Data Cube Research Articles

Median surface-brightness profiles of Lyman-alpha haloes in the MUSE Extremely Deep Field

We present the median surface brightness profiles of diffuse haloes (LAHs) around star-forming galaxies by stacking 155 spectroscopically confirmed emitters (LAEs) at $3<z<4$ in the MUSE Extremely Deep Field (MXDF) with a median luminosity of $ L_ Ly alpha $. After correcting for a systematic surface brightness offset we identified in the data cube, we detect extended emission out to a distance of approx 270 kpc. The median surface-brightness profile shows a power-law decrease in the inner 20 kpc and a possible flattening trend at a greater distance. This shape is similar for LAEs with different luminosities, but the normalisation of the surface-brightness profile increases with luminosity. At distances over 50 kpc, we observe a strong overlap of adjacent LAHs, and the surface brightness is dominated by the LAHs of nearby LAEs. We find no clear evidence of redshift evolution of the observed profiles when comparing with samples at $4<z<5$ and $5<z<6$. Our results are consistent with a scenario in which the inner 20 kpc of the LAH is powered by star formation in the central galaxy, while the LAH beyond a radius of 50 kpc is dominated by photons from surrounding galaxies.

Spectral similarities in galaxies through an unsupervised classification of spaxels

We present the first unsupervised classification of spaxels in hyperspectral images of individual galaxies. Classes identify regions by spectral similarity and thus take all the information into account that is contained in the data cubes (spatial and spectral). We used Gaussian mixture models in a latent discriminant subspace to find clusters of spaxels. The spectra were corrected for small-scale motions within the galaxy based on emission lines with an automatic algorithm. Our data consist of two MUSE/VLT data cubes of JKB 18 and NGC 1068 and one NIRSpec/JWST data cube of NGC 4151. Our classes identify many regions that are most often easily interpreted. Most of the 11 classes that we find for JKB 18 are identified as photoionised by stars. Some of them are known regions, but we mapped them as extended, with gradients of ionisation intensities. One compact structure has not been reported before, and according to diagnostic diagrams, it might be a planetary nebula or a denser region. For NGC 1068, our 16 classes are of active galactic nucleus-type (AGN) or star-forming regions. Their spatial distribution corresponds perfectly to well-known structures such as spiral arms and a ring with giant molecular clouds. A subclassification in the nuclear region reveals several structures and gradients in the AGN spectra. Our unsupervised classification of the MUSE data of NGC 1068 helps visualise the complex interaction of the AGN and the jet with the interstellar medium in a single map. The centre of NGC 4151 is very complex, but our classes can easily be related to ionisation cones, the jet, or H$_2$ emission. We find a new elongated structure that is ionised by the AGN along the N-S axis perpendicular to the jet direction. It is rotated counterclockwise with respect to the axis of the H$_2$ emission. Our work shows that the unsupervised classification of spaxels takes full advantage of the richness of the information in the data cubes by presenting the spectral and spatial information in a combined and synthetic way.

The Moments Method for Approximate Data Cube Queries

We investigate an approximation algorithm for various aggregate queries on partially materialized data cubes. Data cubes are interpreted as probability distributions, and cuboids from a partial materialization populate the terms of a series expansion of the target query distribution. Unknown terms in the expansion are just assumed to be 0 in order to recover an approximate query result. We identify this method as a variant of related approaches from other fields of science, that is, the Bahadur representation and, more generally, (biased) Fourier expansions of Boolean functions. Existing literature indicates a rich but intricate theoretical landscape. Focusing on the data cube application, we start by investigating worst-case error bounds. We build upon prior work to obtain provably optimal materialization strategies with respect to query workloads. In addition, we propose a new heuristic method governing materialization decisions. Finally, we show that well-approximated queries are guaranteed to have well-approximated roll-ups.

