Upcoming photometric surveys will discover tens of thousands of Type Ia
supernovae (SNe Ia), vastly outpacing the capacity of our spectroscopic
resources. In order to maximize the science return of these observations in the
absence of spectroscopic information, we must accurately extract key
parameters, such as SN redshifts, with photometric information alone. We
present Photo-zSNthesis, a convolutional neural network-based method for
predicting full redshift probability distributions from multi-band supernova
lightcurves, tested on both simulated Sloan Digital Sky Survey (SDSS) and Vera
C. Rubin Legacy Survey of Space and Time (LSST) data as well as observed SDSS
SNe. We show major improvements over predictions from existing methods on both
simulations and real observations as well as minimal redshift-dependent bias,
which is a challenge due to selection effects, e.g. Malmquist bias. The PDFs
produced by this method are well-constrained and will maximize the cosmological
constraining power of photometric SNe Ia samples.

采用卷积神经网络基于多波段超新星光变曲线预测完整的红移概率分布以解决光谱资源不足的问题，并在模拟数据和观测数据中均显示出比现有方法更好的性能，最大化了光度超新星样本的宇宙学约束能力。

基于深度学习的 Ia 型超新星光变曲线到红移估计的转换（Photo-zSNthesis）

Photo-zSNthesis: Converting Type Ia Supernova Lightcurves to Redshift  Estimates via Deep Learning

We perform a semi-automated search for strong gravitational lensing systems
in the 9,000 deg$^2$ Dark Energy Camera Legacy Survey (DECaLS), part of the
DESI Legacy Imaging Surveys (Dey et al.). The combination of the depth and
breadth of these surveys are unparalleled at this time, making them
particularly suitable for discovering new strong gravitational lensing systems.
We adopt the deep residual neural network architecture (He et al.) developed by
Lanusse et al. for the purpose of finding strong lenses in photometric surveys.
We compile a training set that consists of known lensing systems in the Legacy
Surveys and DES as well as non-lenses in the footprint of DECaLS. In this paper
we show the results of applying our trained neural network to the cutout images
centered on galaxies typed as ellipticals (Lang et al.) in DECaLS. The images
that receive the highest scores (probabilities) are visually inspected and
ranked. Here we present 335 candidate strong lensing systems, identified for
the first time.

本文使用神经网络在 DECaLS 图像中鉴定出 335 个强引力透镜系统作为候选物体。

DESI DECam Legacy Survey 中强引力透镜的发现

Finding Strong Gravitational Lenses in the DESI DECam Legacy Survey

In the era of large photometric surveys, the importance of automated and
accurate classification is rapidly increasing. Specifically, the separation of
resolved and unresolved sources in astronomical imaging is a critical initial
step for a wide array of studies, ranging from Galactic science to large scale
structure and cosmology. Here, we present our method to construct a large, deep
catalog of point sources utilizing Pan-STARRS1 (PS1) 3$\pi$ survey data, which
consists of $\sim$3$\times10^9$ sources with $m\lesssim23.5\,$mag. We develop a
supervised machine-learning methodology, using the random forest (RF)
algorithm, to construct the PS1 morphology model. We train the model using
$\sim$5$\times10^4$ PS1 sources with HST COSMOS morphological classifications
and assess its performance using $\sim$4$\times10^6$ sources with Sloan Digital
Sky Survey (SDSS) spectra and $\sim$2$\times10^8$ \textit{Gaia} sources. We
construct 11 "white flux" features, which combine PS1 flux and shape
measurements across 5 filters, to increase the signal-to-noise ratio relative
to any individual filter. The RF model is compared to 3 alternative models,
including the SDSS and PS1 photometric classification models, and we find that
the RF model performs best. By number the PS1 catalog is dominated by faint
sources ($m\gtrsim21\,$mag), and in this regime the RF model significantly
outperforms the SDSS and PS1 models. For time-domain surveys, identifying
unresolved sources is crucial for inferring the Galactic or extragalactic
origin of new transients. We have classified $\sim$1.5$\times10^9$ sources
using the RF model, and these results are used within the Zwicky Transient
Facility real-time pipeline to automatically reject stellar sources from the
extragalactic alert stream.

使用监督式机器学习方法生成的 PS1 形态模型，通过 PS1 3 π 调查数据中的白光流量特征，成功区分了可视天文成像中的已解析和未解析光源，并在星系暴发事件的实时处理中实现了恒星光源的自动排除。