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Update GALAH DR4: 917,588 stars, 30 elements

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README.md CHANGED
@@ -3,7 +3,7 @@ license: cc-by-4.0
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  pretty_name: "GALAH DR4 — Stellar Abundances for 917k Stars"
4
  language:
5
  - en
6
- description: "The fourth data release of the Galactic Archaeology with HERMES survey radial velocities, stellar parameters, and up to 31 elemental abundances for 917,588 stars."
7
  task_categories:
8
  - tabular-classification
9
  - tabular-regression
@@ -16,6 +16,7 @@ tags:
16
  - astronomy
17
  - open-data
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  - tabular-data
 
19
  size_categories:
20
  - 100K<n<1M
21
  configs:
@@ -28,121 +29,154 @@ configs:
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29
  # GALAH DR4 — Stellar Abundances for 917k Stars
30
 
31
- *Part of the [Astronomy Datasets](https://huggingface.co/collections/juliensimon/astronomy-datasets-69c24caf2f17e36128946743) collection on Hugging Face.*
32
 
33
- The fourth data release of the GALactic Archaeology with HERMES (GALAH) survey,
34
- providing radial velocities, stellar parameters, and up to 31 elemental abundances
35
- for **917,588** stars observed with the HERMES spectrograph on the
36
- Anglo-Australian Telescope.
 
 
 
37
 
38
  ## Dataset description
39
 
40
- GALAH DR4 is one of the largest stellar spectroscopic surveys, designed to unravel
41
- the formation and evolution of the Milky Way through chemical tagging. Each star has
42
- high-resolution spectra decomposed into fundamental stellar parameters and individual
43
- elemental abundances spanning light elements, alpha-elements, iron-peak elements,
44
- and neutron-capture elements.
 
 
45
 
46
- Key properties:
47
- - **906,689** stars with radial velocity measurements
48
- - **906,432** stars with at least one elemental abundance
49
- - **30** elemental abundance columns ([X/Fe])
50
- - Median **24** abundances per star
51
- - Median SNR: **48.1** per pixel
52
 
53
  ## Schema
54
 
55
- | Column | Type | Description |
56
- |--------|------|-------------|
57
- | `sobject_id` | string | GALAH observation identifier |
58
- | `star_id` | string | GALAH unique star identifier |
59
- | `tmass_id` | string | 2MASS identifier |
60
- | `gaiadr3_source_id` | string | Gaia DR3 source identifier |
61
- | `ra` | float64 | Right Ascension J2000 (degrees) |
62
- | `dec` | float64 | Declination J2000 (degrees) |
63
- | `teff_k` | float64 | Effective temperature (K) |
64
- | `logg` | float64 | Surface gravity (log cm/s^2) |
65
- | `fe_h_dex` | float64 | Iron abundance [Fe/H] (dex) |
66
- | `vmic` | float64 | Microturbulence velocity (km/s) |
67
- | `vsini` | float64 | Projected rotational velocity (km/s) |
68
- | `radial_velocity_kms` | float64 | Barycentric radial velocity (km/s) |
69
- | `radial_velocity_comp2_kms` | float64 | Binary companion RV (km/s) |
70
- | `snr_px_ccd1`..`snr_px_ccd4` | float64 | Signal-to-noise per pixel (4 CCDs) |
71
- | `snr_mean` | float64 | Mean SNR across all 4 CCDs |
72
- | `flag_sp` | Int64 | Spectroscopic quality flag (0 = best) |
73
- | `flag_red` | Int64 | Reduction pipeline quality flag |
74
- | `li_fe`..`eu_fe` | float64 | Elemental abundances [X/Fe] (dex) 31 elements |
75
- | `n_abundances` | Int64 | Count of non-null abundance measurements |
76
-
77
- ### Abundance columns
78
-
79
- Light: `li_fe`, `c_fe`, `n_fe`, `o_fe` ·
80
- Odd-Z: `na_fe`, `al_fe`, `k_fe` ·
81
- Alpha: `mg_fe`, `si_fe`, `ca_fe`, `ti_fe` ·
82
- Iron-peak: `sc_fe`, `v_fe`, `cr_fe`, `mn_fe`, `co_fe`, `ni_fe`, `cu_fe`, `zn_fe` ·
83
- s-process: `rb_fe`, `sr_fe`, `y_fe`, `zr_fe`, `mo_fe`, `ba_fe`, `la_fe`, `ce_fe`, `nd_fe` ·
84
- r-process: `ru_fe`, `sm_fe`, `eu_fe`
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
85
 