Image Data Stream Organization and Online Analysis Application Based on Data Cube Technology

Abstract. This study aims to explore the important role of data-like cube structures in modern remote sensing data processing and data analysis through ArcPy and Python multiprocessing techniques. A multi-scale spatial data cube is innovatively developed to improve the efficiency of remote sensing data management and optimize data analysis. The core of this study is to define and implement grid cells of different sizes that form the basis of data cube, and to quantify the efficient coverage of specific areas using Python multiprocessing techniques. Experiments were conducted in Hainan Province, and efficient data coverage of the whole Hainan Province was realized using the grid data method, which significantly reduced the amount of remote sensing data and processing time required. This shows that the method has successfully improving data coverage capacity and utilization efficiency. The results of this study not only demonstrate the effective application of data-like cubes in remote sensing data processing and analysis, but also provide new perspectives and methods for future complex spatial data analysis and large-scale remote sensing data processing.

Hyperspectral dark-field microscopy of human breast lumpectomy samples for tumor margin detection in breast-conserving surgery.

Hyperspectral dark-field microscopy (HSDFM) and data cube analysis algorithms demonstrate successful detection and classification of various tissue types, including carcinoma regions in human post-lumpectomy breast tissues excised during breast-conserving surgeries. We expand the application of HSDFM to the classification of tissue types and tumor subtypes in pre-histopathology human breast lumpectomy samples. Breast tissues excised during breast-conserving surgeries were imaged by the HSDFM and analyzed. The performance of the HSDFM is evaluated by comparing the backscattering intensity spectra of polystyrene microbead solutions with the Monte Carlo simulation of the experimental data. For classification algorithms, two analysis approaches, a supervised technique based on the spectral angle mapper (SAM) algorithm and an unsupervised technique based on the -means algorithm are applied to classify various tissue types including carcinoma subtypes. In the supervised technique, the SAM algorithm with manually extracted endmembers guided by H&E annotations is used as reference spectra, allowing for segmentation maps with classified tissue types including carcinoma subtypes. The manually extracted endmembers of known tissue types and their corresponding threshold spectral correlation angles for classification make a good reference library that validates endmembers computed by the unsupervised -means algorithm. The unsupervised -means algorithm, with no a priori information, produces abundance maps with dominant endmembers of various tissue types, including carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma. The two carcinomas' unique endmembers produced by the two methods agree with each other within residual error margin. Our report demonstrates a robust procedure for the validation of an unsupervised algorithm with the essential set of parameters based on the ground truth, histopathological information. We have demonstrated that a trained library of the histopathology-guided endmembers and associated threshold spectral correlation angles computed against well-defined reference data cubes serve such parameters. Two classification algorithms, supervised and unsupervised algorithms, are employed to identify regions with carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma present in the tissues. The two carcinomas' unique endmembers used by the two methods agree to residual error margin. This library of high quality and collected under an environment with no ambient background may be instrumental to develop or validate more advanced unsupervised data cube analysis algorithms, such as effective neural networks for efficient subtype classification.

The SARAO MeerKAT 1.3 GHz Galactic Plane Survey

Abstract We present the SARAO MeerKAT Galactic Plane Survey (SMGPS), a 1.3 GHz continuum survey of almost half of the Galactic Plane (251○ ≤l ≤ 358○ and 2○ ≤l ≤ 61○ at |b| ≤ 1${_{.}^{\circ}}$5). SMGPS is the largest, most sensitive and highest angular resolution 1 GHz survey of the Plane yet carried out, with an angular resolution of 8″ and a broadband RMS sensitivity of ∼10–20 μJy beam−1. Here we describe the first publicly available data release from SMGPS which comprises data cubes of frequency-resolved images over 908–1656 MHz, power law fits to the images, and broadband zeroth moment integrated intensity images. A thorough assessment of the data quality and guidance for future usage of the data products are given. Finally, we discuss the tremendous potential of SMGPS by showcasing highlights of the Galactic and extragalactic science that it permits. These highlights include the discovery of a new population of non-thermal radio filaments; identification of new candidate supernova remnants, pulsar wind nebulae and planetary nebulae; improved radio/mid-IR classification of rare Luminous Blue Variables and discovery of associated extended radio nebulae; new radio stars identified by Bayesian cross-matching techniques; the realisation that many of the largest radio-quiet WISE H ii region candidates are not true H ii regions; and a large sample of previously undiscovered background H i galaxies in the Zone of Avoidance.

