Paulo Jorge Garcia

Abstract
Point source detection algorithms play a pivotal role across diverse applications, influencing fields such as astronomy, biomedical imaging, environmental monitoring, and beyond. This article reviews the algorithms used for space imaging applications from ground and space telescopes. The main difficulties in detection arise from the incomplete knowledge of the impulse function of the imaging system, which depends on the aperture, atmospheric turbulence (for ground-based telescopes), and other factors, some of which are time-dependent. Incomplete knowledge of the impulse function decreases the effectiveness of the algorithms. In recent years, deep learning techniques have been employed to mitigate this problem and have the potential to outperform more traditional approaches. The success of deep learning techniques in object detection has been observed in many fields, and recent developments can further improve the accuracy. However, deep learning methods are still in the early stages of adoption and are used less frequently than traditional approaches. In this review, we discuss the main challenges of point source detection, as well as the latest developments, covering both traditional and current deep learning methods. In addition, we present a comparison between the two approaches to better demonstrate the advantages of each methodology.

Abstract
The Adaptive Optics Telemetry (AOT) format has recently been proposed to standardize the telemetry data generated by adaptive optics systems. Yet its usability remains limited by the user's programming expertise and familiarity with the accompanying Python package. There is an opportunity for substantial improvement in data accessibility by offering users an alternative tool for conducting exploratory data analysis in a visual and intuitive manner. We aim to design and develop an open-source Python visualization tool for exploring AOT data. This tool should support researchers and users by offering a broad set of interactive features for the analysis and exploration of the data. We designed a prototype dashboard and performed user testing to validate its usability. We compared the prototype with existing data visualization and exploration tools to ensure we provided the necessary functionality. We made publicly available a user-friendly dashboard for analyzing and exploring AOT data.

Abstract
Estimating turbulence parameters is essential during commissioning and optimising adaptive optics or fringe tracking systems. It also gained new relevance with free-space optical communication applications. The estimation of such parameters is done under the assumption of stationarity. Yet, the stationarity time scale of the atmospheric turbulence is unknown. The breakdown of this assumption leads to incorrect estimates and added error terms. In this paper, we illustrate stationarity detection with unit root testing and the pitfalls of its application to turbulence parameter time series.

Abstract
Context. The amount of adaptive optics (AO) telemetry generated by visible/near-infrared ground-based observatories is ever greater, leading to a growing need for a standardised data exchange format to support performance analysis, AO research, and development activities that involve large-scale telemetry mining, processing, and curation. Aims. This paper introduces the Adaptive Optics Telemetry (AOT) data exchange format as a standard for sharing AO telemetry from visible/infrared ground-based observatories. AOT is based on the flexible image transport system (FITS) and aims to provide unambiguous and consistent data access across various systems and configurations, including natural and single- or multiple-laser guide-star AO systems. Methods. We designed AOT with a focus on two key use cases: atmospheric turbulence parameter estimation and point-spread function reconstruction. We prototyped and tested the design using existing AO telemetry datasets from multiple systems: single conjugate with natural and laser guide stars, tomographic systems with multi-channel wavefront sensors, and single- and multi-wavefront correctors in systems featuring either a Shack-Hartmann or Pyramid as the main wavefront sensor. Results. The AOT file structure has been thoroughly defined, with specified data fields, descriptions, data types, units, and expected dimensions. To support this format, we have developed a Python package that enables the data conversion, reading, writing, and exploration of AOT files; it has been made publicly available and is compatible with a general-purpose Python package manager. We have demonstrated the flexibility of the AOT format by packaging data from five different instruments, installed on different telescopes.

Abstract
Tight relationships exist in the local Universe between the central stellar properties of galaxies and the mass of their supermassive black hole (SMBH)1-3. These suggest that galaxies and black holes co-evolve, with the main regulation mechanism being energetic feedback from accretion onto the black hole during its quasar phase4-6. A crucial question is how the relationship between black holes and galaxies evolves with time; a key epoch to examine this relationship is at the peaks of star formation and black hole growth 8-12 billion years ago (redshifts 1-3)7. Here we report a dynamical measurement of the mass of the black hole in a luminous quasar at a redshift of 2, with a look back in time of 11 billion years, by spatially resolving the broad-line region (BLR). We detect a 40-mu as (0.31-pc) spatial offset between the red and blue photocentres of the H alpha line that traces the velocity gradient of a rotating BLR. The flux and differential phase spectra are well reproduced by a thick, moderately inclined disk of gas clouds within the sphere of influence of a central black hole with a mass of 3.2 x 108 solar masses. Molecular gas data reveal a dynamical mass for the host galaxy of 6 x 1011 solar masses, which indicates an undermassive black hole accreting at a super-Eddington rate. This suggests a host galaxy that grew faster than the SMBH, indicating a delay between galaxy and black hole formation for some systems. Using the GRAVITY+ instrument, dynamical measurement of the black hole mass in a quasar at a redshift of 2.3 (11 billion years ago) shows how the relationship between galaxies and black holes evolves with time.