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Column 1
float64
0.6
4.75
Column 2
float64
2.11
3.45
Column 3
float64
0.05
0.78
Column 4
stringlengths
18
33
0.6
2.685
0.054
TRIS (open square)
0.82
2.772
0.266
TRIS (open square)
1.28
3.45
0.78
Raghunathan & Subrahmanyan (2000)
1.41
2.11
0.38
Levin et al. (1988)
1.425
2.65
0.33
Staggs & al. (1996)
1.47
2.26
0.19
Bensadoun et al. (1993)
2
2.55
0.14
Bersanelli et al. (1994)
2.5
2.62
0.25
Sironi et al. (1984)
2.5
2.518
0.395
TRIS (open square)
2.5
2.79
0.15
Sironi & Bonelli (1986)
2.5
2.5
0.25
Sironi et al. (1991)
3.7
2.59
0.13
De Amici et al. (1988)
3.8
2.56
0.08
De Amici et al. (1990)
3.8
2.71
0.07
De Amici et al. (1990)
3.8
2.64
0.07
De Amici et al. (1991)
4.75
2.71
0.2
Mandolesi et al. (1984)
4.75
2.7
0.07
Mandolesi et al. (1986)

TRIS low-frequency CMB temperature compilation

TRIS page CMB-temperature compilation

Regenerate the preview from the published Parquet data with python tools/render_hub_preview.py tris datasets/tris/preview.png.

This dataset contains the 17-row CMB-temperature compilation served by LAMBDA on its TRIS experiment page. Three rows are TRIS measurements, marked TRIS (open square) by the source. The remaining fourteen rows retain the literature attributions written in the source table.

How to use

python -m venv .venv && .venv/bin/pip install datasets huggingface_hub pyarrow
from datasets import load_dataset
dataset = load_dataset("astro-legacy-archive/tris", "T_CMB-vs-Freq_CAPTION", split="train[:1]", columns=["Column 1", "Column 2", "Column 3", "Column 4"])
row = dataset[0]
print(row["Column 1"], row["Column 2"], row["Column 3"], row["Column 4"])
0.6 2.685 0.054 TRIS (open square)
import json
from huggingface_hub import hf_hub_download
import pyarrow.parquet as pq
path = hf_hub_download("astro-legacy-archive/tris", "T_CMB-vs-Freq_CAPTION.parquet", repo_type="dataset")
print(json.loads(pq.read_schema(path).metadata[b"statistical_product"])["schema"])
astro-legacy-archive/statistical-product

Dataset structure

The source has no column header, so its four logical fields use the numbered labels Column 1 through Column 4.

source column type unit meaning
Column 1 float64 GHz Observing frequency
Column 2 float64 K CMB temperature
Column 3 float64 K Published plus-or-minus temperature uncertainty
Column 4 string Measurement attribution as written in the source row

Rows remain in source order. Column 4 preserves the complete attribution, including punctuation and the source's TRIS (open square) marker. No experiment code or TRIS-selection flag is added.

Provenance

LAMBDA supplies the compilation from the TRIS experiment page. The source file, T_CMB-vs-Freq_CAPTION.txt, is 1,034 bytes with SHA-256 2b526b1d466393d5e851caec9257c25182ee070972ab81650caaec9b3c99b691.

The first three source lines describe the compilation and state that TRIS data are marked as open squares.

Considerations for use

  • This is the comparison compilation served on the TRIS page, not a table containing only TRIS measurements.
  • The measurements come from multiple experiments and publications; use Column 4 to retain that provenance when selecting or comparing rows.
  • The source supplies symmetric plus-or-minus values in Column 3. Consult the cited publications for experiment-specific error models.

Licensing and credit

HEASARC states verbatim:

HEASARC materials are all available freely for your use.

This is a free-use statement rather than a named formal licence.

No explicit licence or requested credit line is published on the checked TRIS page.

Citation

The TRIS experiment and its temperature results are described by Zannoni et al. (2008), The Astrophysical Journal, 688, 12–23, doi:10.1086/592133, arXiv:0806.1415.

Errata and requests

To report an erratum, propose a correction, or ask a question about this dataset, open a Discussion or Pull Request in its Community tab; posting requires a free Hugging Face account. Dataset requests and archive-wide questions may be posted in the README space's Community tab or the Community tab of any dataset. Confirmed corrections are made in the source conversion tools and published by rebuilding the dataset.

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