Surveying image segmentation approaches in astronomy

Image segmentation plays a critical role in unlocking the mysteries of the universe, providing astronomers with a clearer perspective on celestial objects within complex astronomical images and data cubes. Manual segmentation, while traditional, is not only time-consuming but also susceptible to biases introduced by human intervention. As a result, automated segmentation methods have become essential for achieving robust and consistent results in astronomical studies. This review begins by summarizing traditional and classical segmentation methods widely used in astronomical tasks. Despite the significant improvements these methods have brought to segmentation outcomes, they fail to meet astronomers’ expectations, requiring additional human correction, further intensifying the labor-intensive nature of the segmentation process. The review then focuses on the transformative impact of machine learning, particularly deep learning, on segmentation tasks in astronomy. It introduces state-of-the-art machine learning approaches, highlighting their applications and the remarkable advancements they bring to segmentation accuracy in both astronomical images and data cubes. As the field of machine learning continues to evolve rapidly, it is anticipated that astronomers will increasingly leverage these sophisticated techniques to enhance segmentation tasks in their research projects. In essence, this review serves as a comprehensive guide to the evolution of segmentation methods in astronomy, emphasizing the transition from classical approaches to cutting-edge machine learning methodologies. We encourage astronomers to embrace these advancements, fostering a more streamlined and accurate segmentation process that aligns with the ever-expanding frontiers of astronomical exploration.

SPICE connection mosaics to link the Sun’s surface and the heliosphere

Aims. We present an analysis of the first connection mosaic made by the SPICE instrument on board the ESA/NASA Solar Orbiter mission on March 2, 2022. The data will be used to map coronal composition that will be compared with in-situ measurements taken by SWA/HIS to establish the coronal origin of the solar wind plasma observed at Solar Orbiter. The SPICE spectral lines were chosen to have varying sensitivity to the first ionization potential (FIP) effect, and therefore the radiances of the spectral lines will vary significantly depending on whether the elemental composition is coronal or photospheric. We investigate the link between the behavior of sulfur and the hypothesis that Alfvén waves drive FIP fractionation above the chromosphere. Methods. We performed temperature diagnostics using line ratios and emission measure (EM) loci, and computed relative FIP biases using three different approaches (two-line ratio (2LR), ratios of linear combinations of spectral lines (LCR), and differential emission measure (DEM) inversion) in order to perform composition diagnostics in the corona. We then compared the SPICE composition analysis and EUI data of the potential solar wind source regions to the SWA/HIS data products. Results. Radiance maps were extracted from SPICE spectral data cubes, with values matching previous observations. We find isothermal plasma of around log T = 5.8 for the AR loops targeted, and that higher FIP-bias values are present at the footpoints of the coronal loops associated with two ARs. Comparing the results with the SWA/HIS data products encourages us to think that Solar Orbiter was connected to a source of slow solar wind during this observation campaign. We demonstrate FIP fractionation in observations of the upper chromosphere and transition region, emphasized by the behavior of the intermediate-FIP element sulfur.

Gas-dynamical Mass Measurements of the Supermassive Black Holes in the Early-type Galaxies NGC 4786 and NGC 5193 from ALMA and HST Observations* *Based on observations made with the NASA/ESA Hubble Space Telescope, obtained at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS5-26555. These observations are associated