86
  ## Usage
87
 
88
  ```python
89
  from datasets import load_dataset
 
90
 
91
  ds = load_dataset("juliensimon/galah-dr4-stellar-abundances", split="train")
92
  df = ds.to_pandas()
93
 
94
- # High-quality stars with best spectroscopic flags
95
- best = df[df["flag_sp"] == 0]
96
-
97
- # Metal-poor stars
98
- metal_poor = df[df["fe_h_dex"] < -1.0]
99
 
100
- # Stars rich in europium (r-process)
101
- eu_rich = df[df["eu_fe"] > 0.5]
102
-
103
- # Stars with the most measured abundances
104
- well_measured = df.sort_values("n_abundances", ascending=False).head(1000)
105
-
106
- # Kiel diagram (logg vs Teff)
107
- import matplotlib.pyplot as plt
108
- sample = df[df["flag_sp"] == 0].sample(50000)
109
- plt.scatter(sample["teff_k"], sample["logg"], c=sample["fe_h_dex"],
110
- s=0.1, cmap="coolwarm", vmin=-1.5, vmax=0.5)
111
  plt.gca().invert_xaxis()
112
  plt.gca().invert_yaxis()
113
- plt.xlabel("Teff (K)")
114
- plt.ylabel("log g")
115
- plt.colorbar(label="[Fe/H]")
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
116
  ```
117
 
118
  ## Data source
119
 
120
- [GALAH Survey DR4](https://www.galah-survey.org/dr4/) (Buder et al. 2024).
121
- Observed with the HERMES spectrograph (R ≈ 28,000) on the 3.9m Anglo-Australian
122
- Telescope at Siding Spring Observatory.
123
 
124
  ## Update schedule
125
 
126
- Static dataset — uploaded once from the DR4 release catalog.
127
 
128
  ## Related datasets
129
 
130
- - [gaia-dr3-astrophysical-parameters](https://huggingface.co/datasets/juliensimon/gaia-dr3-astrophysical-parameters) — Gaia DR3 stellar parameters
131
- - [exoplanet-catalog](https://huggingface.co/datasets/juliensimon/exoplanet-catalog) — Confirmed exoplanets
132
- - [pulsar-catalog](https://huggingface.co/datasets/juliensimon/pulsar-catalog) — Pulsar catalog
133
 
134
- ## Pipeline
135
 
136
- Source code: [juliensimon/space-datasets](https://github.com/juliensimon/space-datasets)
137
 
138
  ## Citation
139
 
140
  ```bibtex
141
- @article{buder2024galah,
142
- author = {Buder, Sven and others},
143
- title = {The GALAH Survey: Data Release 4},
144
- year = {2024},
145
- journal = {arXiv preprint arXiv:2409.19858},
 
146
  }
147
  ```
148
 
 
3
  pretty_name: "GALAH DR4 — Stellar Abundances for 917k Stars"
4
  language:
5
  - en
6
+ description: "The fourth data release of the GALactic Archaeology with HERMES (GALAH) survey, providing radial velocities, stellar parameters, and up to 31 elemental abundances for 917,588 stars observed with the H"
7
  task_categories:
8
  - tabular-classification
9
  - tabular-regression
 
16
  - astronomy
17
  - open-data
18
  - tabular-data
19
+ - parquet
20
  size_categories:
21
  - 100K<n<1M
22
  configs:
 
29
 
30
  # GALAH DR4 — Stellar Abundances for 917k Stars
31
 
 
32
 
33
+ <div align="center">
34
+ <img src="banner.jpg" alt="A youthful globular star cluster observed by the Hubble Space Telescope" width="400">
35
+ <p><em>Credit: NASA/ESA/Hubble</em></p>
36
+ </div>
37
+
38
+
39
+ *Part of a [dataset collection](https://huggingface.co/collections/juliensimon/astronomy-datasets-69c24caf2f17e36128946743) on Hugging Face.*
40
 
