We present a new approach (MADE) that generates mass, age, and distance
estimates of red giant stars from a combination of astrometric, photometric,
and spectroscopic data. The core of the approach is a Bayesian artificial
neural network (ANN) that learns from and completely replaces stellar
isochrones. The ANN is trained using a sample of red giant stars with mass
estimates from asteroseismology. A Bayesian isochrone pipeline uses the
astrometric, photometric, spectroscopic, and asteroseismology data to determine
posterior distributions for the training outputs: mass, age, and distance.
Given new inputs, posterior predictive distributions for the outputs are
computed, taking into account both input uncertainties, and uncertainties in
the ANN parameters.
We apply MADE to $\sim10\,000$ red giants in the overlap between the
14$^{\mathrm{th}}$ data release from the APO Galactic Evolution Experiment
(APOGEE, Abolfathi et al. 2018) and the Tycho-Gaia astrometric solution (TGAS,
Michalik et al. 2015). The ANN is able to reduce the uncertainty on mass, age,
and distance estimates for training-set stars with high output uncertainties
allocated through the Bayesian isochrone pipeline. The fractional uncertainties
on mass are $<10\%$ and on age are between $10$ to $25\%$. Moreover, the time
taken for our ANN to predict masses, ages, and distances for the entire
catalogue of APOGEE-TGAS stars is of a similar order of the time taken by the
Bayesian isochrone pipeline to run on a handful of stars. Our resulting
catalogue clearly demonstrates the expected thick and thin disc components in
the [M/H]-[$\alpha$/M] plane, when examined by age.

用基于贝叶斯人工神经网络（ANN）的 MADE 方法通过对恒星的天文、光学、和光谱数据的组合生成红巨星的质量、年龄和距离的预测结果，对 10000 个红巨星进行了预测，准确地减少了与之相关联的不确定性，结果清晰表明了在年龄方面的预计厚与薄盘成分。

MADE: 基于贝叶斯机器学习的红巨星的光谱质量、年龄和距离估算器

MADE: A spectroscopic Mass, Age, and Distance Estimator for red giant  stars with Bayesian machine learning

Some recent works in conditional planning have proposed reachability
heuristics to improve planner scalability, but many lack a formal description
of the properties of their distance estimates. To place previous work in
context and extend work on heuristics for conditional planning, we provide a
formal basis for distance estimates between belief states. We give a definition
for the distance between belief states that relies on aggregating underlying
state distance measures. We give several techniques to aggregate state
distances and their associated properties. Many existing heuristics exhibit a
subset of the properties, but in order to provide a standardized comparison we
present several generalizations of planning graph heuristics that are used in a
single planner. We compliment our belief state distance estimate framework by
also investigating efficient planning graph data structures that incorporate
BDDs to compute the most effective heuristics.
We developed two planners to serve as test-beds for our investigation. The
first, CAltAlt, is a conformant regression planner that uses A* search. The
second, POND, is a conditional progression planner that uses AO* search. We
show the relative effectiveness of our heuristic techniques within these
planners. We also compare the performance of these planners with several state
of the art approaches in conditional planning.

提供了一种基于状态距离测量的置信状态距离估计框架，利用聚合状态距离度量的方法来定义置信状态之间的距离，实现了一些技术来聚合状态距离及其关联属性，提出了几种规划图启发式方法的广义形式，利用 BDD 来计算最有效的技巧，这些启发式方法在两个规划器上得到了有效应用。