the Palomar Spectroscopic Survey of Nearby Galaxies

This week we’re exploring spectroscopic observations of 486 nearby galaxies from the Palomar Spectroscopic Survey. In the 1990s, astronomers used the 200-inch Hale Telescope at Palomar Observatory — once the world’s largest — to split the light from the centers of nearly 500 nearby galaxies into a rainbow of wavelengths. By measuring the strength of specific emission lines in those spectra, they classified each galaxy’s nucleus as powered by young stars, by an active galactic nucleus (AGN), or by some combination of both.

The dataset comes from a landmark series of papers by Ho, Filippenko & Sargent that quantified the demographics of nuclear activity in the local universe. Their key finding: roughly 43% of nearby galaxies show spectroscopic signatures of AGN activity, making “active” nuclei far more common than previously recognized. A more recent census of over 8,000 galaxies using similar techniques (Harvard CfA, January 2025) continues to refine these detection rates, confirming that the spectral classification approach pioneered in the Palomar survey remains foundational to the field.

We use the sample of emission-line nuclei derived from a recently completed optical spectroscopic survey of nearby galaxies to quantify the incidence of local (z = 0) nuclear activity. […] Half of the objects can be classified as H II or star-forming nuclei and the other half as some form of AGN, of which we distinguish three classes — Seyfert nuclei, LINERs, and transition objects.

What types of nuclear activity are most common, and how does that vary with galaxy morphology (spirals vs. ellipticals)?
Can you recreate the classic BPT diagnostic diagram using the emission-line ratios? Where do the different activity types fall?
Is there a relationship between a galaxy’s velocity dispersion (a proxy for central mass) and the type of nuclear activity it hosts?
Which galaxy morphological types are most likely to host Seyfert nuclei vs. LINERs?

Thank you to Tony Galvan, Golden Dome Data Science for curating this week’s dataset.

The Data

# Using R
# Option 1: tidytuesdayR R package 
## install.packages("tidytuesdayR")

tuesdata <- tidytuesdayR::tt_load('2026-08-11')
## OR
tuesdata <- tidytuesdayR::tt_load(2026, week = 32)

palomar_emission_lines <- tuesdata$palomar_emission_lines
palomar_survey <- tuesdata$palomar_survey

# Option 2: Read directly from GitHub

palomar_emission_lines <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2026/2026-08-11/palomar_emission_lines.csv')
palomar_survey <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2026/2026-08-11/palomar_survey.csv')

# Using Python
# Option 1: pydytuesday python library
## pip install pydytuesday

import pydytuesday

# Download files from the week, which you can then read in locally
pydytuesday.get_date('2026-08-11')

# Option 2: Read directly from GitHub and assign to an object

palomar_emission_lines = pandas.read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2026/2026-08-11/palomar_emission_lines.csv')
palomar_survey = pandas.read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2026/2026-08-11/palomar_survey.csv')

# Using Julia
# Option 1: TidierTuesday.jl library
## Pkg.add(url="https://github.com/TidierOrg/TidierTuesday.jl")

using TidierTuesday

# Download datasets for the week, and load them as a NamedTuple of DataFrames
data = tt_load("2026-08-11")

# Option 2: Read directly from GitHub and assign to an object with TidierFiles

palomar_emission_lines = read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2026/2026-08-11/palomar_emission_lines.csv")
palomar_survey = read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2026/2026-08-11/palomar_survey.csv")

# Option 3: Read directly from Github and assign without Tidier dependencies
palomar_emission_lines = CSV.read("https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2026/2026-08-11/palomar_emission_lines.csv", DataFrame)
palomar_survey = CSV.read("https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2026/2026-08-11/palomar_survey.csv", DataFrame)

How to Participate

Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data. There are various moderating variables that affect all data, many of which might not have been captured in these datasets. As such, our suggestion is to use the data provided to practice your data tidying and plotting techniques, and to consider for yourself what nuances might underlie these relationships.
Create a visualization, a model, a Quarto report, a shiny app, or some other piece of data-science-related output, using R, Python, or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.
Submit your own dataset!