We present molecular gas-dynamical mass measurements of the central black holes in the giant elliptical galaxies NGC 4786 and NGC 5193, based on CO (2−1) observations from the Atacama Large Millimeter/submillimeter Array (ALMA) and Hubble Space Telescope near-infrared imaging. The central region in each galaxy contains a circumnuclear disk that exhibits orderly rotation with projected line-of-sight velocities of ∼270 km s−1. We build gas-dynamical models for the rotating disk in each galaxy and fit them directly to the ALMA data cubes. At 0.″31 resolution, the ALMA observations do not fully resolve the black hole sphere of influence (SOI), and neither galaxy exhibits a central rise in rotation speed, indicating that emission from deep within the SOI is not detected. As a result, our models do not tightly constrain the central black hole mass in either galaxy, but they prefer the presence of a central massive object in both galaxies. We measure the black hole mass to be (MBH/108M⊙)=5.0±0.2[1σstatistical]−1.3+1.4[systematic] in NGC 4786 and (MBH/108M⊙)=1.4±0.03[1σstatistical]−0.1+1.5[systematic] in NGC 5193. The largest component of each measurement’s error budget is from the systematic uncertainty associated with the extinction correction in the host galaxy models. This underscores the importance of assessing the impact of dust attenuation on the inferred M BH.

SPACE+ DATA DASHBOARD: EMPOWERING INSTITUTIONS AND CITIZENS WITH SEAMLESS SPACE DATA ACCESS

Abstract. This paper presents the Space+ Data Dashboard (SDD), a robust platform developed by the Philippine Space Agency (PhilSA), designed to revolutionize space data access for institutions and citizens. Built using open-source technologies, the SDD seamlessly manages spatial and non-spatial datasets. The SDD is composed of three core components: backend, frontend, and database. The backend uses PostgreSQL with PostGIS for data storage and indexing, Geoserver for vector files, and the Open Data Cube for raster files. Django handles other data types and serves API requests. The front-end uses TerriaMap, a customizable web front-end built on TerriaJS to provide public access and geo-visualization, and ReactJS for user interface and interaction. The SDD is envisioned to fulfill PhilSA’s mandate to empower government institutions and the Filipino people using space technologies. It democratizes data access to facilitate the use of space + geospatial data in disaster management, urban planning, environmental monitoring, and informed decision-making. The dashboard also offers direct downloads of various space-derived datasets. In championing data accessibility, PhilSA ensures data-driven decision-making, advancing both national space initiatives and the United Nations' Sustainable Development Goals.

SUSHI: An algorithm for source separation of hyperspectralimages with non-stationary spectral variation. Semi-blind Unmixing with Sparsity for Hyperspectral Images

Hyperspectral images are data cubes with two spatial dimensions and a third spectral dimension, providing a spectrum for each pixel, and thus allowing the mapping of extended sources' physical properties. In this article, we present the Semi-blind Unmixing with Sparsity for Hyperspectral Images (SUSHI), an algorithm for non-stationary unmixing of hyperspectral images with spatial regularization of spectral parameters. The method allows for the disentangling of physical components without the assumption of a unique spectrum for each component. Thus, unlike most source separation methods used in astrophysics, all physical components obtained by SUSHI vary in spectral shape and in amplitude across the data cube. Non-stationary source separation is an ill-posed inverse problem that needs to be constrained. We achieve this by training a spectral model and applying a spatial regularization constraint on its parameters. For the spectral model, we used an Interpolatory Auto-Encoder, a generative model that can be trained with limited samples. For spatial regularization, we applied a sparsity constraint on the wavelet transform of the model parameter maps. We applied SUSHI to a toy model meant to resemble supernova remnants in X-ray astrophysics, though the method may be used on any extended source with any hyperspectral instrument. We compared this result to the one obtained by a classic 1D fit on each individual pixel. We find that SUSHI obtains more accurate results, particularly when it comes to reconstructing physical parameters. We then applied SUSHI to real X-ray data from the supernova remnant Cassiopeia A and to the Crab Nebula. The results obtained are realistic and in accordance with past findings but have a much better spatial resolution. Thanks to spatial regularization, SUSHI can obtain reliable physical parameters at fine scales that are out of reach for pixel-by-pixel methods.

Enhancing Diagnostic Capabilities for Occupational Lung Diseases Using LIBS Imaging on Biopsy Tissue.

