HATSouth: a global network of fully automated identical wide-field telescopes

HATSouth is the world's first network of automated and homogeneous telescopes that is capable of year-round 24-hour monitoring of positions over an entire hemisphere of the sky. The primary scientific goal of the network is to discover and characterize a large number of transiting extrasolar planets, reaching out to long periods and down to small planetary radii. HATSouth achieves this by monitoring extended areas on the sky, deriving high precision light curves for a large number of stars, searching for the signature of planetary transits, and confirming planetary candidates with larger telescopes. HATSouth employs 6 telescope units spread over 3 locations with large longitude separation in the southern hemisphere (Las Campanas Observatory, Chile; HESS site, Namibia; Siding Spring Observatory, Australia). Each of the HATSouth units holds four 0.18m diameter f/2.8 focal ratio telescope tubes on a common mount producing an 8.2x8.2 arcdeg field, imaged using four 4Kx4K CCD cameras and Sloan r filters, to give a pixel scale of 3.7 arcsec/pixel. The HATSouth network is capable of continuously monitoring 128 square arc-degrees. We present the technical details of the network, summarize operations, and present weather statistics for the 3 sites. On average each of the 6 HATSouth units has conducted observations on ~500 nights over a 2-year time period, yielding a total of more than 1million science frames at 4 minute integration time, and observing ~10.65 hours per day on average. We describe the scheme of our data transfer and reduction from raw pixel images to trend-filtered light curves and transiting planet candidates. Photometric precision reaches ~6 mmag at 4-minute cadence for the brightest non-saturated stars at r~10.5. We present detailed transit recovery simulations to determine the expected yield of transiting planets from HATSouth. (abridged)Comment: 25 pages, 11 figures, 1 table, submitted to PAS

Timely Monitoring of Dynamic Sources with Observations from Multiple Wireless Sensors

Age of Information (AoI) has recently received much attention due to its relevance in IoT sensing and monitoring applications. In this paper, we consider the problem of minimizing the AoI in a system in which a set of sources are observed by multiple sensors in a many-to-many relationship, and the probability that a sensor observes a source depends on the state of the source. This model represents many practical scenarios, such as the ones in which multiple cameras or microphones are deployed to monitor objects moving in certain areas. We formulate the scheduling problem as a Markov Decision Process, and show how the age-optimal scheduling policy can be obtained. We further consider partially observable variants of the problem, and devise approximate policies for large state spaces. Our evaluations show that the approximate policies work well in the considered scenarios, and that the fact that sensors can observe multiple sources is beneficial, especially when there is high uncertainty of the source states.Comment: Submitted for publicatio

Optimal Status Updates for Minimizing Age of Correlated Information in IoT Networks with Energy Harvesting Sensors

Many real-time applications of the Internet of Things (IoT) need to deal with correlated information generated by multiple sensors. The design of efficient status update strategies that minimize the Age of Correlated Information (AoCI) is a key factor. In this paper, we consider an IoT network consisting of sensors equipped with the energy harvesting (EH) capability. We optimize the average AoCI at the data fusion center (DFC) by appropriately managing the energy harvested by sensors, whose true battery states are unobservable during the decision-making process. Particularly, we first formulate the dynamic status update procedure as a partially observable Markov decision process (POMDP), where the environmental dynamics are unknown to the DFC. In order to address the challenges arising from the causality of energy usage, unknown environmental dynamics, unobservability of sensors'true battery states, and large-scale discrete action space, we devise a deep reinforcement learning (DRL)-based dynamic status update algorithm. The algorithm leverages the advantages of the soft actor-critic and long short-term memory techniques. Meanwhile, it incorporates our proposed action decomposition and mapping mechanism. Extensive simulations are conducted to validate the effectiveness of our proposed algorithm by comparing it with available DRL algorithms for POMDPs