GalSpecCNN: Deep Learning Estimates of Fiber Stellar Mass and Specific Star Formation Rate with Uncertainty for LAMOST Galaxies

We present GalSpecCNN, a probabilistic deep-learning framework for estimating fiber-scale stellar mass (M*) and specific star formation rate (sSFR) from LAMOST galaxy spectra. The model uses a one-dimensional convolutional neural network to extract spectral features, followed by a probabilistic regression head that predicts a diagonal bivariate Gaussian distribution. Aleatoric uncertainty is modeled through the predicted variances, while epistemic uncertainty is approximated using Monte Carlo Dropout. On the test set, GalSpecCNN achieves residual scatters of 0.226 dex for M* and 0.319 dex for sSFR, with small mean residuals. The empirical coverages of the nominal 68% predictive intervals are 68.60% for M* and 71.58% for sSFR, indicating good overall calibration with mildly conservative coverage for sSFR. We apply the trained model to approximately 1.16×105 LAMOST DR7 galaxy spectra and construct a catalog of M* and sSFR estimates, together with their associated predictive intervals. This work provides a practical framework for uncertainty-aware estimation of galaxy physical properties from large spectroscopic surveys.