41
  ## Dataset description
42
 
43
+ The fourth data release of the GALactic Archaeology with HERMES (GALAH) survey, providing radial velocities, stellar parameters, and up to 31 elemental abundances for 917,588 stars observed with the HERMES spectrograph on the Anglo-Australian Telescope.
44
+
45
+ GALAH DR4 is one of the largest stellar spectroscopic surveys, designed to unravel the formation and evolution of the Milky Way through chemical tagging. Each star has high-resolution spectra decomposed into fundamental stellar parameters and individual elemental abundances spanning light elements, alpha-elements, iron-peak elements, and neutron-capture elements.
46
+
47
+ GALAH was specifically designed for chemical tagging — the idea that stars born in the same molecular cloud retain a unique multi-dimensional chemical fingerprint that persists long after the birth cluster has dispersed. The HERMES spectrograph delivers four non-contiguous optical wavelength channels at R ~ 28,000, capturing lines of light elements (Li, C, N, O), alpha-elements (Mg, Si, Ca, Ti), iron-peak elements (Sc, V, Cr, Mn, Fe, Co, Ni, Cu, Zn), and neutron-capture elements (Rb, Sr, Y, Zr, Mo, Ba, La, Ce, Nd, Ru, Sm, Eu) — up to 31 distinct abundance dimensions per star.
48
+
49
+ DR4 represents a major advance over DR3, incorporating improved spectral analysis techniques, better treatment of non-LTE effects for critical elements, and cross-matching with Gaia DR3 for precise astrometric information. The inclusion of both s-process elements (Ba, La, Ce from AGB nucleosynthesis) and r-process elements (Eu from neutron star mergers) makes GALAH uniquely powerful for constraining the sites and timescales of heavy element production in the Milky Way.
50
 
 
 
 
 
 
 
