=(12, 6))
for c in sorted(sci["campaign"].dropna().unique()):
    subset = sci[sci["campaign"] == c]
    plt.scatter(subset["target_ra"], subset["target_dec"], s=0.2, alpha=0.3, label=f"C{int(c)}")
plt.xlabel("RA (deg)"); plt.ylabel("Dec (deg)")
plt.gca().invert_xaxis()
plt.title("K2 campaigns on the sky")
plt.legend(loc="upper right", fontsize=6, ncol=2)
plt.show()

# Targets per campaign
per_c = sci.groupby("campaign").size()
per_c.plot.bar()
plt.xlabel("Campaign"); plt.ylabel("Target observations")
plt.title("K2 target count per campaign")
plt.show()

Data source

https://archive.stsci.edu/missions-and-data/k2

Update schedule

Quarterly (1st of Jan/Apr/Jul/Oct at 15:00 UTC) via GitHub Actions.

Related datasets

• juliensimon/kepler-observations

• juliensimon/kepler-eclipsing-binaries

• juliensimon/kepler-transit-timing

• juliensimon/nasa-exoplanets

• juliensimon/tess-toi-candidates

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About the author

Created by Julien Simon — AI Operating Partner at Fortino Capital. Part of the Space Datasets collection.

Citation

@dataset{k2_observations,
  title = {K2 Observation Catalog},
  author = {juliensimon},
  year = {2026},
  url = {https://huggingface.co/datasets/juliensimon/k2-observations},
  publisher = {Hugging Face}
}

License

CC-BY-4.0