Laser-induced breakdown spectroscopy (LIBS) imaging continues to gain strength as an influential bioanalytical technique, showing intriguing potential in the field of clinical analysis. This is because hyperspectral LIBS imaging allows for rapid, comprehensive elemental analysis, covering elements from major to trace levels consistently year after year. In this study, we estimated the potential of a multivariate spectral data treatment approach based on a so-called convex envelope method to detect exotic elements (whether they are minor or in trace amounts) in biopsy tissues of patients with occupational exposure-related diseases. More precisely, we have developed an approach called Interesting Features Finder (IFF), which initially allowed us to identify unexpected elements without any preconceptions, considering only the set of spectra contained in a LIBS hyperspectral data cube. This task is, in fact, almost impossible with conventional chemometric tools, as it entails identifying a few exotic spectra among several hundred thousand others. Once this detection was performed, a second approach based on correlation was used to locate their distribution in the biopsies. Through this unique data analysis pipeline to processing massive LIBS spectroscopic data, it was possible to detect and locate exotic elements such as tin and rhodium in a patient's tissue section, ultimately leading to a possible reclassification of their lung condition as an occupational disease. This review will thus demonstrate the potential of this new diagnostic tool based on LIBS imaging in addressing the shortcomings of approaches developed thus far. The proposed data processing approach naturally transcends this specific framework and can be leveraged across various domains of analytical chemistry, where the detection of rare events is concealed within extensive data sets.

An Automated Chemical Exploration of NGC 6334I at 340 au Resolution

Much of the information gleaned from observations of star-forming regions comes from the analysis of their molecular emission spectra, particularly in the radio regime. The time-consuming nature of fitting synthetic spectra to observations interactively for such line-rich sources, however, often results in such analysis being limited to data extracted from a single-dish observation or a handful of pixels from an interferometric observation. Yet, star-forming regions display a wide variety of physical conditions that are difficult, if not impossible, to accurately characterize with such a limited number of spectra. We have developed an automated fitting routine that visits every pixel in the field of view of an Atacama Large Millimeter/submillimeter Array (ALMA) data cube and determines the best-fit physical parameters, including excitation temperature and column densities, for a given list of molecules. In this proof-of-concept work, we provide an overview of the fitting routine and apply it to 0.″26, 1.1 km s−1 resolution ALMA observations of two sites of massive star formation in NGC 6334I. Parameters were found for 21 distinct molecules by generating synthetic spectra across 7.48 GHz of spectral bandwidth between 280 and 351 GHz. Spatial images of the derived parameters for each of the >8000 pixels are presented with special attention paid to the C2H4O2 isomers and their relative variations. We highlight the greater scientific utility of the column density and velocity images of individual molecules compared to traditional moment maps of single transitions.

Differential optical transfer function (dOTF) broad-spectrum wavefront sensing using integral field unit spectroscopy

Context. Detecting and characterizing Earth-like exoplanets is a critical scientific goal for the next generation of telescopes. However, current direct imaging instruments are hindered by evolving noncommon path aberrations (NCPAs), which lead to persistent speckles in the images. The differential optical transfer function (dOTF) is an image-based, noniterative, and model-independent wavefront sensor that combines differential images to estimate the complex field of the pupil. Aims. Given the objective of spectrally characterizing exoplanets to discern their composition and potentially detect biosignatures, we aim to explore the integration of the dOTF sensing method with an integral field unit (IFU) spectrograph. An IFU spectrograph generates a spectral data cube that contains diverse spectral information within a single dataset. Combining these two concepts is expected to improve the efficiency with which NCPAs can be spectrally measured and characterized. Methods. This Letter presents an implementation strategy for integrating dOTF and IFU spectroscopy data, enabling estimations of the electric field at various wavelengths simultaneously. Results. This method is specifically optimized to provide the most effective combination with minimal hardware requirements. It involves the use of multiple simultaneous deformable mirror actuator displacements in conjunction with an adapted Lyot stop employing a circular variable filter. Conclusions. The dOTF wavefront sensor coupled with an IFU spectrograph promises to be rapid and efficient, capable of measuring NCPAs across the entire spectrum provided by the spectrograph. Because the combination of dOTF and IFU spectroscopy is immediately applicable, it is of potential interest for various present-day on-sky instruments, as well as future IFU-based instruments coupled to the next generation of extremely large telescopes (ELTs).