51
 
52
  ## Schema
53
 
54
+ | Column | Type | Description | Sample | Null % |
55
+ |--------|------|-------------|--------|--------|
56
+ | `sobject_id` | object | GALAH spectroscopic observation identifier (unique per exposure); format encodes field and fiber number | 170910004101274 | 0.0% |
57
+ | `tmass_id` | object | 2MASS photometric catalog cross-identifier (e.g. 'J12345678+1234567'); null if no 2MASS match | 00000011+0522500 | 0.0% |
58
+ | `gaiadr3_source_id` | object | Gaia DR3 astrometric source identifier; enables cross-match for precise positions, proper motions, and parallaxes; null if unmatched | 2745049530295263232 | 0.0% |
59
+ | `ra` | float64 | Right ascension, ICRS J2000.0, in decimal degrees (0-360) | 0.0005000000237487257 | 0.0% |
60
+ | `dec` | float64 | Declination, ICRS J2000.0, in decimal degrees (-90 to +90) | 5.380555629730225 | 0.0% |
61
+ | `teff_k` | float32 | Effective temperature in Kelvin from spectral synthesis; GALAH targets FGK stars, typical range 4000-7500 K; uncertainty ~100 K; null if spectral pipeline failed (flag_sp > 0) | 4486.2554 | 1.2% |
62
+ | `logg` | float32 | Log surface gravity in cgs (log cm/s²); main sequence dwarfs: 4.0-5.0, subgiants: 3.5-4.5, red giants: 1.5-3.5; null if flag_sp > 0 | 4.657445 | 1.2% |
63
+ | `fe_h_dex` | float32 | [Fe/H] iron abundance in dex relative to solar; GALAH surveys -2.5 to +0.5 dex; typical uncertainty ~0.1 dex; null if flag_sp > 0 | -0.3963167 | 1.2% |
64
+ | `vmic` | float32 | Microturbulence velocity in km/s; internal parameter of the spectral model capturing small-scale turbulent broadening; typical range 0.5-2.0 km/s | 0.7832511 | 1.2% |
65
+ | `vsini` | float32 | Projected rotational velocity v sin i in km/s; slow rotators (FGK dwarfs) typically < 10 km/s; null for stars where rotation is unresolved at R~28,000 | 6.9111066 | 1.2% |
66
+ | `radial_velocity_kms` | float32 | Barycentric radial velocity in km/s from cross-correlation; precision ~0.1 km/s; null for very low S/N spectra | 10.483474 | 1.2% |
67
+ | `radial_velocity_comp2_kms` | float32 | Barycentric radial velocity of a detected binary companion in km/s; non-null only for double-lined spectroscopic binaries (SB2) | 186.25742 | 96.5% |
68
+ | `snr_px_ccd1` | float32 | Signal-to-noise ratio per pixel for HERMES CCD 1 (blue channel, ~4713-4903 Å); drives which light-element abundances can be measured | 26.964375 | 1.9% |
69
+ | `snr_px_ccd2` | float32 | Signal-to-noise ratio per pixel for HERMES CCD 2 (green channel, ~5648-5873 Å); drives which iron-peak abundances can be measured | 45.697865 | 0.0% |
70
+ | `snr_px_ccd3` | float32 | Signal-to-noise ratio per pixel for HERMES CCD 3 (red channel, ~6478-6737 Å); drives which alpha-element abundances can be measured | 75.889725 | 0.6% |
71
+ | `snr_px_ccd4` | float32 | Signal-to-noise ratio per pixel for HERMES CCD 4 (IR channel, ~7585-7887 Å); drives which neutron-capture abundances can be measured | 72.90598 | 2.9% |
72
+ | `flag_sp` | Int64 | Spectroscopic analysis quality flag; 0 = good stellar parameters; >0 encodes specific problems (binary contamination, emission, grid edge); use flag_sp == 0 for clean samples | 0 | 0.0% |
73
+ | `flag_red` | Int64 | Reduction pipeline quality flag; 0 = successful reduction; >0 indicates issues with sky subtraction, cross-talk, or cosmic rays | 0 | 0.0% |
74
+ | `c_fe` | float32 | [C/Fe] carbon abundance ratio in dex; elevated in carbon-enhanced metal-poor (CEMP) stars | 0.05938914 | 1.4% |
75
+ | `n_fe` | float32 | [N/Fe] nitrogen abundance ratio in dex; a tracer of CNO cycling and AGB dredge-up | -0.3913465 | 24.8% |
76
+ | `o_fe` | float32 | [O/Fe] oxygen abundance ratio in dex; key alpha-element tracing core-collapse supernova enrichment | -0.19825687 | 4.7% |
77
+ | `na_fe` | float32 | [Na/Fe] sodium abundance ratio in dex; anti-correlates with O in globular cluster stars | -0.16347441 | 2.6% |
78
+ | `al_fe` | float32 | [Al/Fe] aluminium abundance ratio in dex; traces Mg-Al chain proton captures in massive stars | -0.048628658 | 7.4% |
79
+ | `k_fe` | float32 | [K/Fe] potassium abundance ratio in dex; sensitive to non-LTE effects; limited by spectral coverage | -0.03559644 | 6.2% |
80
+ | `mg_fe` | float32 | [Mg/Fe] magnesium abundance ratio in dex; primary alpha-element; high in old, metal-poor disk stars; decreases with increasing [Fe/H] due to Type Ia SNe iron contribution | 0.3087402 | 2.1% |
81
