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In this paper, we propose a solution for a fundamental problem in computational harmonic analysis, namely, the construction of a multiresolution analysis with directional components. We will do so by constructing subdivision schemes which…
Light curves feature many kinds of variability, including instrumental systematics, intrinsic stellar variability such as pulsations, and flux changes caused by transiting exoplanets or eclipsing binary stars. Detrending is a key…
We implemented a real-time data processor (rta-dp) framework that can be used to develop real-time analysis pipelines and data handling systems to manage high-throughput data streams with distributed applications in the context of ground…
PHOTOMETRYPIPELINE (PP) is an automated pipeline that produces calibrated photometry from imaging data through image registration, aperture photometry, photometric calibration, and target identification with only minimal human interaction.…
Kernel ridge regression (KRR) is a popular scheme for non-linear non-parametric learning. However, existing implementations of KRR require that all the data is stored in the main memory, which severely limits the use of KRR in contexts…
In recent years, a number of new instruments and data reduction pipelines have been developed to obtain high-precision radial velocities (RVs). In particular in the optical, considerable progress has been made and RV precision below 50 cm/s…
ORAC-DR is a general purpose data reduction pipeline system designed to be instrument and observatory agnostic. The pipeline works with instruments as varied as infrared integral field units, imaging arrays and spectrographs, and…
This report describes the winning solution to the WeatherProof Dataset Challenge (CVPR 2024 UG2+ Track 3). Details regarding the challenge are available at https://cvpr2024ug2challenge.github.io/track3.html. We propose an enhanced semantic…
Recent image restoration methods have produced significant advancements using deep learning. However, existing methods tend to treat the whole image as a single entity, failing to account for the distinct objects in the image that exhibit…
Seismology has witnessed significant advancements in recent years with the application of deep learning methods to address a broad range of problems. These techniques have demonstrated their remarkable ability to effectively extract…
Here, I present the community with exoTEDRF (EXOplanet Transit and Eclipse Data Reduction Framework; formerly known as supreme-SPOON), an end-to-end pipeline for data reduction and light curve analysis of time series observations (TSOs) of…
With the advent of modern multi-detector heterodyne instruments that can result in observations generating thousands of spectra per minute it is no longer feasible to reduce these data as individual spectra. We describe the automated data…
For scientific knowledge to be findable, accessible, interoperable, and reusable, it needs to be machine-readable. Moving forward from post-publication extraction of knowledge, we adopted a pre-publication approach to write research…
We optimize pipeline parallelism for deep neural network (DNN) inference by partitioning model graphs into $k$ stages and minimizing the running time of the bottleneck stage, including communication. We give practical and effective…
The Python package teareduce has been developed to support teaching activities related to the reduction of astronomical data. Specifically, it serves as instructional material for students participating in practical classes on the…
Keyphrase extraction (KPE) automatically extracts phrases in a document that provide a concise summary of the core content, which benefits downstream information retrieval and NLP tasks. Previous state-of-the-art (SOTA) methods select…
Data preparation (DP) transforms raw data into a form suitable for downstream applications, typically by composing operations into executable pipelines. Building such pipelines is time-consuming and requires sophisticated programming…
The direct detection and characterization of planetary and substellar companions at small angular separations is a rapidly advancing field. Dedicated high-contrast imaging instruments deliver unprecedented sensitivity, enabling detailed…
The CHaracterizing ExOPlanet Satellite (CHEOPS), to be launched in December 2019, will detect and characterize small size exoplanets via ultra high precision photometry during transits. CHEOPS is designed as a follow-up telescope and…
The launches of NASA Kepler and TESS missions have significantly enhanced the interest in the exoplanet field during the last 15 years, providing a vast amount of public data that is being exploited by the community thanks to the continuous…