PydyTuesday: A Posit collaboration with TidyTuesday

Exploring the TidyTuesday data in Python? Posit has some extra resources for you! Have you tried making a Quarto dashboard? Find videos and other resources in Posit’s PydyTuesday repo.
Share your work with the world using the hashtags #TidyTuesday and #PydyTuesday so that Posit has the chance to highlight your work, too!
Deploy or share your work however you want! If you’d like a super easy way to publish your work, give Connect Cloud a try.

Data Dictionary

`palomar_emission_lines.csv`

variable	class	description
galaxy_name	character	Common name of the galaxy. Can be joined with the palomar_survey dataset.
h_gamma	double	H-gamma (4340 angstrom) line intensity relative to H-alpha (= 1.0).
h_beta	double	H-beta (4861 angstrom) line intensity relative to H-alpha (= 1.0). Fundamental reference line for emission-line diagnostics.
oiii_5007	double	[O III] 5007 angstrom forbidden line intensity relative to H-alpha. Strongest line from doubly-ionized oxygen.
oi_6300	double	[O I] 6300 angstrom forbidden line intensity relative to H-alpha. Enhanced in shocks and LINER nuclei.
nii_6583	double	[N II] 6583 angstrom forbidden line intensity relative to H-alpha. Key line in BPT classification diagrams.
sii_6716	double	[S II] 6716 angstrom forbidden line intensity relative to H-alpha. One member of the sulfur doublet used for density estimates.
sii_6731	double	[S II] 6731 angstrom forbidden line intensity relative to H-alpha. The 6716/6731 ratio is density-sensitive.

`palomar_survey.csv`

variable	class	description
galaxy_name	character	Common name of the galaxy (e.g. “NGC 224”, “IC 342”).
hubble_type	character	Morphological type in the Hubble sequence (e.g. “Sb”, “E2”, “SBc”). Includes luminosity class when available.
b_magnitude	double	Apparent blue (B-band) magnitude, uncorrected for extinction. Brighter objects have lower values.
helio_velocity_km_s	double	Heliocentric radial velocity in km/s. Negative values indicate motion toward us. Dividing by ~70 gives approximate distance in Mpc.
ra_j2000	character	Right ascension in J2000 coordinates (hours, minutes, seconds).
dec_j2000	character	Declination in J2000 coordinates (degrees, arcminutes, arcseconds).
ha_hb_ratio	double	Observed H-alpha to H-beta intensity ratio. The theoretical value for pure hydrogen gas is 2.86; higher values indicate dust reddening.
internal_reddening_ebv	double	Internal color excess E(B-V) derived from the H-alpha/H-beta ratio. Measures dust inside the galaxy reddening the nuclear light.
sii_density_ratio	double	Ratio of [S II] 6716 to [S II] 6731 line intensities. Used to estimate electron density in the emitting gas.
electron_density_cm3	double	Electron density in particles per cubic centimeter, derived from the [S II] doublet ratio.
log_oiii_hb	double	Dereddened [O III] 5007 / H-beta intensity ratio. Higher values indicate higher ionization. Used on the y-axis of BPT diagrams.
log_oi_ha	double	Dereddened [O I] 6300 / H-alpha intensity ratio. Sensitive to shock excitation and partial ionization zones common in LINERs.
log_nii_ha	double	Dereddened [N II] 6583 / H-alpha intensity ratio. The primary x-axis of the classic BPT diagnostic diagram.
log_sii_ha	double	Dereddened [S II] (6716+6731) / H-alpha intensity ratio. Used in alternative BPT diagrams to separate Seyferts from LINERs.
spectral_class	character	Nuclear spectral classification code (e.g. “S1.9”, “L2”, “T2:”, “H”). Letters encode type, numbers subtype, colons indicate uncertainty.
activity_type	character	Simplified nuclear activity classification: “Seyfert”, “LINER”, “Transition”, “H II”, or “Absorption”.
activity_subtype	integer	Numeric subtype (1 or 2). Type 1 shows broad emission lines; type 2 shows only narrow lines.
classification_confidence	character	Confidence level: “confident”, “uncertain”, or “very uncertain”.
velocity_dispersion_km_s	double	Adopted central stellar velocity dispersion in km/s. Correlates with central mass via the M-sigma relation.
velocity_dispersion_error	double	Measurement uncertainty on the velocity dispersion in km/s.

