Related papers: MOSFiT: Modular Open-Source Fitter for Transients
We present FitTeD, a public light curve and spectral fitting Python-package based on evolving relativistic discs. At its heart this package uses the solutions of the time dependent general relativistic disc equations to compute multi-band…
Time-domain astrophysics has leaped forward with the direct discovery of gravitational waves and the emergence of new generation instruments for multi-messenger studies. The capacity of the multi-messenger multi-wavelength community to…
Modern astronomical surveys, such as the Zwicky Transient Facility (ZTF), are capable of detecting thousands of transient events per year, necessitating the use of automated and scalable data analysis techniques. Recent advances in machine…
Tracking multiple objects in videos relies on modeling the spatial-temporal interactions of the objects. In this paper, we propose a solution named TransMOT, which leverages powerful graph transformers to efficiently model the spatial and…
In recent work on time-series prediction, Transformers and even large language models have garnered significant attention due to their strong capabilities in sequence modeling. However, in practical deployments, time-series prediction often…
Multivariate time series forecasting is crucial across various industries, where accurate extraction of complex periodic and trend components can significantly enhance prediction performance. However, existing models often struggle to…
swdatatoolkit is a Python-based scientific software library designed to support the acquisition, preprocessing, and analysis of solar and space weather data. The toolkit consolidates functionality across multiple domains, including data…
The identification of extragalactic fast optical transients (eFOTs) as potential multi-messenger sources is one of the main challenges in time-domain astronomy. However, recent developments have allowed for probes of rapidly-evolving…
Scene understanding using multi-modal data is necessary in many applications, e.g., autonomous navigation. To achieve this in a variety of situations, existing models must be able to adapt to shifting data distributions without arduous data…
Changepoint detection is commonly formulated by minimizing the sum of in-sample losses to quantify the model's overall fit. However, for flexible modeling procedures -- especially those involving high-dimensional parameter spaces or…
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…
Despite image reconstruction becoming more widespread when interpreting OIFITS Data, model fitting in u,v space often remains the best way to interpret data, either because of the sparsity of the data, or because a quantitative measurement…
What constitutes an object? This has been a long-standing question in computer vision. Towards this goal, numerous learning-free and learning-based approaches have been developed to score objectness. However, they generally do not scale…
Machine learning has become essential for automated classification of astronomical transients, but current approaches face significant limitations: classifiers trained on simulations struggle with real data, models developed for one survey…
The early and complete temporal characterization of optical, fast, transient sources requires continuous and multiband observations over different timescales (hours to months). For time-domain astronomy, using several telescopes to analyze…
Transient detection and flux measurement via image subtraction stand at the base of time domain astronomy. Due to the varying seeing conditions, the image subtraction process is non-trivial, and existing solutions suffer from a variety of…
Since its launch, TESS has provided high cadence observations for objects across the sky. Although high cadence TESS observations provide a unique possibility to study the rapid time evolution of numerous objects, artifacts in the data make…
As next-generation imaging instruments and interferometers search for planets closer to their stars, they must contend with increasing orbital motion and longer integration times. These compounding effects make it difficult to detect faint…
When planning a survey for astronomical transients, many factors such as cadence, filter choice, sky coverage, and depth of observations need to be balanced in order to optimize the scientific gain of the survey. Here we present a software…
A new golden age in astronomy is upon us, dominated by data. Large astronomical surveys are broadcasting unprecedented rates of information, demanding machine learning as a critical component in modern scientific pipelines to handle the…