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“At other telescopes, astronomers are developing machine-learning algorithms that could identify, extract, and compensate for dirty data“ Whenever I see “machi

• EZ-E 8y ago
  It's probably closer to statistics than to machine-learning at this stage. People just love to say they are doing machine learning, it's "sexy"
  • xevb3k 8y ago
    It just says “developing”. I would imagine this is code for “writing papers about”. I’m sure those papers use whatever technique is currently hot and will produce a decent publication. I doubt they use simple statistical approaches, because that’s likely not novel or publishable. This is all fine. However, the problem is suggesting that this is a somehow reasonable/tenable solution to solving the noise issues... Maybe the noise can be removed by other methods, maybe the noise doesn’t matter, but magic doesn’t solve the noise issues.
    • martinpw 8y ago
      > Maybe the noise can be removed by other methods, maybe the noise doesn’t matter, but magic doesn’t solve the noise issues. The noise definitely matters. When you are observing with a telescope you are almost by definition working at the limit of the signal to noise ratio of the instrument, otherwise you would be asked to use a less powerful instrument (since the observing time on these larger instruments is so precious). I worked on a project analyzing data from an X-ray telescope back in my research days. We spent 3 full years building an incredibly detailed model of the instrument noise, just so we could subtract it out and have confidence in our remaining signal. That was longer than the time we spent doing the actual science on the corrected data.
  • deleted 8y ago
    [deleted]
• oh-kumudo 8y ago
  I mean de-noising techniques, nowadays can be definitely claimed as Machine Learning, even though they have long existed prior to even ML as a concept even being invented...
  • xevb3k 8y ago
    For example?
    • tanilama 8y ago
      PCA for example. It is essentially SVD. And it is quite funny though, I believe that there is no learning in PCA(maybe depends on implementation, but the popular one I believe used heuristics), it is still categoried as ML techniques all the time.
      • chriswarbo 8y ago
        PCA can "learn" in the sense that we can apply it to more and more data, and that can affect the directions of the components. For example, let's say we take measurements one at a time and apply PCA to the whole dataset after each measurement; if the first 100 measurements follow some distibution A, then PCA will give the components of A; if the next 1000 measurements follow some different distribution B, then PCA will 'learn' to give the components of B a higher priority than those of A; i.e. it adapts to the data. In that hypothetical setup, we end up storing every raw datapoint. If we think of these as parameters, then PCA is a non-parametric model; although it's maximally inefficient in terms of information/compression.
      • plouffy 8y ago
        The learning aspect could be how many principal components to use ?
        • tanilama 8y ago
          That is a hyper parameter to the model. It is not learned.
• jasone 8y ago
  We had a morally similar concept to "machine learning" in academia during my grad school days: "non-parametric statistic". To me they were anathema, but non-parametric statistics are shockingly common both in academia and in industry. They work well, except when they don't.
  • krona 8y ago
    They work well, except when they don't. Just like parametric statistics, then?
  • MrLeap 8y ago
    non-parametrics! I took that class in college. This brings back memories. I got the impression you use non parametrics when you've got a sample size of 4 and you don't want all your statistics being un-legit sounding numbers like 25, 22.5, 50.. etc. "Increase" our sample size by adding every permutation of the 4 data points we started with to the sample! Gets you some cool statsi numbers like 63.1924.
• tabtab 8y ago
  I wonder if multiple smaller scopes electronically "connected" would be cheaper than one big one.
• qubitcoder 8y ago
  For another perspective, check out this article in Symmetry Magazine, "Neural networks meet space" [1]. > "Analyses that typically take weeks to months to complete, that require the input of experts and that are computationally demanding, can be done by neural nets within a fraction of a second, in a fully automated way and, in principle, on a cell phone’s computer chip,” says postdoctoral fellow Laurence Perreault Levasseur, a co-author of a study published today in Nature." > "The team at the Kavli Institute for Particle Astrophysics and Cosmology (KIPAC), a joint institute of SLAC and Stanford, used neural networks to analyze images of strong gravitational lensing, where the image of a faraway galaxy is multiplied and distorted into rings and arcs by the gravity of a massive object, such as a galaxy cluster, that’s closer to us. The distortions provide important clues about how mass is distributed in space and how that distribution changes over time – properties linked to invisible dark matter that makes up 85 percent of all matter in the universe and to dark energy that’s accelerating the expansion of the universe." > "Until now this type of analysis has been a tedious process that involves comparing actual images of lenses with a large number of computer simulations of mathematical lensing models. This can take weeks to months for a single lens." > "But with the neural networks, the researchers were able to do the same analysis in a few seconds, which they demonstrated using real images from NASA’s Hubble Space Telescope and simulated ones." [1] https://www.symmetrymagazine.org/article/neural-networks-meet-space https://www.symmetrymagazine.org/article/neural-networks-mee...