Mastering Astropy Python: How To Stack Astronomical FITS Data Cube Spaxel Spectra | DESI ASTRO

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In this video, we dive deep into the technique of stacking FITS spectral data cubes, a crucial process for astronomers seeking to maximize the signal-to-noise ratio (SNR) in their observations. Stacking is a key data reduction method used to combine multiple exposures or observations of the same target, ultimately improving the quality and clarity of the resulting dataset. This is particularly useful when studying faint objects like distant galaxies, nebulae, or exoplanetary atmospheres.

Chapter Timestamps
-----------------------------------
00:00:00 Introduction To stacking Spectra
00:01:44 Spectra Stacking Methods
00:03:23 Download SDSS MANGA Data Cube
00:04:10 Read data Cube
00:06:50 Extracting Spectra: Flux & Wavelength
00:08:37 Stacking The Spaxel Spectra
00:11:40 Visualize Stacked Spectra

Why Stack FITS Spectral Data Cubes?
Stacking FITS spectral data cubes serves multiple purposes:

Enhancing Signal-to-Noise Ratio (SNR): In astronomical observations, faint signals from distant objects are often buried in noise. Noise can come from various sources, including the telescope, the atmosphere, and the detector itself. By stacking multiple data cubes of the same region of the sky or object, astronomers can average out the noise, allowing the true signal to become more prominent.

Improving Data Quality: Stacking allows researchers to combine several low-exposure observations to simulate a longer, higher-quality exposure. Instead of increasing exposure time in a single observation (which may introduce tracking issues or saturation), multiple shorter exposures are taken and then stacked, resulting in a sharper and clearer image.

Reducing Instrumental and Atmospheric Artifacts: By averaging multiple observations, artifacts like cosmic rays, detector noise, or atmospheric distortions are minimized. These artifacts can appear as spikes or irregularities in individual exposures, but when combined through stacking, they become less significant, leaving a cleaner dataset for analysis.

Maximizing Data from Different Observations: Stacking also allows astronomers to combine data from different instruments or observing runs. For example, observations of the same target taken over several nights or by different telescopes can be stacked to create a composite dataset. This is particularly useful in multi-wavelength studies, where data from different parts of the spectrum are compared to provide a more complete picture of the object being studied.

Ways of Stacking FITS Spectral Data Cubes:

Averaging Stacking: Combines multiple data cubes by averaging each pixel across the cubes, reducing noise and increasing signal clarity.

Median Stacking: Uses the median value of each pixel, helping to minimize the impact of outliers or noise spikes.

Weighted Stacking: Assigns different weights to cubes based on their quality (e.g., SNR), giving more importance to better data in the final result.

Error-based Stacking: Incorporates the error estimates of each data cube, reducing uncertainties in the stacked data.

Each method enhances data quality, but the best approach depends on the specific goal of the analysis.

[goutham]

Beautiful video. Will you be explaining the rest of the methods too?
Also can you make a video of how to extract radial velocity curve from stellar spectra?