AstroNN Distances

The AstroNN distance pipeline estimates spectrophotometric distances to stars by predicting a “fake” absolute Ks-band magnitude from APOGEE spectra, then combining it with photometry and extinction information to compute a distance.

What it does

Given an APOGEE coadded spectrum and associated photometric metadata, this pipeline produces:

• A predicted Ks-band absolute luminosity (“fakemag”)

• A spectrophotometric distance (in parsecs)

• Extinction-related quantities

It operates on coadded APOGEE spectra (ApogeeCoaddedSpectrumInApStar).

How it works

Network architecture

The distance model uses a Bayesian Convolutional Neural Network (ApogeeDistBCNN) implemented in PyTorch. The architecture is simpler than the full AstroNN abundance network:

1. Two 1D convolutional layers (filter size 8, channels: 1 -> 2 -> 4)

2. Max pooling (pool length 4)

3. Two dense layers (7512 -> 192 -> 64)

4. Two output heads:

   • Prediction head: a dense layer with softplus activation that outputs the predicted fakemag

   • Variance head: a dense layer that outputs the log-variance of the prediction

The single output target is fakemag, a neural network-predicted Ks-band absolute magnitude.

Uncertainty estimation

Like the main AstroNN pipeline, this uses MC Dropout:

• Dropout (rate 0.3) is applied at inference time.

• The network is evaluated 100 times per spectrum.

• Total uncertainty combines the predictive variance with MC dropout variance.

Distance calculation

The distance is computed by combining:

1. fakemag: the predicted absolute Ks-band magnitude from the spectrum

2. k_mag: the apparent Ks-band magnitude from 2MASS photometry (from the source catalog)

3. E(B-V): reddening from the source catalog

4. A_K: Ks-band extinction, computed as A_K = E(B-V) * 0.3517

The extinction-corrected apparent magnitude is calculated, and the distance modulus is used to convert to a distance in parsecs via the astroNN.gaia.fakemag_to_pc utility.

Preprocessing

Spectra are continuum-normalized using astroNN.apogee.apogee_continuum (DR17 mode), the same normalization used in the main AstroNN pipeline.

Output fields

Field	Type	Description
k_mag	float	2MASS Ks-band apparent magnitude used
ebv	float	E(B-V) reddening value used
A_k_mag	float	Ks-band extinction (A_K)
L_fakemag	float	Predicted Ks-band absolute luminosity (fakemag)
e_L_fakemag	float	Uncertainty in fakemag
dist	float	Spectrophotometric distance (parsecs)
e_dist	float	Uncertainty in distance (parsecs)
result_flags	bitmask	Bitfield encoding quality flags

Flags

Flag	Bit	Description
flag_fakemag_unreliable	2^0	Predicted fakemag is unreliable (fakemag_err / fakemag >= 0.2)
flag_missing_photometry	2^1	Missing Ks-band apparent magnitude from source catalog
flag_missing_extinction	2^2	Missing extinction (E(B-V)) from source catalog
flag_no_result	2^11	Exception raised when loading spectra

Summary flags

• flag_warn: Set when flag_missing_extinction is set. Missing extinction alone is a warning because the distance can still be computed (with lower accuracy).

• flag_bad: Set when any of flag_fakemag_unreliable, flag_missing_photometry, or flag_no_result is set.

Caveats

• This pipeline requires ancillary data beyond the spectrum: Ks-band photometry from 2MASS and E(B-V) reddening values must be present in the source catalog. Missing photometry will cause the flag_missing_photometry flag to be set and the result to be marked as bad.

• Missing extinction is treated as a warning rather than a fatal error. The pipeline will substitute zero values for missing photometry or extinction, but the resulting distances should be treated with caution.

• Very large distance values (> 10^10 pc) are replaced with NaN.

• The extinction law used is: A_K = 0.3517 * E(B-V), derived from the relation A_K = 0.918 * E(B-V) / 2.61.

• The fakemag prediction uses a softplus activation, ensuring the predicted absolute magnitude is always positive.

• This pipeline only operates on coadded spectra, not individual visit spectra.