Examining Spatiotemporal Photosynthetic Vegetation Trends in Djibouti Using Fractional Cover Metrics in the Digital Earth Africa Open Data Cube

The Horn of Africa has sensitive, arid ecosystems, with its vegetation commonly distressed by factors such as climate change, population increase, unstable water resources, and rarely enforced land use management practices. These factors make countries such as Djibouti highly variable locations for the growth of vegetation and agricultural products, and these countries are becoming more vulnerable to food insecurity as the climate warms. The rapid growth of satellite and digital image processing technology over the last five decades has improved our ability to track long-term agricultural and vegetation changes. Data cubes are a newer approach to managing satellite imagery and studying temporal patterns. Here, we use the cloud-based Digital Earth Africa, Open Data Cube to analyze 30 years of Landsat imagery and orthomosaics. We analyze long-term trends in vegetation dynamics by comparing annual fractional cover metrics (photosynthetic vegetation, non-photosynthetic vegetation, and bare ground) to the Normalized Difference Vegetation Index. Investigating Djibouti-wide and regional vegetation trends, we provide a comparison of trends between districts and highlight a primary agricultural region in the southeast as a detailed example of vegetation change. The results of the Sen’s slope and Mann–Kendall regression analyses of the data cube suggest a significant decline in vegetation (p = 0.00002), equating to a loss of ~0.09 km2 of arable land per year (roughly 2.7 km2 over the 30-year period). Overall, decreases in photosynthetic vegetation and increases in both non-photosynthetic vegetation and bare soil areas indicate that the region is becoming more arid and that land cover is responding to this trend.

Isopach Map of Mishrif Formation in Nasiriya Oil Field by Using 3D Seismic Data

The current study included interpretations of the three-dimensional seismic Data for the Nasiriya oil field implemented by the Oil exploration company in 2010 with a planned surface coverage area of 1273 km². The Nasiriyah field is located in southern Iraq within the Dhi Qar Governorate, about (10) km northwest of the governorate center (Nasiriyah), and the Muthanna governorate is located in the northwestern part of the field, which is approximately (15) km west of the governorate center (Samawah). The information from the wells (well velocity survey, formation depths, logs) was relied upon and entered into the seismic cube data to capture the top of the Mishrif Formation. In addition is to pick the top of the Rumaila Formation, which represents the bottom of the Mishrif Formation. The top and bottom Mishrif reflectors were picked, interpreted, and tracked within the region. The reflector's two-way time (TWT), velocity, and depth maps showed structural closure features in the center of the area, and the general direction of the closure is thenorthwest–southeast. The structural maps showed a general tendency towards the northeast. The isopach map showed that the thickness values of the Mishrif Formation increased to the west and northwest while the thickness decreased toward the east and southeast.

Cube query interestingness: Novelty, relevance, peculiarity and surprise

In this paper, we discuss methods to assess the interestingness of a query in an environment of data cubes. We assume a hierarchical multidimensional database, storing data cubes and level hierarchies. We start with a comprehensive review of related work in the fields of human behavior studies and computer science. We define the interestingness of a query as a vector of scores along different aspects, like novelty, relevance, surprise and peculiarity and complement this definition with a taxonomy of the information that can be used to assess each of these aspects of interestingness. We provide both syntactic (result-independent) and extensional (result-dependent) checks, measures and algorithms for assessing the different aspects of interestingness in a quantitative fashion. We also report our findings from a user study that we conducted, analyzing the significance of each aspect, its evolution over time and the behavior of the study’s participants.

