<jupyter_start><jupyter_text>Astronomical Data The case of the Sloan Digital Sky Survey![sdss telescope](./images/06-sdss-telescope-small.jpg)- 2.5 m telescope at Apache Point Observatory, New Mexico.    - *Let's talk about spatial resolution*Airy Rings![Airy ring](./images/06-Airy-ring.jpg)Rayleigh Criterion![Angular Resolution](./images/06-angular-resolution.png)Atmospheric Seeing![Seeing](./images/06-seeing.jpg)- 12.000 deg$^2$ (full sky ~40.000 deg$^2$)- 120 MP camera; 0.396 arcsec/pixel.    - *Let's talk about CCDs*- Imaging in 5 photometric bands: $u, g, r, i , z$    - *Let's talk about astronomical filters*![sdss filters](./images/06-sdss-filters.png)- Photometry with precision of up to 0.01-0.02 mag- Limiting magnitude is $r\sim22.5$    - *Let's talk about the magnitude system in Astronomy, and uncertainties in magnitudes.* ---  A word about photometry- For stars, fairly simple. Define an aperture that contains the star, define a ring to estimate the background and subtract it.- But what about an extended source like a galaxy? Let's look at an example image<jupyter_code>import numpy as np
import matplotlib.pyplot as plt

from astroquery.sdss import SDSS
from astropy import coordinates as coords

pos = coords.SkyCoord('0h8m05.63s +14d50m23.3s', frame='icrs')
xid = SDSS.query_region(pos, spectro=True)
im = SDSS.get_images(matches=xid, band='r')
data = im[0][0].data
std = data.std()
plt.figure(figsize=(10, 8))
plt.imshow(data[1100: 1400, 900:1400], origin='lower', vmin=0, vmax=std, cmap='gray_r')<jupyter_output><empty_output><jupyter_text>- DR7 includes ~1.6 M spectra: 900,000 Galaxies, 120,000 QSOs, and 460,000 stars- These are taken with fibers with a 3" diameter size- Wavelength coverage: 3800--9200 Angstroms- R ~ 2000*Let's discuss about Astronomical spectra*<jupyter_code>sp = SDSS.get_spectra(matches=xid)

plt.figure(figsize=(8, 6))

plt.plot(10**(sp[0][1].data.loglam), sp[0][1].data.flux)

plt.xlabel('Wavelength [Angstrom]')
plt.ylabel('Flux');<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1347201584943041, 'fraction_numerical': 0.05794947994056464, 'mean_word_length': 5.253869969040248, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '480842', 'license': 'MIT', 'n_tokens_mistral': 793, 'n_tokens_neox': 675, 'n_words': 205, 'path': '06-Astronomical Data I.ipynb', 'repo_name': 'thevalentino/AS450-astroinformatica', 'size': '2019'}