arXiv subjects

Malvina Bellotti

Publications and source records attributed to Malvina Bellotti.

3 recordsLinked to original sources

arXiv2026

A pipeline for identifying astrophysical signals and instrumental glitches in LISA data

The Laser Interferometer Space Antenna (LISA) is a space-based gravitational wave detector scheduled for launch in the 2030s by the European Space Agency. Operating in the 0.1 mHz to 0.1 Hz band, LISA will observe a variety of astrophysical sources including but not limited to supermassive black hole mergers, extreme mass ratio inspirals, and white dwarf binaries. LISA will yield new insights, including supermassive black hole formation, new tests of general relativity, population studies of white dwarfs and others. Like all interferometric gravitational wave detectors, LISA data is expected to contain non-astrophysical transients, commonly referred to as glitches. We introduce a two step pipeline that consists of a search algorithm for detecting potential transients in simulated LISA data followed by a machine learning model that classifies the detected transients as gravitational waves or instrumental glitches. This is important for MBHBs with very high masses (around $10^6\ M_{\odot}$) which are transients that could be morphologically similar to LISA glitches in the data. Initial results demonstrate a promising ability to classify detected events with an accuracy close to $98\%$. The pipeline also shows the ability to analyze a month of data in under 2 minutes on a laptop. This shows an eventual utility of our pipeline for enabling accurate rapid alerts from low latency searches. It could also support model selection in global-fit analyses by providing an initial summary of the location and type of individual signals. This paper also discusses challenges encountered during training, including limitations of the current framework.

gr-qc↗

arXiv2026

Fast premerger detection of massive black-hole binaries in LISA based on time-frequency excess power

The Laser Interferometer Space Antenna is expected to observe gravitational waves from massive black hole binaries across cosmic time. Many are anticipated to be detectable hours to weeks before coalescence. We present a fast algorithm for the premerger detection and preliminary characterization of such binaries. The method performs a search for excess power with a chirping time-frequency morphology in short-time Fourier transform spectrograms. By tiling the time-frequency plane with slices defined by the quadrupole frequency evolution, we define a signal significance relative to a fitted background distribution of instrumental noise and Galactic foreground. Individual search triggers are followed by a coherence tracker that groups triggers consistent with the same physical signal over time. Doing so, our analysis provides progressively refined estimates of the chirp mass and coalescence time. We validate our algorithm on the SangriaHM LISA Data Challenge dataset, successfully detecting all 15 injected massive black-hole binaries: 14 of them hours to weeks before merger, while one is only detected after the binary coalescence. The algorithm yields chirp mass relative errors below $3\%$ for high-SNR sources and coalescence time uncertainties of up to a few hours. With a computational cost of less than a second to process a 10-day data segment on a single core, our approach is suitable for generating real-time alerts, triggering protected observational periods, and providing informative priors for Bayesian parameter estimation.

astro-ph.IM↗

arXiv2024

Quick recipes for gravitational-wave selection effects

Accurate modeling of selection effects is a key ingredient to the success of gravitational-wave astronomy. The detection probability plays a crucial role in both statistical population studies, where it enters the hierarchical Bayesian likelihood, and astrophysical modeling, where it is used to convert predictions from population-synthesis codes into observable distributions. We review the most commonly used approximations, extend them, and present some recipes for a straightforward implementation. These include a closed-form expression capturing both multiple detectors and noise realizations written in terms of the so-called Marcum Q-function and a ready-to-use mapping between signal-to-noise ratio thresholds and false-alarm rates from state-of-the-art detection pipelines. The bias introduced by approximating the matched filter signal-to-noise ratio with the optimal signal-to-noise ratio is not symmetric: sources that are nominally below threshold are more likely to be detected than sources above threshold are to be missed. Using both analytical considerations and software injections in detection pipelines, we confirm that including noise realizations when estimating the selection function introduces an average variation of a few %. This effect is most relevant for large catalogs and specific subpopulations of sources at the edge of detectability (e.g. high redshifts).

astro-ph.HE↗