Related papers: Error dependencies in the space-based CNEOS fireba…

The goal of this work is to determine if the dynamics of individual Taurid Complex (TC) objects are consistent with the formation of the complex via fragmentation of a larger body, or if the current orbital affinities between the TC members…

Accurate atomic data and plasma models are essential for interpreting the upcoming high-quality spectra from missions like XRISM and Athena. Estimating physical quantities, like temperature, abundance, turbulence, and resonance scattering…

We report an early result from the ICARUS experiment on the search for nu_mu to nu_e signal due to the LSND anomaly. The search was performed with the ICARUS T600 detector located at the Gran Sasso Laboratory, receiving CNGS neutrinos from…

Uncertainty-aware deep learning (DL) models recently gained attention in fault diagnosis as a way to promote the reliable detection of faults when out-of-distribution (OOD) data arise from unseen faults (epistemic uncertainty) or the…

The DANSS experiment collected 5.5 million Inverse Beta Decay (IBD) events during 5 years of operation. Data were collected at 3 distances (10.9~m, 11.9~m, and 12.9~m) from the center of the core of the industrial $3.1~GW$ reactor. The IBD…

Due to the high-precision nature of the ASTROD (Astrodynamical Space Test of Relativity using Optical Devices) mission concept, the asteroid perturbations on the ASTROD spacecraft is crucial. These perturbations need to be modelled and…

Separating meteor showers from the sporadic meteor background is critical for the study of both showers and the sporadic complex. The linkage of meteors to meteor showers, to parent bodies, and to other meteors is done using measures of…

A new method of event characterization based on Deep Learning is presented. The PointNet models can be used for fast, online event-by-event impact parameter determination at the CBM experiment. For this study, UrQMD and the CBM detector…

Modern machine learning models, that excel on computer vision tasks such as classification and object detection, are often overconfident in their predictions for Out-of-Distribution (OOD) examples, resulting in unpredictable behaviour for…

Deep neural networks (DNNs) are known to produce incorrect predictions with very high confidence on out-of-distribution inputs (OODs). This limitation is one of the key challenges in the adoption of DNNs in high-assurance systems such as…

Environmental perception obtained via object detectors have no predictable safety layer encoded into their model schema, which creates the question of trustworthiness about the system's prediction. As can be seen from recent adversarial…

When dealing with an orbit determination problem, uncertainties naturally arise from intrinsic errors related to observation devices and approximation models. Following the least squares method and applying approximation schemes such as the…

Superfireballs are rare phenomena for which the reports are scarce and the estimation of their abundance has a huge margin of uncertainty. As a citizen science project we have gathered >500 reports from newspapers in the 1850-2000 period.…

Information about the physical properties of astrophysical objects cannot be measured directly but is inferred by interpreting spectroscopic observations in the context of atomic physics calculations. Ratios of emission lines, for example,…

PyNeb is a Python package widely used to model emission lines in gaseous nebulae. We take advantage of its object-oriented architecture, class methods, and historical atomic database to structure a practical environment for atomic data…

We have analysed the orbits and ablation characteristics in the atmosphere of 59 earth-impacting fireballs, produced by meteoroids one meter in diameter or larger, described here as meter-scale. Using heights at peak luminosity as a proxy…

Detecting impact where an individual makes contact with the ground within a fall event is crucial in fall detection systems, particularly for elderly care where prompt intervention can prevent serious injuries. The UP-Fall dataset, a key…

Catalina Sky Survey (CSS) is a major survey of Near-Earth Objects (NEOs). In a recent work, we used CSS observations from 2005-2012 to develop a new population model of NEOs (NEOMOD). CSS's G96 telescope was upgraded in 2016 and detected…

The literature shows outstanding capabilities for CNNs in event recognition in images. However, fewer attempts are made to analyze the potential causes behind the decisions of the models and exploring whether the predictions are based on…

Redshift-independent distances underpin much of astrophysics, and there exists a plethora of methods to estimate them. However, the extent to which the distances they imply are consistent, while crucial for the integrity of the distance…