Related papers: RoBo6: Standardized MMT Light Curve Dataset for Ro…
The understanding of the reconstruction and calibration of electrons and photons is one of the key steps at the start-up of data-taking with ATLAS at the LHC (Large Hadron Collider). The calorimeter cells are electronically calibrated…
This paper presents a unique outdoor aerial visual-inertial-LiDAR dataset captured using a multi-sensor payload to promote the global navigation satellite system (GNSS)-denied navigation research. The dataset features flight distances…
Context. The accurate classification of hundreds of thousands of galaxies observed in modern deep surveys is imperative if we want to understand the universe and its evolution. Aims. Here, we report the use of machine learning techniques to…
Machine learning (ML) is a standard approach for estimating the redshifts of galaxies when only photometric information is available. ML photo-z solutions have traditionally ignored the morphological information available in galaxy images…
One of the challenges in evaluating multi-object video detection, tracking and classification systems is having publically available data sets with which to compare different systems. However, the measures of performance for tracking and…
The Surface Photometry and Accurate Rotation Curves (SPARC) database has provided the community with mass models for 175 nearby galaxies, allowing different research teams to test different dark matter models, galaxy evolution models, and…
Surface-level marine debris remains a practical bottleneck for autonomous clean-up, where small, reflective targets (e.g., aluminum cans) must be detected at distance under glare, ripples, and partial submersion. This paper presents, an ASV…
Attitude is one of the crucial parameters for space objects and plays a vital role in collision prediction and debris removal. Analyzing light curves to determine attitude is the most commonly used method. In photometric observations,…
We present rotation curves of 19 galaxies from THINGS, The HI Nearby Galaxy Survey. The high spatial and velocity resolution of THINGS make these the highest quality HI rotation curves available to date for a large sample of nearby…
The changing-look blazars (CLBs) are the blazars that their optical spectral lines at different epochs show a significant changes and present a clear transition between the standard FSRQ and BL Lac types. The changing-look phenomena in…
In this paper we investigate how implementing machine learning could improve the efficiency of the search for Trans-Neptunian Objects (TNOs) within Dark Energy Survey (DES) data when used alongside orbit fitting. The discovery of multiple…
Asteroids are an indelible part of most astronomical surveys though only a few surveys are dedicated to their detection. Over the years, high cadence microlensing surveys have amassed several terabytes of data while scanning primarily the…
[Abridged and Edited] We investigate nuclear light profiles in 135 ATLAS3D galaxies for which the Hubble Space Telescope (HST) imaging is available and compare them to the large scale kinematics obtained with the SAURON integral-field…
Rejecting cosmic rays (CRs) is essential for the scientific interpretation of CCD-captured data, but detecting CRs in single-exposure images has remained challenging. Conventional CR detectors require experimental parameter tuning for…
Real-world point cloud datasets have made significant contributions to the development of LiDAR-based perception technologies, such as object segmentation for autonomous driving. However, due to the limited number of instances in some rare…
The EROS project (Exp\'erience de Recherche d'Objets Sombres) carried out photometric surveys of dense stellar fields towards the Magellanic Clouds (LMC and SMC), the Galactic Bulge and Galactic spiral arms, over the period 1990-2003. The…
We have built a custom mobile multi-camera large-space dense light field capture system, which provides a series of high-quality and sufficiently dense light field images for various scenarios. Our aim is to contribute to the development of…
In this paper, we present a high-performance and light-weight deep learning model for Remote Sensing Image Classification (RSIC), the task of identifying the aerial scene of a remote sensing image. To this end, we first valuate various…
The exponential growth in Unmanned Aerial Vehicles (UAVs) usage underscores the critical need of detecting them at extended distances to ensure safe operations, especially in densely populated areas. Despite the tremendous advances made in…
Accurate detection and classification of nuclei in histopathology images are critical for diagnostic and research applications. We present KongNet, a multi-headed deep learning architecture featuring a shared encoder and parallel,…