The following is "just" an MS Thesis but I simply found it fascinating for two reasons: Even though the author knows about astrophysics, you can read the thesis and think that he could be foreign to the field. Second, since CNNs are doing well on images, Pavel does what people have been doing with voice, convert spectra into images. From the thesis:
I was inspired by the way CRT monitors rasterize image on the shadow mask. The output matrices of intensities were normalized using linear normalization and 8-bit grayscale encoding. Finally the product was then converted into an actual PNG image using imagemagick scripting tools, which is the fastest and most reliable approach I have found. The process is illustrated in figures 5.10 and 5.11.
this tells me that hyperspectral imaging is cleary next. Without further ado: Spectral classification using convolutional neural networks by Pavel Hála
There is a great need for accurate and autonomous spectral classification methods in astrophysics. This thesis is about training a convolutional neural network (ConvNet) to recognize an object class (quasar, star or galaxy) from one-dimension spectra only. Author developed several scripts and C programs for datasets preparation, preprocessing and postprocessing of the data. EBLearn library (developed by Pierre Sermanet and Yann LeCun) was used to create ConvNets. Application on dataset of more than 60000 spectra yielded success rate of nearly 95%. This thesis conclusively proved great potential of convolutional neural networks and deep learning methods in astrophysics.
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