Modern spectroscopic surveys can only target a small fraction of the vast
amount of photometrically cataloged sources in wide-field surveys. Here, we
report the development of a generative AI method capable of predicting optical
galaxy spectra from photometric broad-band images alone. This method draws from
the latest advances in diffusion models in combination with contrastive
networks. We pass multi-band galaxy images into the architecture to obtain
optical spectra. From these, robust values for galaxy properties can be derived
with any methods in the spectroscopic toolbox, such as standard population
synthesis techniques and Lick indices. When trained and tested on 64x64-pixel
images from the Sloan Digital Sky Survey, the global bimodality of star-forming
and quiescent galaxies in photometric space is recovered, as well as a
mass-metallicity relation of star-forming galaxies. The comparison between the
observed and the artificially created spectra shows good agreement in overall
metallicity, age, Dn4000, stellar velocity dispersion, and E(B-V) values.
Photometric redshift estimates of our generative algorithm can compete with
other current, specialized deep-learning techniques. Moreover, this work is the
first attempt in the literature to infer velocity dispersion from photometric
images. Additionally, we can predict the presence of an active galactic nucleus
up to an accuracy of 82%. With our method, scientifically interesting galaxy
properties, normally requiring spectroscopic inputs, can be obtained in future
data sets from large-scale photometric surveys alone. The spectra prediction
via AI can further assist in creating realistic mock catalogs.

开发了一种能够从光度宽频带图像中预测光学星系光谱的生成式人工智能方法，并通过与观测光谱的对比显示了良好的一致性，该方法还能推断光度图像中的速度色散和活跃星系核的存在，并可以在大规模光度调查中获得科学上有趣的星系属性。

无光谱的星系光谱学：条件扩散模型解读光度图像中的星系属性

Galaxy spectroscopy without spectra: Galaxy properties from photometric  images with conditional diffusion models

White dwarfs (WDs) polluted by exoplanetary material provide the
unprecedented opportunity to directly observe the interiors of exoplanets.
However, spectroscopic surveys are often limited by brightness constraints, and
WDs tend to be very faint, making detections of large populations of polluted
WDs difficult. In this paper, we aim to increase considerably the number of WDs
with multiple metals in their atmospheres. Using 96,134 WDs with Gaia DR3 BP/RP
(XP) spectra, we constructed a 2D map using an unsupervised machine learning
technique called Uniform Manifold Approximation and Projection (UMAP) to
organize the WDs into identifiable spectral regions. The polluted WDs are among
the distinct spectral groups identified in our map. We have shown that this
selection method could potentially increase the number of known WDs with 5 or
more metal species in their atmospheres by an order of magnitude. Such systems
are essential for characterizing exoplanet diversity and geology.

通过使用无监督机器学习技术 UMAP，我们构建了一个二维地图，将 Gaia DR3 BP/RP (XP) 光谱下的 96,134 颗白矮星组织成可识别的光谱区域，其中被污染的白矮星是我们地图中确定的一个独特光谱群体之一。我们展示了这种选择方法有可能将已知至少具有 5 种或更多金属物种在其大气层中存在的白矮星数量增加一个数量级，这对于表征外行星多样性和地质学是至关重要的。

使用 Gaia XP 光谱和无监督机器学习搜寻受污染的白矮星和其他宝藏

Hunting for Polluted White Dwarfs and Other Treasures with Gaia XP  Spectra and Unsupervised Machine Learning

We develop an automated technique for detecting damped Lyman-$\alpha$
absorbers (DLAs) along spectroscopic lines of sight to quasi-stellar objects
(QSOs or quasars). The detection of DLAs in large-scale spectroscopic surveys
such as SDSS-III sheds light on galaxy formation at high redshift, showing the
nucleation of galaxies from diffuse gas. We use nearly 50 000 QSO spectra to
learn a novel tailored Gaussian process model for quasar emission spectra,
which we apply to the DLA detection problem via Bayesian model selection. We
propose models for identifying an arbitrary number of DLAs along a given line
of sight. We demonstrate our method's effectiveness using a large-scale
validation experiment, with excellent performance. We also provide a catalog of
our results applied to 162 858 spectra from SDSS-III data release 12.

