Comments (8)
Here the Md star parsec distances calculated by Deep Learning appear to be consistently off across multiple Sloan Digital Sky Survey Apogee DR16 star fields
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Hi @JosephKarpinski , thanks for reporting the issue with all the detail.
The reason why NN distance is very wrong for Md stars is because they are not really many of them in our training set due to cuts as we focus mostly on giants. You can check dist_error
to check how certain we are on NN dist
. Moreover we recommend to cut out all the stars where NN logg has more than 0.2dex uncertainty (i.e. logg_err
which Md stars generally have almost 0.4dex uncertainty on logg from NN model)
When plotting the Md stars in Orion, the stars piling up at ~400pc for Gaia parallax because Gaia parallax are good at such short distance while NN distance are everywhere and errorbar is huge.
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Here’s the thing. Are the AstroNN distance values questionable for all Apogee dwarf stars, given it’s focus on giants?
The large sample of GKd stars?
Not sure how this would impact any AstroNN dwarf generated metrics.
Looking more into GKd impact …
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Yes astroNN distances of dwarfs are generally questionable especially for stars with logg uncertainty>0.2dex. The focus of giants is because our goal to map the milkyway at a large distance and since this neural network works by predicting the luminosity of stars and we have approx 7% typical uncertainty in luminosity will be translated into approx 7% distance uncertainty, neural network that predicts luminosity (thus distance with apparent magnitude) probably can never outperform Gaia which uses geometric parallax at such a close distance.
Considering the target selection of APOGEE which dwarfs wont be selected at a far distance because they will be too dim to be selected (thus only giants are selected at a great distance if your goal is to map the MillkyWay in large volume anyway), I would always recommend to use Gaia parallax to get the distance to dwarfs in APOGEE even if astroNN produces reasonable distance to dwarfs, since Gaia geometric parallax will always be much better for them.
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from astronn.
Yes APOGEE DR17 will be using Gaia eDR3 and eDR3 parallax does improve quite a lot but neural network distance is still much better beyond a few kpc.
If you want Gaia eDR3 parallax with APOGEE DR16, you can use my script here to generate Gaia eDR3 data file row-matched to APOGEE allstar file: https://github.com/henrysky/astroNN_APOGEE_VAC/blob/master/2_gaia_xmatch.py
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Related Issues (15)
- Parrallel odeint integration wrt func or parameter HOT 2
- Galaxy-10 missing images HOT 1
- tensorflow 2.4.1 HOT 3
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- Loading Galaxy10 dataset HOT 3
- Keras's fit_generator failed when use_multiprocessing=True on WIndows only HOT 1
- Bugs in 3 of the demo_tutorial/NN_uncertainty_analysis HOT 1
- Current .h5 dataset loading mechanism is problematic
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- Weird errors raised by running the new accelerated BNN test() method HOT 2
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