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The domain knowledge needed for this particular paper is literally just math, first semester undergrad physics, a chapter or two of an introductory astrophysics

The domain knowledge needed for this particular paper is literally just math, first semester undergrad physics, a chapter or two of an introductory astrophysics text, and some image processing—which, if you understand the underlying math, is easy to pick up. The novelty here is the algorithm, which I imagine, given the prerequisite knowledge, most people here could conceivably develop. Astronomy is currently a highly accessible field. Amateurs are still making meaningful research contributions. There is also a lot of grunt work available using other people's methods. Edit to add: You don't even need to gather your own data. There are many, many datasets lying around waiting to be processed by new techniques.

ad404b8a372f2b9 4y ago
That's unrealistic, the final result being understandable with an undergrad level of math does not mean it takes an undergrad level of math to reach that result. No one here could have divinated that algorithm without doing the legwork. The basic theory alone, which you list, represents a significant time investment and that's only to reproduce the work, knowing exactly which concepts you need to learn. A lot of the papers I read are implemented by adding a single line of code to a model that someone with an undergrad knowledge of probabilities could have written. Except they couldn't because to get that line of code you need a deep knowledge of the prior work, good fundamental knowledge of the discipline to even come up with such an idea, knowledge of the datasets that are in use, the problems that are interesting to work on and those that aren't, etc ...
- cgriswald 4y ago
  No, this guy just wants to get involved in stuff like this and you're telling him its impossible; in a field where amateurs are making meaningful contributions; in response to a paper that is more data science than astrophysics. > No one here could have divinated that algorithm without doing the legwork. No one here could understand how to combine rudimentary image processing in such a way as to track a moving object over time? It's impressive, certainly, but there's more data processing here than astrophysics. Most people with a BA in science or math would have enough requisite knowledge to pick up what they would need for this paper over the course of a weekend. The part they couldn't do that with would be the image processing and data science. If they already have that? Yes, this is absolutely achievable. Just copying this work on a different data set? Also very achievable. The knowledge of which datasets are in use? In this field those are both mostly public and easy to find. Yes, those who don't have some of this knowledge will have to put in more time than others, but the implication you make about the sheer amount of time needed and what would actually be required just to get involved just doesn't apply here. There was a time half a decade ago where someone like the original poster could literally get a job doing this stuff. That may still even be the case.
  - ad404b8a372f2b9 4y ago
    > No one here could understand how to combine rudimentary image processing in such a way as to track a moving object over time? You make it sound easy, I don't know why those 10 authors even wasted their time publishing this. It reminds me of a joke about getting grants, you show someone your proposal and they'll tell you it's a bad idea and will never work, you show them your work after a few years and they'll tell you the results are obvious and everyone already knew. I didn't tell him it's impossible, I said it requires a lot of work beyond scripting and specifically listed some of the aspects of the work that are easier to get into. Of course they could get a job doing it, that would involve spending 8 hours a day working on the topic and doing all the things I described. You word it like you're disagreeing with me but go on to list skill after skill that's required to actually achieve this. A bachelor of science alone is three years of studies, data science and image processing are very specialized scientific skill-sets that take a long time to acquire. I've seen plenty of people take a 6-months bootcamp who are barely able to manipulate a matrix at the end. Knowing which datasets and problems to work on requires a scientific culture which you acquire by reading the literature, not just googling. Combining all these skills into an idea that results into a quality publishable scientific output is a job and it requires as much work as any job does. By all means, let people do the work and contribute if it's interesting to them, but everyone here feels the same way when we see a paper come through here in which they "just" combine some Hough transforms and some observations "oh I could have done that." It doesn't work that way.
    - deleted 4y ago
      [deleted]
    - pbhjpbhj 4y ago
      I don't think the person you're arguing with did make it sound easy to do what it seems the paper authors did: which appears to be having the idea to apply this sort of analysis. As with so much development there is a massive gap between 'I can do that now, someone showed it worked' and 'I could have come up with that before anyone showed it worked'. Leading edge science becomes demonstrations that undergrads do after a time. Plenty of fields seem to progress when someone realises a simple idea can be applied in new way. 6-months to learn matrix arithmetic algos sounds like that person just wasn't cut out for that task. Best option here seems like encourage the person to try applying their domain knowledge from CV to the field at hand and ask them to report back how they get on. If they've applied Hough transforms before, perhaps, they might be able to refine the algos here, for example. tl;dr I think you're being a bit precious about it all.