+ | `si_fe` | float32 | [Si/Fe] silicon abundance ratio in dex; alpha-element; co-produced with Mg in core-collapse supernovae | 0.19373856 | 3.5% |
82
+ | `ca_fe` | float32 | [Ca/Fe] calcium abundance ratio in dex; alpha-element; traces both core-collapse and Type Ia supernova nucleosynthesis | 0.12320362 | 1.9% |
83
+ | `ti_fe` | float32 | [Ti/Fe] titanium abundance ratio in dex; odd alpha-element; useful for separating thin disk, thick disk, and halo populations | 0.3395407 | 1.6% |
84
+ | `sc_fe` | float32 | [Sc/Fe] scandium abundance ratio in dex; iron-peak element; produced mainly in core-collapse supernovae | 0.01370742 | 2.0% |
85
+ | `v_fe` | float32 | [V/Fe] vanadium abundance ratio in dex; iron-peak element; constrains explosive nucleosynthesis models | 0.32754487 | 10.6% |
86
+ | `cr_fe` | float32 | [Cr/Fe] chromium abundance ratio in dex; iron-peak element with known non-LTE corrections required | 0.1857635 | 1.6% |
87
+ | `mn_fe` | float32 | [Mn/Fe] manganese abundance ratio in dex; traces Type Ia supernova contribution (Mn is overproduced in Chandrasekhar-mass SNe Ia) | -0.023918502 | 1.9% |
88
+ | `co_fe` | float32 | [Co/Fe] cobalt abundance ratio in dex; iron-peak element sensitive to neutron excess in the explosive burning region | 0.13685165 | 8.6% |
89
+ | `ni_fe` | float32 | [Ni/Fe] nickel abundance ratio in dex; closely follows Fe; used to distinguish thick-disk from halo stars | -0.123810135 | 1.7% |
90
+ | `cu_fe` | float32 | [Cu/Fe] copper abundance ratio in dex; iron-peak element with significant s-process contribution | 0.16626377 | 9.5% |
91
+ | `zn_fe` | float32 | [Zn/Fe] zinc abundance ratio in dex; bridges iron-peak and neutron-capture elements; useful metallicity probe | 0.087473795 | 4.9% |
92
+ | `rb_fe` | float32 | [Rb/Fe] rubidium abundance ratio in dex; s-process element; traces AGB stellar nucleosynthesis | -0.008337121 | 66.0% |
93
+ | `sr_fe` | float32 | [Sr/Fe] strontium abundance ratio in dex; light s-process element; also has r-process and charged-particle process contributions | 0.035072897 | 69.8% |
94
+ | `y_fe` | float32 | [Y/Fe] yttrium abundance ratio in dex; s-process element with Ba/Y ratio used to age-date stellar populations | 0.11842539 | 1.7% |
95
+ | `zr_fe` | float32 | [Zr/Fe] zirconium abundance ratio in dex; s-process element co-produced with Y and Sr | 0.11218737 | 29.0% |
96
+ | `mo_fe` | float32 | [Mo/Fe] molybdenum abundance ratio in dex; neutron-capture element with both s- and r-process origin | -0.14827245 | 83.9% |
97
+ | `ba_fe` | float32 | [Ba/Fe] barium abundance ratio in dex; dominant s-process tracer; high in AGB-enriched stars and young thin-disk stars | -0.090060145 | 2.0% |
98
+ | `la_fe` | float32 | [La/Fe] lanthanum abundance ratio in dex; s-process element; La/Eu ratio distinguishes s- from r-process enrichment | 0.061767366 | 26.8% |
99
+ | `ce_fe` | float32 | [Ce/Fe] cerium abundance ratio in dex; s-process element produced in low-mass AGB stars | -0.025505573 | 28.7% |
100
+ | `nd_fe` | float32 | [Nd/Fe] neodymium abundance ratio in dex; mixed s- and r-process origin | -0.1502057 | 12.2% |
101
+ | `ru_fe` | float32 | [Ru/Fe] ruthenium abundance ratio in dex; primarily r-process origin; rare to measure in stellar spectra | -0.30789047 | 76.8% |
102
+ | `sm_fe` | float32 | [Sm/Fe] samarium abundance ratio in dex; r-process dominated element; traces neutron star merger enrichment | -0.017710395 | 49.8% |
103
+ | `eu_fe` | float32 | [Eu/Fe] europium abundance ratio in dex; the cleanest r-process tracer; high in metal-poor halo stars; r-process enrichment from neutron star mergers | -0.08262927 | 69.8% |
104
+ | `n_abundances` | Int64 | Count of non-null [X/Fe] abundance measurements for this star; ranges 0-31; derived column useful for selecting well-characterised stars | 27 | 0.0% |
105
+ | `snr_mean` | float32 | Mean S/N per pixel averaged across all four HERMES CCDs; derived column; stars with snr_mean < 30 have fewer reliable abundance measurements | 55.364487 | 0.0% |
106
+
107
+ ## Quick stats
108
+
109
+ - **917,588** stars observed with HERMES spectrograph
110
+ - **906,689** stars with radial velocity measurements
111
+ - **906,432** stars with at least one elemental abundance (99%)
112
+ - **30** elemental abundance columns ([X/Fe]), median **24** per star
113
+ - **663,075** stars with clean spectroscopic flags (flag_sp == 0)
114
+ - Median SNR across 4 HERMES CCDs: **48.1** per pixel
115
 
