相关论文: Automated galaxy sizes in Euclid images using the …
The ACS Nearby Galaxy Survey Treasury (ANGST) is a systematic survey to establish a legacy of uniform multi-color photometry of resolved stars for a volume-limited sample of nearby galaxies (D<4 Mpc). The survey volume encompasses 69…
Medical imaging plays a critical role in the diagnosis and treatment planning of various medical conditions, with radiology and pathology heavily reliant on precise image segmentation. The Segment Anything Model (SAM) has emerged as a…
We present the results of the single-component S\'ersic profile fitting for the magnitude-limited sample of \IE$<23$ galaxies within the 63.1 deg$^2$ area of the Euclid Quick Data Release (Q1). The associated morphological catalogue…
We discuss the ability of the planned Euclid mission to detect deviations from General Relativity using its extensive redshift survey of more than 50 Million galaxies. Constraints on the gravity theory are placed measuring the growth rate…
We measure galaxy sizes on a sample of $\sim1200$ galaxies with confirmed spectroscopic redshifts $2 \leq z_{spec} \leq 4.5$ in the VIMOS Ultra Deep Survey (VUDS), representative of star-forming galaxies with $i_\mathrm{AB} \leq 25$. We…
We investigate the formation and early evolution and fragmentation of an accretion disk around a forming massive protostar. We use a grid-based self-gravity-radiation-hydrodynamics code including a sub-grid module for stellar and dust…
We investigate the resolved star formation properties of a sample of 45 massive galaxies (M_*>10^11M_solar) within a redshift range of 1.5 < z < 3 detected in the GOODS NICMOS Survey (Conselice et al. 2011), a HST H-band imaging program. We…
Present-day multi-wavelength deep imaging surveys allow to characterise the outskirts of galaxies with unprecedented precision. Taking advantage of this situation, we define a new physically motivated measurement of size for galaxies based…
A new method is developed for estimating photometric redshifts to galaxies, using realistic template SEDs, extending over four decades in wavelength (i.e. from 0.05 micron to 1 mm). The template SEDs are constructed for four different…
Spectroscopic surveys such as the Dark Energy Spectroscopic Instrument (DESI) and Euclid are mapping the spatial distribution of millions of galaxies, with Emission Line Galaxies (ELGs) serving as the dominant tracer in the redshift range…
Following the successful paradigm shift of large language models, leveraging pre-training on a massive corpus of data and fine-tuning on different downstream tasks, generalist models have made their foray into computer vision. The…
Medical image segmentation is a key task in the imaging workflow, influencing many image-based decisions. Traditional, fully-supervised segmentation models rely on large amounts of labeled training data, typically obtained through manual…
The Segment Anything Model (SAM) is a promptable segmentation model recently introduced by Meta AI that has demonstrated its prowess across various fields beyond just image segmentation. SAM can accurately segment images across diverse…
Tidal features are a key observable prediction of the hierarchical model of galaxy formation and contain a wealth of information about the properties and history of a galaxy. Modern wide-field surveys such as LSST and Euclid will…
The two-step galaxy morphology classification framework {\tt USmorph} successfully combines unsupervised machine learning (UML) with supervised machine learning (SML) methods. To enhance the UML step, we employed a dual-encoder architecture…
Improved understanding of gas/liquid transport in electrochemical gas-evolving systems is increasingly demanded for optimizing device performance. However, high-resolution measurement techniques for in-situ imaging remain limited. This work…
We use a cosmological hydrodynamic simulation calculated with Enzo and the semi-analytic galaxy formation model (SAM) GAMMA to address the chemical evolution of dwarf galaxies in the early universe. The long-term goal of the project is to…
Entering the era of large-scale galaxy surveys which will deliver unprecedented amounts of photometric and spectroscopic data, there is a growing need for more efficient, data driven, and less model-dependent techniques to analyze spectral…
Image-based crack detection algorithms are increasingly in demand in infrastructure monitoring, as early detection of cracks is of paramount importance for timely maintenance planning. While deep learning has significantly advanced crack…
In industrial anomaly detection, model efficiency and mobile-friendliness become the primary concerns in real-world applications. Simultaneously, the impressive generalization capabilities of Segment Anything (SAM) have garnered broad…