Photometric Redshift Analysis using Supervised Learning Algorithms and Deep Learning. (arXiv:1901.07544v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Chong_K/0/1/0/all/0/1">Kenny Chong</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Yang_A/0/1/0/all/0/1">Abel Yang</a>
We present a catalogue of galaxy photometric redshifts for the Sloan Digital
Sky Survey (SDSS) Data Release 12. We use various supervised learning
algorithms to calculate redshifts using photometric attributes on a
spectroscopic training set. Two training sets are analysed in this paper. The
first training set consists of 995,498 galaxies with redshifts up to $z approx
0.8$. On the first training set, we achieve a cost function of 0.00501 and a
root mean squared error value of 0.0707 using the XGBoost algorithm. We
achieved an outlier rate of 2.1% and 86.81%, 95.83%, 97.90% of our data
points lie within one, two, and three standard deviation of the mean
respectively. The second training set consists of 163,140 galaxies with
redshifts up to $zapprox0.2$ and is merged with the Galaxy Zoo 2 full catalog.
We also experimented on convolutional neural networks to predict five
morphological features (Smooth, Features/Disk, Star, Edge-on, Spiral). We
achieve a root mean squared error of 0.117 when validated against an unseen
dataset with over 200 epochs. Morphological features from the Galaxy Zoo,
trained with photometric features are found to consistently improve the
accuracy of photometric redshifts.
We present a catalogue of galaxy photometric redshifts for the Sloan Digital
Sky Survey (SDSS) Data Release 12. We use various supervised learning
algorithms to calculate redshifts using photometric attributes on a
spectroscopic training set. Two training sets are analysed in this paper. The
first training set consists of 995,498 galaxies with redshifts up to $z approx
0.8$. On the first training set, we achieve a cost function of 0.00501 and a
root mean squared error value of 0.0707 using the XGBoost algorithm. We
achieved an outlier rate of 2.1% and 86.81%, 95.83%, 97.90% of our data
points lie within one, two, and three standard deviation of the mean
respectively. The second training set consists of 163,140 galaxies with
redshifts up to $zapprox0.2$ and is merged with the Galaxy Zoo 2 full catalog.
We also experimented on convolutional neural networks to predict five
morphological features (Smooth, Features/Disk, Star, Edge-on, Spiral). We
achieve a root mean squared error of 0.117 when validated against an unseen
dataset with over 200 epochs. Morphological features from the Galaxy Zoo,
trained with photometric features are found to consistently improve the
accuracy of photometric redshifts.
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