相关论文: Learning Intrinsic Alignments from Local Galaxy En…
We present DeepVoid, an application of deep learning trained on a physical definition of cosmic voids to detect voids in density fields and galaxy distributions. By semantically segmenting the IllustrisTNG simulation volume using the tidal…
Current large-scale astrophysical experiments produce unprecedented amounts of rich and diverse data. This creates a growing need for fast and flexible automated data inspection methods. Deep learning algorithms can capture and pick up…
As we enter the era of large imaging surveys such as $\textit{Roman}$, Rubin, and $\textit{Euclid}$, a deeper understanding of potential biases and selection effects in optical astronomical catalogs created with the use of ML-based methods…
To date, galaxy image simulations for weak lensing surveys usually approximate the light profiles of all galaxies as a single or double S\'ersic profile, neglecting the influence of galaxy substructures and morphologies deviating from such…
Intrinsically aligned galaxy shapes are one of the most important systematics in cosmic shear measurements. So far theoretical studies of intrinsic alignments almost exclusively focus on their statistics at the two-point level. Results from…
We have designed, developed, and applied a convolutional neural network (CNN) architecture using multi-task learning to search for and characterize strong HI Lya absorption in quasar spectra. Without any explicit modeling of the quasar…
Automated characterization of galactic substructure is an essential step in understanding the transformative physical processes driving galaxy evolution. In this study, we investigate the application of deep learning (DL) frameworks to…
Temporal graph neural networks have shown promising results in learning inductive representations by automatically extracting temporal patterns. However, previous works often rely on complex memory modules or inefficient random walk methods…
The Laser Interferometer Space Antenna (LISA) will observe gravitational waves in the millihertz frequency band, detecting signals from a vast number of astrophysical sources embedded in instrumental noise. Extracting individual signals…
Upcoming surveys are predicted to discover galaxy-scale strong lenses on the order of $10^5$, making deep learning methods necessary in lensing data analysis. Currently, there is insufficient real lensing data to train deep learning…
End-to-end autonomous driving models based on Vision-Language-Action (VLA) architectures have shown promising results by learning driving policies through behavior cloning on expert demonstrations. However, imitation learning inherently…
To reach the full potential for the next generation of weak lensing surveys, it is necessary to mitigate the contamination of intrinsic alignments (IA) of galaxies in the observed cosmic shear signal. The self calibration (SC) of intrinsic…
Intrinsic alignments (IAs) of galaxies are an important contaminant for cosmic shear studies, but the modelling is complicated by the dependence of the signal on the source galaxy sample. In this paper, we use the halo model formalism to…
Hydrodynamical simulations play a fundamental role in modern cosmological research, serving as a crucial bridge between theoretical predictions and observational data. However, due to their computational intensity, these simulations are…
We describe a case study of translational research, applying interpretability techniques developed for computer vision to machine learning models used to search for and find gravitational waves. The models we study are trained to detect…
We present a new method for measuring the scale dependence of the intrinsic alignment (IA) contamination to the galaxy-galaxy lensing signal, which takes advantage of multiple shear estimation methods applied to the same source galaxy…
We explore the capability of deep learning to classify cosmic structures. In cosmological simulations, cosmic volumes are segmented into voids, sheets, filaments and knots, according to the distribution and kinematics of dark matter (DM),…
Data assimilation (DA) is a cornerstone of scientific and engineering applications, combining model forecasts with sparse and noisy observations to estimate latent system states. Classical high-dimensional DA methods, such as the ensemble…
The intrinsic alignment (IA) of galaxies has been shown to be a significant barrier to precision cosmic shear measurements. (Zhang, 2010, ApJ, 720, 1090) proposed a self-calibration technique for the power spectrum to calculate the induced…
Deep learning (DL) has achieved great success in many applications, but it has been less well analyzed from the theoretical perspective. The unexplainable success of black-box DL models has raised questions among scientists and promoted the…