ad404b8a372f2b9 4y ago

That's unrealistic, the final result being understandable with an undergrad level of math does not mean it takes an undergrad level of math to reach that result. No one here could have divinated that algorithm without doing the legwork. The basic theory alone, which you list, represents a significant time investment and that's only to reproduce the work, knowing exactly which concepts you need to learn. A lot of the papers I read are implemented by adding a single line of code to a model that someone with an undergrad knowledge of probabilities could have written. Except they couldn't because to get that line of code you need a deep knowledge of the prior work, good fundamental knowledge of the discipline to even come up with such an idea, knowledge of the datasets that are in use, the problems that are interesting to work on and those that aren't, etc ...

cgriswald 4y ago
No, this guy just wants to get involved in stuff like this and you're telling him its impossible; in a field where amateurs are making meaningful contributions; in response to a paper that is more data science than astrophysics. > No one here could have divinated that algorithm without doing the legwork. No one here could understand how to combine rudimentary image processing in such a way as to track a moving object over time? It's impressive, certainly, but there's more data processing here than astrophysics. Most people with a BA in science or math would have enough requisite knowledge to pick up what they would need for this paper over the course of a weekend. The part they couldn't do that with would be the image processing and data science. If they already have that? Yes, this is absolutely achievable. Just copying this work on a different data set? Also very achievable. The knowledge of which datasets are in use? In this field those are both mostly public and easy to find. Yes, those who don't have some of this knowledge will have to put in more time than others, but the implication you make about the sheer amount of time needed and what would actually be required just to get involved just doesn't apply here. There was a time half a decade ago where someone like the original poster could literally get a job doing this stuff. That may still even be the case.
- ad404b8a372f2b9 4y ago
  > No one here could understand how to combine rudimentary image processing in such a way as to track a moving object over time? You make it sound easy, I don't know why those 10 authors even wasted their time publishing this. It reminds me of a joke about getting grants, you show someone your proposal and they'll tell you it's a bad idea and will never work, you show them your work after a few years and they'll tell you the results are obvious and everyone already knew. I didn't tell him it's impossible, I said it requires a lot of work beyond scripting and specifically listed some of the aspects of the work that are easier to get into. Of course they could get a job doing it, that would involve spending 8 hours a day working on the topic and doing all the things I described. You word it like you're disagreeing with me but go on to list skill after skill that's required to actually achieve this. A bachelor of science alone is three years of studies, data science and image processing are very specialized scientific skill-sets that take a long time to acquire. I've seen plenty of people take a 6-months bootcamp who are barely able to manipulate a matrix at the end. Knowing which datasets and problems to work on requires a scientific culture which you acquire by reading the literature, not just googling. Combining all these skills into an idea that results into a quality publishable scientific output is a job and it requires as much work as any job does. By all means, let people do the work and contribute if it's interesting to them, but everyone here feels the same way when we see a paper come through here in which they "just" combine some Hough transforms and some observations "oh I could have done that." It doesn't work that way.
  - deleted 4y ago
    [deleted]
  - pbhjpbhj 4y ago
    I don't think the person you're arguing with did make it sound easy to do what it seems the paper authors did: which appears to be having the idea to apply this sort of analysis. As with so much development there is a massive gap between 'I can do that now, someone showed it worked' and 'I could have come up with that before anyone showed it worked'. Leading edge science becomes demonstrations that undergrads do after a time. Plenty of fields seem to progress when someone realises a simple idea can be applied in new way. 6-months to learn matrix arithmetic algos sounds like that person just wasn't cut out for that task. Best option here seems like encourage the person to try applying their domain knowledge from CV to the field at hand and ask them to report back how they get on. If they've applied Hough transforms before, perhaps, they might be able to refine the algos here, for example. tl;dr I think you're being a bit precious about it all.