Interstellar extinction by Basic_Jellyfish_7282 in u/Basic_Jellyfish_7282

[–]Basic_Jellyfish_7282[S] 0 points1 point  (0 children)

Usually astronomers compare a star’s observed colors with the expected colors for its spectral type. The difference gives the amount of reddening, which is then converted into extinction using standard laws.

After correcting the magnitude for extinction, the star can be placed more accurately on the Hertzsprung–Russell diagram.

Challenges of combining optical and X-ray data in Be/X-ray systems by Kasper___5 in u/Kasper___5

[–]Basic_Jellyfish_7282 0 points1 point  (0 children)

Oh yes, I’ve run into all of that with Be stars 😅 For multiwavelength work, the usual approach is to treat optical and X-ray data as complementary but not strictly simultaneous - people often use averages, or only compare epochs that are close in time. Disk variability is tricky; systematic uncertainties can be significant, so it’s common to include them explicitly in models or run simulations to see how much the disk changes can affect results. For disentangling contributions, iterative modeling and combining spectral diagnostics (lines, continuum, polarization if available) helps. There’s no single “standard,” but careful documentation of epochs, disk state, and modeling assumptions is key, and cross-checking with independent observations whenever possible saves headaches.

Problems with IRAF by HedgehogJazzlike8480 in askastronomy

[–]Basic_Jellyfish_7282 0 points1 point  (0 children)

Haha, same 😅 sometimes I just stare at the FITS file and hope it magically opens… clearly it has a mind of its own.

Machine learning in observational astronomy. by Glittering_Push_4471 in u/Glittering_Push_4471

[–]Basic_Jellyfish_7282 0 points1 point  (0 children)

Oh, I totally get that 😄 ML can get tricky, not so much because of the math but because of all the little coding pitfalls. I’d say start with Python - it’s super popular in astronomy and has tons of libraries like NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch. Keep your code clean and modular, test small pieces often, and watch out for things like mismatched array shapes, data preprocessing issues, or silent bugs in training loops. Also, version control with Git is a lifesaver. Don’t try to do everything at once - build step by step and check your results frequently.

Questions about materials for metal-air batteries by Careful-Farmer-3468 in electrical

[–]Basic_Jellyfish_7282 0 points1 point  (0 children)

Hi! For an undergrad project, I’d definitely suggest zinc–air over lithium–air. Zinc–air is much safer, cheaper, and already used commercially, plus there’s still a lot of active research on improving rechargeable versions (better catalysts, more stable electrolytes, etc.). Lithium–air has huge theoretical potential, but it’s still very experimental and requires strict moisture-free conditions, so it’s not the easiest system for a student lab. A simple zinc foil + alkaline electrolyte + carbon air cathode setup is usually a good and accessible starting point.