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'“The neural networks we tested—three publicly available neural nets and one that we developed ourselves—were able to determine the properties of each lens, inc
by hacker_9 9y ago
'“The neural networks we tested—three publicly available neural nets and one that we developed ourselves—were able to determine the properties of each lens, including how its mass was distributed and how much it magnified the image of the background galaxy,” says the study’s lead author Yashar Hezaveh, a NASA Hubble postdoctoral fellow at KIPAC.
This goes far beyond recent applications of neural networks in astrophysics, which were limited to solving classification problems, such as determining whether an image shows a gravitational lens or not.'
Pretty fascinating stuff. Once you think about it, applying NNs to space makes a lot of sense. There is a ton of data to sift through and find patterns in. Amazing to think neural nets could crunch through this data in seconds, and point out areas of interest immediately. I wonder if NNs have been used in the search for exo planets yet.
- tachyonbeam 9y ago> I wonder if NNs have been used in the search for exo planets yet. Might be kind of overkill. The patterns being looked at for exoplanets are periodical dimming of stars AFAIK. I don't think you necessarily need a neural network to sift through that.
- jawbone3 9y agoDude, stars don't have constant brigthness...
- QAPereo 9y agoThey are also very very far away, and only a few photons are making it to the lens every so often; it's quite a difficult task.
- yh_82367 9y agoyes and no. Most exoplanet detections so far have been indirect: people just search for the dimming from the eclipse or similar other methods. So you're right about that. But recently we're starting to actually directly take sensitive enough images to be able to see planets (see http://planetimager.org http://planetimager.org). They aren't using NNs yet. But there have been discussions of using NNs for various aspects of those searches.
- sanxiyn 9y agoDetecting dimming is actually surprisingly difficult and currently partly done by humans using Mechanical Turk-like platform. https://www.planethunters.org/ https://www.planethunters.org/
- jawbone3 9y agoAstronomy strives to be a science, so ultimately it needs to tune causal models with data. NNs will by their nature never be causal (their entire point is that they can approximate everything), so they will be used like here to find candidates to investigate with real models.
- xaedes 9y ago"NNs will by their nature never be causal (their entire point is that they can approximate everything)". A NN can sureley result in a traditional causal model. For example: Build a NN that has the same computational structure as some simple physics law. By giving it training data it then figures out necessary constants. That may converge to the traditional model, which is mirrowed in the network architecture anyway. So I really can't support saying NN will by their nature NEVER be causal.
- jacobush 9y agoEspecially since humans arguably uses wetware neural nets and don't we like to think we are coming up with causal models?
- sanxiyn 9y agoWell, "neural nets that come up with causal models" is a research problem and we don't know how to do so.
- jawbone3 9y agoI'm not saying a NN doesn't reproduce analytical expressions, rather that "take everything and mix it together a bunch of times using this recipe" is not the form that the laws of nature has. I'm not sure what you think explicit construction proves, it is clearly a case of taking preexisting knowledge and expressing it in what is doubthlessly a more akward form.
- nl 9y agoThe article says they use the neural net to find lenses which match the model the astronomers develop: To train the neural networks in what to look for, the researchers showed them about half a million simulated images of gravitational lenses for about a day. I'm not sure what you mean by a "real models" in any case. Things like NN based generative models combined with model selection certainly can build models which discover real-world behavior. There's a long history of this in the disease epidemiology field. In these cases it isn't usually neural network based, but that is mostly about the most appropriate learning algorithm for the data available.