Related papers: 3D Detection and Characterisation of ALMA Sources …
We present 500 high-resolution, full-sky millimeter-wave Deep Learning (DL) simulations that include lensed CMB maps and correlated foreground components. We find that these MillimeterDL simulations can reproduce a wide range of…
The surface density of populations of galaxies with steep/shallow source counts is increased/decreased by gravitational lensing magnification. These effects are usually called `magnification bias' and `depletion' respectively. However, if…
Goals of this research phase is to investigate advanced detection and classification pardims useful for data-mining passive large passive acoustic archives. Technical objectives are to develop and refine a High Performance Computing,…
Deep Learning (DL) techniques are increasingly applied in scientific studies across various domains to address complex research questions. However, the methodological details of these DL models are often hidden in the unstructured text. As…
First hydrostatic cores are predicted by theories of star formation, but their existence has never been demonstrated convincingly by (sub)millimeter observations. Furthermore, the multiplicity at the early phases of the star formation…
In the operation & maintenance (O&M) of photovoltaic (PV) plants, the early identification of failures has become crucial to maintain productivity and prolong components' life. Of all defects, cell-level anomalies can lead to serious…
Characterizing protostellar outflows is fundamental to understanding star formation feedback, yet traditional methods are often hindered by projection effects and complex morphologies. We present a multi-modal deep learning framework that…
A deep learning approach based on big data is proposed to locate broadband acoustic sources using a single hydrophone in ocean waveguides with uncertain bottom parameters. Several 50-layer residual neural networks, trained on a huge number…
We investigate unsupervised anomaly detection for high-dimensional data and introduce a deep metric learning (DML) based framework. In particular, we learn a distance metric through a deep neural network. Through this metric, we project the…
We present sensitive high angular resolution ($\sim$ 0.1$''$ -- 0.3$''$) continuum ALMA (The Atacama Large Millimeter/Submillimeter Array) observations of the archetypal hot core located in Orion-KL. The observations were made in five…
We have updated and applied a convolutional neural network (CNN) machine learning model to discover and characterize damped Ly$\alpha$ systems (DLAs) based on Dark Energy Spectroscopic Instrument (DESI) mock spectra. We have optimized the…
Large language models (LLMs) show strong potential for neural architecture generation, yet existing approaches produce complete model implementations from scratch -- computationally expensive and yielding verbose code. We propose Delta-Code…
Line-intensity mapping (LIM) is an emerging observational technique that is used to observe the universe on large scales at low resolution through spectral line emission. Stacking analyses coadd cutouts of LIM data on positions of known…
We present an analysis of a deep (1$\sigma$=13 $\mu$Jy) cosmological 1.2-mm continuum map based on ASPECS, the ALMA Spectroscopic Survey in the Hubble Ultra Deep Field. In the 1 arcmin$^2$ covered by ASPECS we detect nine sources at…
We study the abundance of substructure in the matter density near galaxies using ALMA Science Verification observations of the strong lensing system SDP.81. We present a method to measure the abundance of subhalos around galaxies using…
Change detection (CD) methods have been applied to optical data for decades, while the use of hyperspectral data with a fine spectral resolution has been rarely explored. CD is applied in several sectors, such as environmental monitoring…
We consider the capabilities for detecting low order CO emission lines from high-z galaxies using the next generation of radio telescopes operating at 22 and 43 GHz. We employ models for the evolution of dusty star forming galaxies based on…
We present Atacama Large Millimeter/Submillimeter Array (ALMA) continuum observations of a sample of nine star-forming galaxies at redshifts 1.47 and 2.23 selected from the High-$z$ Emission Line Survey (HiZELS). Four galaxies in our sample…
Galaxy morphology is shaped by stellar activity, feedback, gas and dust properties, and interactions with surroundings, and can therefore provide insight into these processes. In this paper, we study the spatial offsets between stellar and…
Aiming at improving the performance of existing detection algorithms developed for different applications, we propose a region regression-based multi-stage class-agnostic detection pipeline, whereby the existing algorithms are employed for…