Datasets:
license: cc-by-4.0
pretty_name: Planck Second Sunyaev-Zeldovich Source Catalog (PSZ2)
language:
- en
description: >-
Galaxy clusters detected via the Sunyaev-Zeldovich effect by ESA Planck, with
redshifts, masses, and integrated Compton parameters
task_categories:
- tabular-classification
tags:
- space
- planck
- sunyaev-zeldovich
- galaxy-cluster
- cmb
- esa
- cosmology
- astronomy
- open-data
- tabular-data
- parquet
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: train
path: data/planck-sz2.parquet
default: true
Planck Second Sunyaev-Zeldovich Source Catalog (PSZ2)
Credit: NASA/ESA/STScI
Part of the Astronomy Datasets and Galaxies & Cosmology collections on Hugging Face.
Complete catalog of galaxy clusters detected via the thermal Sunyaev-Zeldovich (SZ) effect by the ESA Planck satellite, sourced from NASA HEASARC. Currently 1,655 galaxy clusters (1,094 confirmed with redshifts, 561 candidates).
Dataset description
The Sunyaev-Zeldovich (SZ) effect is the inverse Compton scattering of cosmic microwave background (CMB) photons by the hot intracluster medium (ICM) of galaxy clusters. As CMB photons pass through the ICM (electron temperatures of 10^7-10^8 K), they receive a characteristic energy boost that produces a spectral distortion observable at millimeter wavelengths: a decrement below ~217 GHz and an increment above. This effect is unique in cosmology because its surface brightness is redshift-independent, making it an extraordinarily powerful tool for detecting massive clusters at any distance.
The Planck satellite's all-sky survey at nine frequencies (30-857 GHz) provided the first uniform all-sky SZ cluster catalog. The PSZ2 catalog represents the largest SZ-selected sample of galaxy clusters, detected using three independent methods: two implementations of matched multi-frequency filters (MMF1 and MMF3) and PowellSnakes (PwS), a Bayesian detection algorithm. Each cluster's integrated Compton parameter Y5R500 quantifies the total thermal energy of the ICM and serves as a low-scatter mass proxy through the Y-M scaling relation.
These SZ-selected clusters are essential for constraining cosmological parameters (Omega_m, sigma_8), calibrating the cluster mass function, understanding large-scale structure formation, and cross-matching with optical, X-ray, and gravitational lensing surveys.
Schema
| Column | Type | Description |
|---|---|---|
__row |
string | |
source_number |
string | |
name |
string | PSZ2 catalog designation |
ra |
float | Right ascension (J2000, degrees) |
dec |
float | Declination (J2000, degrees) |
lii |
float | Galactic longitude (degrees) |
bii |
float | Galactic latitude (degrees) |
error_radius |
string | |
snr |
float | Signal-to-noise ratio of the SZ detection |
ref_pipeline_code |
string | |
det_pipeline_codes |
string | |
pccs2_match |
string | |
psz1_match |
string | |
ir_contam_flag |
string | |
nn_quality_flag |
string | |
y5r500 |
float | Integrated Compton parameter Y_5R500 (arcmin^2) |
y5r500_error |
string | |
validation_status |
string | |
redshift |
float | Spectroscopic or photometric redshift |
redshift_source_name |
string | |
mass_sz |
string | |
mass_sz_pos_err |
string | |
mass_sz_neg_err |
string | |
mcxc_name |
string | |
redmapper_name |
string | |
act_name |
string | |
spt_name |
string | |
wise_flag |
string | |
ami_det_significance |
string | |
cosmology_sample_flag |
string | |
source_note |
string | |
__x_ra_dec |
string | |
__y_ra_dec |
string | |
__z_ra_dec |
string | |
is_confirmed |
bool | Has a measured redshift (derived column) |
Quick stats
- 1,655 galaxy clusters detected via the SZ effect
- 1,094 confirmed with measured redshifts (median z = 0.224)
- Highest SNR: PSZ2 G075.71+13.51 (SNR = 49.0)
- Median SNR: 5.6, Max SNR: 49.0
Usage
from datasets import load_dataset
ds = load_dataset("juliensimon/planck-sz2-clusters", split="train")
df = ds.to_pandas()
# Confirmed clusters with redshifts
confirmed = df[df["is_confirmed"]]
print(f"{len(confirmed):,} clusters with measured redshifts")
# Highest SNR detections
top = df.nlargest(10, "snr")[["name", "snr", "redshift", "msz"]]
# Redshift distribution
import matplotlib.pyplot as plt
df["redshift"].dropna().hist(bins=50)
plt.xlabel("Redshift")
plt.ylabel("Count")
plt.title("Planck SZ2 Cluster Redshift Distribution")
Data source
All data comes from the Planck PSZ2 Catalog hosted by NASA's High Energy Astrophysics Science Archive Research Center (HEASARC), accessed via the TAP protocol. The original catalog was published by the Planck Collaboration (Planck Collaboration XXVII, 2016, A&A, 594, A27).
Update schedule
Semi-annual on January 1st and July 1st at 07:00 UTC via GitHub Actions.
Related datasets
- galaxy-clusters — Multi-wavelength galaxy cluster catalog
- desi-dr1-redshifts — DESI DR1 spectroscopic redshifts
- pantheon-plus-sne-ia — Pantheon+ Type Ia supernovae for cosmology
Pipeline
Source code: juliensimon/space-datasets
Support
If you find this dataset useful, please give it a ❤️ on the dataset page and share feedback in the Community tab! Also consider giving a ⭐️ to the space-datasets repo.
Citation
@dataset{planck_sz2_clusters,
author = {Simon, Julien},
title = {Planck Second Sunyaev-Zeldovich Source Catalog (PSZ2)},
year = {2026},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/juliensimon/planck-sz2-clusters},
note = {Based on Planck Collaboration XXVII (2016) data via NASA HEASARC}
}