Time-first approach for land cover mapping using big Earth observation data time-series in a data cube – a case study from the Lake Geneva region (Switzerland)

ABSTRACT Accurate, consistent, and high-resolution Land Use & Cover (LUC) information is fundamental for effectively monitoring landscape dynamics and better apprehending drivers, pressures, state, and impacts on land systems. Nevertheless, the availability of such national products with high thematic accuracy is still limited and consequently researchers and policymakers are constrained to work with data that do not necessarily reflect on-the-ground realities impending to correctly capture details of landscape features as well as limiting the identification and quantification of drivers and rate of change. Hereafter, we took advantage of the Switzerland’s official LUC statistical sampling survey and dense time-series of Sentinel-2 data, combining them with Machine and Deep Learning methods to produce an accurate and high spatial resolution land cover map over the Lake Geneva region. Findings suggest that time-first approach is a valuable alternative to space-first approaches, accounting for the intra-annual variability of classes, hence improving classification performances. Results demonstrate that Deep Learning methods outperform more traditional Machine Learning ones such as Random Forest, providing more accurate predictions with lower uncertainty. The produced land cover map has a high accuracy, an improved spatial resolution, while at the same time preserving the statistical significance (i.e. class proportion) of the official national dataset. This work paves the way towards the objective to produce a yearly high resolution land cover map of Switzerland and potentially implement a continuous land change monitoring capability. However further work is required to properly address challenges such as the need for increased temporal resolution for LUC information or the quality of training samples.

Method for GPU-based spectral data cube reconstruction of integral field snapshot imaging spectrometers.

In this paper, the principles of spectral data cube reconstruction based on an integral field snapshot imaging spectrometer and GPU-based acceleration are presented. The primary focus is on improving the reconstruction algorithm using GPU parallel computing technology to enhance the computational efficiency for real-time applications. And the computational tasks of the spectral reconstruction algorithm were transferred to the GPU through program parallelization and memory optimization, resulting in significant performance gains. Experimental results indicate that the average processing time of the GPU-based parallel algorithm is approximately 29.43 ms, showing a substantial acceleration ratio of about 14.27 compared to the traditional CPU serial algorithm with an average processing time of around 420.46 ms. The study aims to refine the GPU parallelization algorithm for continued improvement in computational efficiency and overall performance. The anticipated applications of this research include providing crucial technical support for the perception and monitoring of crop growth traits in agricultural production, contributing to the modernization and advancement of intelligence in the field.

MIGHTEE-H i: H i galaxy properties in the large-scale structure environment at z ∼ 0.37 from a stacking experiment

ABSTRACT We present the first measurement of H i mass of star-forming galaxies in different large scale structure environments from a blind survey at z ∼ 0.37. In particular, we carry out a spectral line stacking analysis considering 2875 spectra of colour-selected star-forming galaxies undetected in H i at 0.23 < z < 0.49 in the COSMOS field, extracted from the MIGHTEE-H i Early Science data cubes, acquired with the MeerKAT radio telescope. We stack galaxies belonging to different subsamples depending on three different definitions of large-scale structure environment: local galaxy overdensity, position inside the host dark matter halo (central, satellite, or isolated), and cosmic web type (field, filament, or knot). We first stack the full star-forming galaxy sample and find a robust H i detection yielding an average galaxy H i mass of $M_{\rm H \, {\small I}}=(8.12\pm 0.75)\times 10^9\, {\rm M}_\odot$ at ∼11.8σ. Next, we investigate the different subsamples finding a negligible difference in MH i as a function of the galaxy overdensity. We report an H i excess compared to the full sample in satellite galaxies (MH i = (11.31 ± 1.22) × 109, at ∼10.2σ) and in filaments (MH i = (11.62 ± 0.90) × 109. Conversely, we report non-detections for the central and knot galaxies subsamples, which appear to be H i-deficient. We find the same qualitative results also when stacking in units of H i fraction (fH i). We conclude that the H i amount in star-forming galaxies at the studied redshifts correlates with the large-scale structure environment.