116
  ## Usage
117
 
118
  ```python
119
  from datasets import load_dataset
120
+ import matplotlib.pyplot as plt
121
 
122
  ds = load_dataset("juliensimon/galah-dr4-stellar-abundances", split="train")
123
  df = ds.to_pandas()
124
 
125
+ # Kiel diagram (Teff vs logg) coloured by [Fe/H] — shows stellar populations
126
+ best = df[(df["flag_sp"] == 0) & df["teff_k"].notna() & df["logg"].notna()]
127
+ sample = best.sample(min(50_000, len(best)), random_state=42)
 
 
128
 
129
+ sc = plt.scatter(sample["teff_k"], sample["logg"],
130
+ c=sample["fe_h_dex"], s=0.1, cmap="coolwarm",
131
+ vmin=-1.5, vmax=0.5, alpha=0.6)
 
 
 
 
 
 
 
 
132
  plt.gca().invert_xaxis()
133
  plt.gca().invert_yaxis()
134
+ plt.xlabel("Effective Temperature (K)")
135
+ plt.ylabel("log g (dex)")
136
+ plt.title("GALAH DR4 Kiel Diagram")
137
+ plt.colorbar(sc, label="[Fe/H] (dex)")
138
+ plt.tight_layout()
139
+ plt.show()
140
+
141
+ # Abundance pattern: alpha-element enhancement vs metallicity
142
+ alpha_cols = ["mg_fe", "si_fe", "ca_fe", "ti_fe"]
143
+ best["alpha_fe"] = best[alpha_cols].mean(axis=1)
144
+ sub = best.dropna(subset=["fe_h_dex", "alpha_fe"]).sample(30_000, random_state=0)
145
+ plt.figure()
146
+ plt.scatter(sub["fe_h_dex"], sub["alpha_fe"], s=0.1, alpha=0.3, c="steelblue")
147
+ plt.axhline(0, color="gray", lw=0.5, ls="--")
148
+ plt.xlabel("[Fe/H] (dex)")
149
+ plt.ylabel("[alpha/Fe] (dex)")
150
+ plt.title("Alpha-element Enhancement vs Metallicity")
151
+ plt.tight_layout()
152
+ plt.show()
153
  ```
154
 
155
  ## Data source
156
 
157
+ https://www.galah-survey.org/dr4/
 
 
158
 
159
  ## Update schedule
160
 
161
+ Static dataset — uploaded once from the DR4 release catalog
162
 
163
  ## Related datasets
164
 
165
+ - [juliensimon/apogee-dr17-stellar-abundances](https://huggingface.co/datasets/juliensimon/apogee-dr17-stellar-abundances)
 
 
166
 
167
+ - [juliensimon/hipparcos-catalog](https://huggingface.co/datasets/juliensimon/hipparcos-catalog)
168
 
169
+ - [juliensimon/pulsar-catalog](https://huggingface.co/datasets/juliensimon/pulsar-catalog)
170
 
171
  ## Citation
172
 
173
  ```bibtex
174
+ @dataset{galah_dr4_stellar_abundances,
175
+ title = {GALAH DR4 Stellar Abundances for 917k Stars},
176
+ author = {juliensimon},
177
+ year = {2026},
178
+ url = {https://huggingface.co/datasets/juliensimon/galah-dr4-stellar-abundances},
179
+ publisher = {Hugging Face}
180
  }
181
  ```
182
 
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