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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?

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    $\begingroup$ This is a reaaaally broad question. Like broad enough to have three PhDs and two postdocs working on it. $\endgroup$
    – usernumber
    Dec 20, 2019 at 9:00
  • $\begingroup$ The up-voting suggests that people feel this is an interesting question, but as currently written it's probably too broad for a Stack Exchange answer to cover it. Can you add some more information to the question, perhaps what it was that suggested to you that it would be a good topic? $\endgroup$
    – uhoh
    Dec 21, 2019 at 5:26

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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.

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  • $\begingroup$ Thanks. Can you please add few more details about type of data? $\endgroup$ May 4, 2018 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. $\endgroup$ May 4, 2018 at 14:04
  • $\begingroup$ Ok can you just explain what you meant by "one epoch, many filters and one filter many epochs"? $\endgroup$ May 5, 2018 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 $\endgroup$ May 5, 2018 at 16:43

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