研究针对类星体光谱线探测阻尼 Lyman-alpha 吸收体的自动化技术，提供基于贝叶斯模型选择的新型高斯过程模型识别技术并在大规模数据集中验证，以揭示高红移率下星系形成的特点。

利用高斯过程检测阻尼 Lyman-$α$ 吸收体

Detecting Damped Lyman-$α$ Absorbers with Gaussian Processes

Flat space models with multiply connected topology, which have compact
dimensions, are tested against the distribution of high-redshift ($z \geq 4$)
quasars of the Sloan Digital Sky Survey (SDSS). When the compact dimensions are
smaller in size than the observed universe, topological lensing occurs, in
which multiple images of single objects (ghost images) are observed. We improve
on the recently introduced method to identify ghost images by means of
four-point statistics. Our method is valid for any of the 17 multiply connected
flat models, including nontoroial ones that are compactified by screw motions
or glide reflection. Applying the method to the data revealed one possible case
of topological lensing caused by sixth-turn screw motion, however, it is
consistent with the simply connected model by this test alone. Moreover,
simulations suggest that we cannot exclude the other space models despite the
absence of their signatures. This uncertainty mainly originates from the patchy
coverage of SDSS in the South Galactic cap, and the situation will be improved
by future wide-field spectroscopic surveys.

通过 Sloan Digital Sky Survey 的高红移类星体分布，测试具有紧致尺寸的多连通拓扑平面模型，并通过四点统计学方法识别出其中可能存在的拓扑透镜现象，然而仅仅通过该方法无法排除其他空间模型的存在，因此需要未来的广域光谱巡天。

在 Sloan Digital Sky Survey 类星体目录中寻找非环面拓扑透镜现象

A search for nontoroidal topological lensing in the Sloan Digital Sky  Survey quasar catalog

Building on the legacy of the Sloan Digital Sky Survey (SDSS-I and II),
SDSS-III is a program of four spectroscopic surveys on three scientific themes:
dark energy and cosmological parameters, the history and structure of the Milky
Way, and the population of giant planets around other stars. In keeping with
SDSS tradition, SDSS-III will provide regular public releases of all its data,
beginning with SDSS DR8 (which occurred in Jan 2011). This paper presents an
overview of the four SDSS-III surveys. BOSS will measure redshifts of 1.5
million massive galaxies and Lya forest spectra of 150,000 quasars, using the
BAO feature of large scale structure to obtain percent-level determinations of
the distance scale and Hubble expansion rate at z<0.7 and at z~2.5. SEGUE-2,
which is now completed, measured medium-resolution (R=1800) optical spectra of
118,000 stars in a variety of target categories, probing chemical evolution,
stellar kinematics and substructure, and the mass profile of the dark matter
halo from the solar neighborhood to distances of 100 kpc. APOGEE will obtain
high-resolution (R~30,000), high signal-to-noise (S/N>100 per resolution
element), H-band (1.51-1.70 micron) spectra of 10^5 evolved, late-type stars,
measuring separate abundances for ~15 elements per star and creating the first
high-precision spectroscopic survey of all Galactic stellar populations (bulge,
bar, disks, halo) with a uniform set of stellar tracers and spectral
diagnostics. MARVELS will monitor radial velocities of more than 8000 FGK stars
with the sensitivity and cadence (10-40 m/s, ~24 visits per star) needed to
detect giant planets with periods up to two years, providing an unprecedented
data set for understanding the formation and dynamical evolution of giant
planet systems. (Abridged)

SDSS-III 是一个包含四个光谱调查的计划，主要研究领域包括暗能量和宇宙学参数、银河历史和结构以及其他恒星周围的巨行星种群。