arXiv subjects

David Degen

Publications and source records attributed to David Degen.

4 recordsLinked to original sources

arXiv2026

MIRA: a data management and education platform connecting students to robotic telescopes

Hands-on telescope experience is often used to drive student engagement in astronomy education, but scaling access to larger groups of students is operationally challenging. Consequently, students encounter only a fraction of the professional workflow, rarely engaging with the rigorous peer-review, time-allocation processes, or automated data reduction pipelines that govern modern research facilities. We present the design of MIRA (Mentored Investigations using Robotic Astronomy), a data management and educational platform that connects Swiss secondary school and undergraduate students with operational robotic observatories. MIRA structures the entire observation lifecycle: proposal, review, acceptance/rejection, scheduling, and observation. Following execution, the platform automatically reduces raw FITS frames (including astrometric calibration and photometry) and serves them via a web-accessible archive accompanied by Python-based analysis tutorials. By separating educational front-ends from low-level telescope controls through Astra and ASCOM Alpaca, MIRA delivers an authentic scientific research workflow that bridges classroom learning with professional observatory operations.

astro-ph.IM↗

arXiv2026

Astra: an open-source fully autonomous robotic observatory control software

Robotic and autonomous observatories are critical for modern time-domain and high-cadence astronomical surveys. The operation of these facilities requires complex software coordination to manage hardware, schedule observations, and ensure safety. However, existing observatory control software are often proprietary and platform-locked or require complex message-brokering infrastructure. Here we present Astra (Automated Survey observaTory Robotised with Alpaca): an open-source, cross-platform Python system for the sustained, fully autonomous operation of astronomical observatories, requiring no external message-broker infrastructure. Astra controls observatory hardware via the ASCOM Alpaca protocol, and executes prescheduled observatory actions under continuous safety supervision. Its multi-device actions include plate-solve-based pointing correction with a local Gaia--2MASS catalogue fallback, PID-controlled autoguiding, and autofocus. A FastAPI web service provides a browser UI, REST and WebSocket APIs for real-time status, image previews, and SQLite-backed telemetry and logs. Astra has run in fully unattended production since January 2024, scaling to six telescopes across three facilities: the SPECULOOS-South network (4 $\times$ 1\,m class, Chile), SAINT-EX (1\,m class, Mexico), and the ETH Observatory (0.5\,m class, Switzerland), with no schedule aborts attributable to Astra software. Across the SPECULOOS-South network, it achieves sub-arcsecond autoguiding (0.11\unit{\arcsecond} median pointing scatter) and plate-solve failure rates below 1\% on three of the four telescopes (3\% on the narrowest-field unit), demonstrating that an open, standards-based software stack can meet the reliability demands of production survey astronomy.

astro-ph.IM↗

arXiv2021

Retrieval of aerosol properties from in situ, multi-angle light scattering measurements using invertible neural networks

Atmospheric aerosols have a major influence on the earths climate and public health. Hence, studying their properties and recovering them from light scattering measurements is of great importance. State of the art retrieval methods such as pre-computed look-up tables and iterative, physics-based algorithms can suffer from either accuracy or speed limitations. These limitations are becoming increasingly restrictive as instrumentation technology advances and measurement complexity increases. Machine learning algorithms offer new opportunities to overcome these problems, by being quick and precise. In this work we present a method, using invertible neural networks to retrieve aerosol properties from in situ light scattering measurements. In addition, the algorithm is capable of simulating the forward direction, from aerosol properties to measurement data. The applicability and performance of the algorithm are demonstrated with simulated measurement data, mimicking in situ laboratory and field measurements. With a retrieval time in the millisecond range and a weighted mean absolute percentage error of less than 1.5%, the algorithm turned out to be fast and accurate. By introducing Gaussian noise to the data, we further demonstrate that the method is robust with respect to measurement errors. In addition, realistic case studies are performed to demonstrate that the algorithm performs well even with missing measurement data.

physics.ao-ph↗

arXiv2020

On the moduli space of ricci flat metrics on a K3 surface

We show that the moduli space of Ricci flat metrics of unit volume (including orbifold metrics) on a K3 surface is simply connected and that it has the same rational cohomology as the automorphism group of the K3 lattice $(-E_8)^{\oplus 2}\oplus U^{\oplus 3}$.

math.DG↗