Related papers: DeepSurveySim: Simulation Software and Benchmark C…
Telescope arrays are receiving increasing attention due to their promise of higher resource utilization, greater sky survey area, and higher frequency of full space-time monitoring than single telescopes. Compared with the ordinary…
Studying the rapid variability of many astronomical objects is key to understanding the underlying processes at play. However, a combination of limited telescope availability, viewing constraints, and the unpredictable nature of many…
ScopeSim is a flexible multipurpose instrument data simulation framework built in Python. It enables both raw and reduced observation data to be simulated for a wide range of telescopes and instruments quickly and efficiently on a personal…
A concept of the ground-based optical astronomical observations efficiency is considered in this paper. We believe that a telescope efficiency can be increased by properly allocating observation tasks with respect to the current environment…
We present MauveSim, the instrument simulator software for Mauve, the latest mission from Blue Skies Space dedicated to time-domain stellar astronomy. MauveSim functions as an end-to-end simulator, employing the most up-to-date knowledge of…
Automated searches for strong gravitational lensing in optical imaging survey datasets often employ machine learning and deep learning approaches. These techniques require more example systems to train the algorithms than have presently…
How ground-based telescopes schedule their observations in response to competing science priorities and constraints, variations in the weather, and the visibility of a particular part of the sky can significantly impact their efficiency. In…
We describe a new software package for simulating channelised, high-time resolution data streams from radio telescopes. The software simulates data from the telescope and observing system taking into account the observation strategy,…
Benchmark experiments are required to test, compare, tune, and understand optimization algorithms. Ideally, benchmark problems closely reflect real-world problem behavior. Yet, real-world problems are not always readily available for…
Recent advances in deep reinforcement learning (deep RL) enable researchers to solve challenging control problems, from simulated environments to real-world robotic tasks. However, deep RL algorithms are known to be sensitive to the problem…
In many areas of decision-making, forecasting is an essential pillar. Consequently, many different forecasting methods have been proposed. From our experience, recently presented forecasting methods are computationally intensive, poorly…
For time-domain astronomy, it is crucial to frequently image celestial objects at specific depths within a predetermined cadence. To fulfill these scientific demands, scientists globally have started or planned the development of…
Modern astronomical surveys such as the Large Synoptic Sky Survey (LSST) promise an unprecedented wealth of discoveries, delivered in the form of ~10 million alerts of time-variable events per night. Astronomers are faced with the daunting…
Machine learning, and eventually true artificial intelligence techniques, are extremely important advancements in astrophysics and astronomy. We explore the application of deep learning using neural networks in order to automate the…
$\nu$SpaceSim is a highly-efficient (e.g., fast) module-based, end-to-end simulation package that models the physical processes of cosmic neutrino interactions that leads to detectable signals for sub-orbital and space-based instruments.…
Recent advances in probabilistic modelling have led to a large number of simulation-based inference algorithms which do not require numerical evaluation of likelihoods. However, a public benchmark with appropriate performance metrics for…
Earth observation satellite imaging scheduling is a challenging NP-hard combinatorial optimisation problem central to space mission operations. While next-generation agile Earth observation satellites (EOS) increase operational flexibility,…
Agile Earth Observation Satellites (AEOSs) constellations offer unprecedented flexibility for monitoring the Earth's surface, but their scheduling remains challenging under large-scale scenarios, dynamic environments, and stringent…
Despite centuries of close association, statistics and astronomy are surprisingly distant today. Most observational astronomical research relies on an inadequate toolbox of methodological tools. Yet the needs are substantial: astronomy…
Satellite mission planning for Earth observation satellites is a combinatorial optimization problem that consists of selecting the optimal subset of imaging requests, subject to constraints, to be fulfilled during an orbit pass of a…