Cleaning Script

# Palomar Spectroscopic Survey of Nearby Galaxies
# Data from Ho, Filippenko & Sargent (1995, 1997, 2009)
# Observed with the 200-inch Hale Telescope at Palomar Observatory
#
# Downloads three catalogs from VizieR and joins them into two datasets:
#   palomar_survey - Galaxy properties + spectral classification (486 rows)
#   palomar_emission_lines - Raw emission-line intensities (418 rows)

library(httr)
library(readr)
library(dplyr)
library(stringr)

# --- Helper: download a VizieR table as a data frame ---
get_vizier_table <- function(catalog, table_name) {
  url <- paste0(
    "https://vizier.cds.unistra.fr/viz-bin/asu-tsv?-source=",
    catalog, "/", table_name,
    "&-out.max=unlimited&-oc.form=dec"
  )
  resp <- GET(url, timeout(60))
  stopifnot(status_code(resp) == 200)
  txt <- content(resp, "text", encoding = "UTF-8")
  lines <- strsplit(txt, "\n")[[1]]

  non_comment <- which(!grepl("^#", lines) & nchar(trimws(lines)) > 0)
  header <- strsplit(lines[non_comment[1]], "\t")[[1]]
  data_lines <- lines[non_comment[-(1:2)]]
  data_lines <- data_lines[!grepl("^-", data_lines)]

  tsv_text <- paste(c(paste(header, collapse = "\t"), data_lines), collapse = "\n")
  read_tsv(I(tsv_text), show_col_types = FALSE, trim_ws = TRUE)
}

# Download raw tables from VizieR
observations   <- get_vizier_table("J/ApJS/98/477", "table2")
classification <- get_vizier_table("J/ApJS/112/315", "table4")
emission_lines <- get_vizier_table("J/ApJS/112/315", "table2")
dispersions    <- get_vizier_table("J/ApJS/183/1", "table1")

# Galaxy properties from the observation log
galaxies <- observations |>
  mutate(Name = trimws(Name)) |>
  distinct(Name, .keep_all = TRUE) |>
  transmute(
    galaxy_name = Name,
    hubble_type = trimws(Type),
    b_magnitude = BT,
    helio_velocity_km_s = HRV,
    ra_j2000 = `_RA.icrs`,
    dec_j2000 = `_DE.icrs`
  )

# Nuclear spectral classification and dereddened emission-line ratios
classes <- classification |>
  mutate(Name = trimws(Name)) |>
  transmute(
    galaxy_name = Name,
    ha_hb_ratio = `Ha/Hb`,
    internal_reddening_ebv = `E(B-V)int`,
    sii_density_ratio = `R([SII])`,
    electron_density_cm3 = Ne,
    log_oiii_hb = `[OIII]/Hb`,
    log_oi_ha = `[OI]/Ha`,
    log_nii_ha = `[NII]/Ha`,
    log_sii_ha = `[SII]/Ha`,
    spectral_class = trimws(Class),
    activity_type = case_when(
      grepl("^S", spectral_class) ~ "Seyfert",
      grepl("^L", spectral_class) ~ "LINER",
      grepl("^T", spectral_class) ~ "Transition",
      grepl("^H", spectral_class) ~ "H II",
      TRUE ~ "Absorption"
    ),
    activity_subtype = as.integer(str_extract(spectral_class, "[12]")),
    classification_confidence = case_when(
      grepl("::", spectral_class) ~ "very uncertain",
      grepl(":", spectral_class) ~ "uncertain",
      TRUE ~ "confident"
    )
  )

# Velocity dispersions
vel_disp <- dispersions |>
  mutate(Name = trimws(Name)) |>
  transmute(
    galaxy_name = Name,
    velocity_dispersion_km_s = sig,
    velocity_dispersion_error = e_sig
  )

# Join into main dataset
palomar_survey <- galaxies |>
  left_join(classes, by = "galaxy_name") |>
  left_join(vel_disp, by = "galaxy_name")

# Emission-line intensities (normalized to H-alpha = 1.0)
palomar_emission_lines <- emission_lines |>
  mutate(galaxy_name = trimws(Name)) |>
  transmute(
    galaxy_name,
    h_gamma = Hgamma,
    h_beta = Hbeta,
    oiii_5007 = `[OIII]`,
    oi_6300 = `[OI]`,
    nii_6583 = `[NII]`,
    sii_6716 = `[SII]a`,
    sii_6731 = `[SII]b`
  )