juliensimon/kepler-observations

Kepler Observation Catalog

Credit: NASA/Ames/JPL-Caltech

Part of a dataset collection on Hugging Face.

Dataset description

The Kepler Observation Catalog indexes every target observed by NASA's Kepler space telescope during its prime mission (2009–2013), drawn from the Mikulski Archive for Space Telescopes (MAST). Kepler is the most successful exoplanet-hunting mission in history: by continuously monitoring ~200,000 stars in a single 100-square-degree field of view in Cygnus–Lyra, it discovered the majority of confirmed exoplanets through high-precision photometric transit detection, including the first Earth-sized planets in habitable zones.

Each row in this catalog is one Kepler target — identified by its 9-digit Kepler Input Catalog (KIC) ID — with the cadence at which it was observed (long cadence = 29.4-minute integration, capable of catching transits on weeks-to-months orbital periods; short cadence = 58.9-second integration, used for asteroseismology and short-period transits), the pointing (RA/Dec), and a 17-character bitmask indicating which of Kepler's 17 quarterly observing periods contain data for that target. The quarters_observed column summarises the mask as an integer count — a target observed in all 17 quarters has the longest, most exoplanet-favourable light curve in the archive.

This dataset is designed for cross-matching with other exoplanet catalogs (Kepler confirmed planets, TESS TOI, Gaia DR3), for selecting targets with long baselines for long-period planet searches, and for understanding the Kepler field's completeness. It complements the Kepler eclipsing binary and transit timing variation catalogs already in this collection by providing the full target list. Each target's raw and de-trended light curves can be retrieved from MAST using the obs_id.

The catalog is derived from MAST's CAOM table dbo.caomobservation (collection = 'KEPLER'). The K2 extended mission (2014–2018) uses a different observation-id schema and is published as a separate dataset (planned). The Kepler prime-mission archive is static, so this dataset is refreshed quarterly to pick up any late-stage reprocessing.

This dataset is suitable for tabular classification tasks.

Schema

Column	Type	Description	Sample	Null %
obs_id	string	MAST observation identifier (e.g., 'kplr000757076_lc_Q111111111111111111'); encodes KIC ID, cadence (lc=long, sc=short), and Q-flags for each of the 17 Kepler quarters (1=observed, 0=not). Primary key.	kplr000757076_lc_Q111111111111111111	0.0%
intent	string	Observation intent: 'science' (target star monitoring) or 'calibration'	science	0.0%
target_ra	float64	Target right ascension in decimal degrees (ICRS). Kepler observed a fixed ~100 sq. deg. field near RA 290°, Dec 45° in Cygnus-Lyra.	291.03872	0.0%
target_dec	float64	Target declination in decimal degrees (ICRS)	36.59813	0.0%
kic_id	Int64	Kepler Input Catalog identifier (9-digit integer) for the target star; shared with the NASA Exoplanet Archive	757076	0.8%
cadence	string	Cadence type: 'lc' (long cadence, 29.4-minute integration) or 'sc' (short cadence, 58.9-second integration)	lc	0.8%
quarters_mask	string	17-character string of '1'/'0' flags marking which Kepler quarters contain data for this target (Q1–Q17, in order)	111111111111111111	0.8%
quarters_observed	Int64	Number of Kepler quarters (out of 17) in which the target was observed; higher = longer light-curve baseline	18	0.0%

Quick stats

• 212,993 Kepler prime-mission observations (2009–2013)
• 207,656 long cadence (29.4 min), 3,724 short cadence (58.9 s)
• 82,915 targets observed in all 17 Kepler quarters (maximum baseline)
• 207,656 distinct Kepler Input Catalog (KIC) targets

Usage

from datasets import load_dataset

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

from datasets import load_dataset

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

# Targets with longest baseline (all 17 quarters)
full_baseline = df[(df["quarters_observed"] == 17) & (df["cadence"] == "lc")]
print(f"Targets observed across the full Kepler prime mission: {len(full_baseline):,}")

# Map of Kepler field
import matplotlib.pyplot as plt
sample = df.sample(min(50000, len(df)))
plt.figure(figsize=(10, 8))
plt.scatter(sample["target_ra"], sample["target_dec"], s=0.2, alpha=0.3)
plt.xlabel("RA (deg)"); plt.ylabel("Dec (deg)")
plt.title("Kepler prime-mission field of view (50K sample)")
plt.gca().invert_xaxis()
plt.show()

# Cadence distribution per quarter
import numpy as np
mask_chars = np.array([list(m) for m in df["quarters_mask"].fillna("0" * 17)])
per_quarter = (mask_chars == "1").sum(axis=0)
plt.bar(range(1, 18), per_quarter)
plt.xlabel("Kepler quarter"); plt.ylabel("Targets observed")
plt.title("Kepler target count per quarter")
plt.show()

Data source

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

Update schedule

Quarterly (1st of Jan/Apr/Jul/Oct at 14:00 UTC) via GitHub Actions.

Related datasets

• juliensimon/kepler-eclipsing-binaries

• juliensimon/kepler-transit-timing

• juliensimon/nasa-exoplanets

• juliensimon/tess-toi-candidates

• juliensimon/hst-observations

• juliensimon/jwst-observations

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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{kepler_observations,
  title = {Kepler Observation Catalog},
  author = {juliensimon},
  year = {2026},
  url = {https://huggingface.co/datasets/juliensimon/kepler-observations},
  publisher = {Hugging Face}
}

License

CC-BY-4.0