--- name: astrophysics-analysis-cefafdfb2390 description: This skill should be used when the user asks to "analyze astronomical data", "process FITS files", "create sky maps", "work with cosmological simulations", "fit spectral data", "calculate redshifts", "analyze light curves", "work with astronomical catalogs", "perform astrometry", "process images from telescopes", or mentions astronomical instruments, surveys, or astrophysical phenomena. Provides guidance for scientific astrophysics workflows and best practices. version: 1.0.0 --- # Astrophysics Analysis Skill ## Overview This skill provides specialized guidance for astrophysics analysis workflows, astronomical data processing, and scientific computing tasks common in astronomy research. It covers observational astronomy, cosmological analysis, and astrophysical simulations. ## Core Capabilities ### Astronomical Data Formats Handle standard astronomy file formats: - **FITS files**: Read and write Flexible Image Transport System files using astropy.io.fits - **HDF5**: Process large simulation datasets with h5py - **VOTable**: Work with Virtual Observatory tables using astropy.io.votable - **ASCII catalogs**: Parse space-separated and CSV astronomical catalogs ### Observational Analysis Perform common observational astronomy tasks: - **Photometry**: Aperture and PSF photometry using photutils - **Spectroscopy**: Spectral extraction, calibration, and line fitting with specutils - **Astrometry**: World coordinate system handling and coordinate transformations - **Time series**: Light curve analysis and period finding ### Cosmological Computations Execute cosmological calculations: - **Distance measures**: Luminosity distance, angular diameter distance, comoving distance - **Cosmological parameters**: Work with standard cosmologies (Planck, WMAP) - **Power spectra**: Matter and CMB power spectrum analysis - **Redshift calculations**: Photometric and spectroscopic redshift handling ## Recommended Python Stack Use these established astrophysics libraries: ```python # Core astronomy import astropy from astropy import units as u from astropy.coordinates import SkyCoord from astropy.io import fits from astropy.cosmology import Planck18 # Visualization import matplotlib.pyplot as plt from astropy.visualization import ZScaleInterval, ImageNormalize # Data analysis import numpy as np from scipy import optimize, interpolate # Specialized tools import photutils # Photometry import specutils # Spectroscopy import healpy # HEALPix sky maps ``` ## Common Workflows ### Loading FITS Data ```python from astropy.io import fits from astropy.wcs import WCS # Open FITS file with fits.open('image.fits') as hdul: data = hdul[0].data header = hdul[0].header wcs = WCS(header) ``` ### Coordinate Transformations ```python from astropy.coordinates import SkyCoord from astropy import units as u # Create sky coordinate coord = SkyCoord(ra=10.5*u.deg, dec=-30.2*u.deg, frame='icrs') # Transform to galactic galactic = coord.galactic print(f"l={galactic.l:.2f}, b={galactic.b:.2f}") ``` ### Cosmological Distances ```python from astropy.cosmology import Planck18 import astropy.units as u z = 0.5 # redshift d_L = Planck18.luminosity_distance(z) d_A = Planck18.angular_diameter_distance(z) d_C = Planck18.comoving_distance(z) ``` ### Spectral Line Fitting ```python from specutils import Spectrum1D from specutils.fitting import fit_lines from astropy.modeling import models # Create Gaussian model for emission line g_init = models.Gaussian1D(amplitude=1*u.Jy, mean=6563*u.AA, stddev=2*u.AA) g_fit = fit_lines(spectrum, g_init) ``` ## Best Practices ### Units and Quantities Always use astropy units for physical quantities: ```python from astropy import units as u # Attach units wavelength = 5000 * u.AA flux = 1e-17 * u.erg / u.s / u.cm**2 / u.AA # Convert between units wavelength_nm = wavelength.to(u.nm) ``` ### Error Handling Propagate uncertainties through calculations: ```python from astropy.nddata import StdDevUncertainty from uncertainties import ufloat # Using uncertainties package flux = ufloat(1.5e-17, 0.2e-17) # value +/- error ``` ### Reproducibility Document analysis parameters and random seeds: ```python import numpy as np np.random.seed(42) # For reproducible results # Log parameters params = { 'aperture_radius': 5.0, # arcsec 'background_annulus': (10.0, 15.0), 'sigma_clip': 3.0 } ``` ### Memory Management Handle large datasets efficiently: ```python # Use memmap for large FITS files with fits.open('large_image.fits', memmap=True) as hdul: # Process in chunks chunk = hdul[0].data[1000:2000, 1000:2000] ``` ## Data Sources Access astronomical archives: - **MAST**: Hubble, JWST, TESS data via astroquery.mast - **ESO Archive**: VLT, ALMA data via astroquery.eso - **SDSS**: Sloan Digital Sky Survey via astroquery.sdss - **Vizier**: Catalog access via astroquery.vizier - **Simbad**: Object information via astroquery.simbad ### Example Archive Query ```python from astroquery.simbad import Simbad from astroquery.vizier import Vizier # Query Simbad for object result = Simbad.query_object("M31") # Query Vizier catalog v = Vizier(columns=['*']) catalogs = v.query_region("M31", radius=1*u.deg, catalog="II/246") ``` ## Visualization Standards Create publication-quality figures: ```python import matplotlib.pyplot as plt from astropy.visualization import ZScaleInterval, ImageNormalize # Astronomical image display norm = ImageNormalize(data, interval=ZScaleInterval()) plt.imshow(data, norm=norm, cmap='gray', origin='lower') plt.colorbar(label='Counts') ``` ## Performance Considerations Optimize computation for large datasets: - Use NumPy vectorized operations instead of loops - Consider Dask for out-of-core computation - Use JAX for GPU-accelerated numerical work - Profile code with cProfile before optimization ## When to Use This Skill Activate this skill for tasks involving: - Processing telescope observations - Analyzing galaxy spectra or photometry - Working with cosmological simulations - Calculating astronomical quantities with proper units - Querying astronomical databases and catalogs - Creating sky maps or coordinate transformations - Fitting models to astrophysical data --- Source attribution: David-Li0406 Original project: https://github.com/David-Li0406/meta-skill-evloving/blob/ca3a335628981df10c36e00cb9850df2c247ab9a/skill-flow/data/skills-refined-36k/skillsmp/astrophysics-analysis/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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See the License for the specific language governing permissions and limitations under the License. # Mega AI Hub resource metadata Creator / publisher: David-Li0406 Original project: https://github.com/David-Li0406/meta-skill-evloving/blob/ca3a335628981df10c36e00cb9850df2c247ab9a/skill-flow/data/skills-refined-36k/skillsmp/astrophysics-analysis/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Design, Education & Research Roles: Data Scientist, Researcher, UX Designer Specialties: API, Testing Tools: Astropy, Matplotlib, Photutils, Scipy, Specutils Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 18c1f6db326e981a3a517407b88bce3e0bf400cdc761daccad80abca6d8d2669