Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.37034

FLITS: an interactive, reproducible workbench for fast radio burst analysis

Dirk Kuiper·Ziggy Pleunis·Jason W. T. Hessels
View original source ↗Read source document ↗Export metadata ↓

Abstract

Fast radio bursts (FRBs) are brief flashes of radio emission whose time, frequency, and polarization structure constrain their sources and propagation environments. FLITS (Fast-Look Interactive Transient Suite) is an open-source Python package with a browser interface for analysing detected bursts. It combines data inspection, interference mitigation, measurement, and modelling, including fits of the fitburst burst model, in a shared session, and reads SIGPROC filterbank, PSRFITS, and CHIME/FRB HDF5 data. The same routines measure larger samples from the command line and flag bursts whose automatic selections need checking. Trial dispersion measures can be scored by signal-to-noise, by burst structure, or with a smoothed-structure metric that also reports the range of dispersion measures consistent with the best one. Portable session files retain the selected data regions, excluded channels, calibration inputs, available results, input-data checksum, and software provenance, and supported measurements can be replayed from the command line and compared with their saved values. Uncertainty labels identify missing calibration information. A public reference workflow provides downloadable data and numerical checks.

Explore related subjects

astro-ph.IMastro-ph.HE

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Dirk Kuiper, Ziggy Pleunis, Jason W. T. Hessels. 2026-10-27. FLITS: an interactive, reproducible workbench for fast radio burst analysis. https://arxiv.org/abs/2610.37034

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

An autoencoder-based surrogate waveform model for quasi-circular binary-black-hole mergers

The generation of accurate waveforms from binary black hole (BBH) mergers is a major effort in Gravitational-Wave Astronomy. In recent years, machine-learning-based surrogate models for BBH waveforms have been proposed. Those offer the potential to dramatically accelerate waveform generation while maintaining accuracy competitive with that of traditional waveform approximants. In this work, we investigate the viability of autoencoders as generative models for gravitational-wave signals from quasi-circular BBH mergers. We introduce AESur3dq8, a novel surrogate waveform model based on autoencoders that enables the rapid and accurate construction of large template banks, producing millions of waveforms in under a second using modest computational resources. The model is trained on the numerical-relativity-informed surrogate NRHybSur3dq8 and subsequently fine-tuned using the SXS catalog of BBH simulations. We demonstrate that waveforms generated by AESur3dq8 achieve mismatches of order $10^{-4}$ with respect to Numerical Relativity waveforms, and that parameter estimation performed with these templates yields results fully consistent with those reported by the LIGO-Virgo-KAGRA Collaboration for observed gravitational-wave events.

astro-ph.IM↗
arXiv2026

Modeling Dependence Structures in Astronomical Multi-Band Time Series Data via Multi-Output Gaussian Processes

Modern astronomical time-domain surveys routinely collect multi-band light curves that provide complementary information about the physical processes governing source variability. Although various multi-output Gaussian process (GP) models have been applied in different astronomical contexts, the statistical and astrophysical implications of their dependence structures have received comparatively limited attention. In this work, we examine three existing approaches to modeling cross-band dependence within a common multi-output GP framework and investigate how their underlying assumptions affect statistical inference and astrophysical interpretation. These include covariance-based, stochastic differential equation (SDE)-based, and latent-process approaches, which differ mainly in how cross-band dependence is introduced. The covariance-based approach directly specifies cross-band covariance functions to characterize dependence, whereas the SDE-based approach specifies joint temporal dynamics, with cross-band dependence arising from coupled dynamics or correlated driving processes. The latent-process approach represents dependence through shared stochastic processes and their transformations, which can reflect physical relationships among multiple light curves. We demonstrate the practical implications of these modeling choices through applications to multi-band active galactic nucleus light curves and emission-line reverberation mapping. Rather than advocating a universally preferred approach, this work provides guidance for selecting dependence structures according to scientific objectives and clarifies how these choices influence the statistical characterization and scientific interpretation of multi-band stochastic variability.

astro-ph.IM↗
arXiv2026

A Framework for Evaluating Forward Contamination Risk for Bio-ISRU Microorganisms

NASA planetary protection policy seeks to avoid inadvertent forward contamination of celestial bodies and backward contamination of Earth during space exploration. For Mars missions, it has historically focused on sterilization of landers and rovers. Current policy provides no framework for assessing the forward contamination risk of missions that deliberately include living organisms and must not be sterilized, such as a human crew, agriculture, or biotechnology for manufacturing and life support. We propose the PRIM (Propagation Restricted, Inert on Mars) framework to evaluate the forward-contamination risk posed by known organisms. Building on the biocontainment literature and probability-of-contamination model, PRIM bounds the ability of an organism released from a worst-case off-nominal event to sustain growth on the Martian surface at a probability under P_c <= 10^-4 using independent single-stressor propagation assays. We apply the PRIM framework to two engineered Mars biological in situ resource utilization chassis organisms, using low water activity and carbon starvation to qualify them for low forward contamination risk even at high bioburden, and outline how to extend the assays to phototrophs and anaerobes. PRIM offers a path to flight-qualify living organisms for Mars based on laboratory assays rather than bioburden alone. This enables biological life support infrastructure for a sustained presence on Mars while keeping forward contamination risk demonstrably low.

astro-ph.IM↗