I have decided to do undergraduate thesis on "estimating photometric redshift" or something related to this using machine learning. Reading previous papers, I have come to know that work has been done on this topic using support vector machine, artificial neural network, Bayesian approach and even deep learning. Can you suggest what are the modifications or additions that I can possibly make?


The process of determining photometric redshift includes, e.g.,: 1. objects of interest 2. types of data (e.g., one epoch multiple filters, one filter multiple epochs) 3. method of analysis (including model, and algorithm)

From what you mentioned, those are just algorithms. So, you might take a look around on other aspects as listed.

  • $\begingroup$ Thanks. Can you please add few more details about type of data? $\endgroup$ – Moonzarin Esha May 4 '18 at 13:52
  • $\begingroup$ I don't know how to elaborate about that topic at the moment. I think it pretty much explains itself. May be, someone else can help on that. Or, if I can think of something later, I will let you know. $\endgroup$ – Kornpob Bhirombhakdi May 4 '18 at 14:04
  • $\begingroup$ Ok can you just explain what you meant by "one epoch, many filters and one filter many epochs"? $\endgroup$ – Moonzarin Esha May 5 '18 at 14:27
  • $\begingroup$ Epoch = date Filter = for example, UBVRI bands one epoch, many filters = for example, observe today in UBVRI bands one filter, many epochs = for example, observe today and tomorrow in U band only $\endgroup$ – Kornpob Bhirombhakdi May 5 '18 at 16:43

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