arXiv subjects

Tabata Aira Ferreira

Publications and source records attributed to Tabata Aira Ferreira.

6 recordsLinked to original sources

arXiv2026

Search for continuous gravitational waves from neutron stars in five globular clusters in the first part of the fourth LIGO-Virgo-KAGRA observing run

We present the results of directed searches for continuous gravitational waves from unknown neutron stars in five Milky Way globular clusters. We carry out these searches in the LIGO data from the first eight months of the fourth LIGO-Virgo-KAGRA observing run using the WEAVE semicoherent program, which sums matched-filter detection-statistic values over many time segments spanning the observation period. No gravitational wave signal is detected in the search band of 20-475 Hz. Injections of simulated continuous wave signals in the data indicate that we achieve the most sensitive results to date across most of the explored parameter space volume, obtaining median 95% confidence level upper limits as low as $\sim 4.2 \times 10^{-26}$ near 282 Hz for NGC 6397. We also derive upper limits on neutron star ellipticity and $r$-mode amplitudes, reaching $\lesssim 10^{-5}$ and $\lesssim 10^{-3}$, respectively, at frequencies above 200 Hz.

gr-qc↗

arXiv2026

Scattered light noise at LIGO Livingston Observatory during O4

Scattered light is one of the most common sources of noise in the LIGO gravitational wave detectors. Light scattering is a highly non-linear process through which motion at low frequencies gets up-converted and creates noise in a higher frequency band in the detector data. From the beginning of the fourth observation run, many glitches appeared in the data of LIGO Livingston detector in the frequency range 10-40 Hz, and the morphology of these glitches suggested that they were produced by scattered light. From our analysis, we identified two different populations of scattered light glitches, one group having higher SNR than the other. The glitches of the high- SNR group were solely modulated by microseismic ground motion (ground motion in 0.1-1.0 Hz) and in this paper, we present models of possible coupling mechanisms for these glitches. We also present results of a statistical correlation analysis based on our models, which indicates that the microseismic ground motion at the corner station along the X direction is the one most correlated with the noise which create these high SNR glitches. After installing baffles very close to the test mass mirrors, we have noticed a significant reduction in the rate and SNR of these glitches. The low-SNR glitches were primarily modulated by high frequency (10-30 Hz) vertical ground motion at the corner station, and this motion was coupling through a specific vacuum chamber at the corner station. After installing an additional seismic isolation platform in that vacuum chamber, these glitches have disappeared.

astro-ph.IM↗

arXiv2026

The Sound of Noise: Leveraging the Inductive Bias of Pre-trained Audio Transformers for Glitch Identification in LIGO

Transient noise artifacts, or glitches, fundamentally limit the sensitivity of gravitational-wave (GW) interferometers and can mimic true astrophysical signals, particularly the short-duration intermediate-mass black hole (IMBH) mergers. Current glitch classification methods, such as Gravity Spy, rely on supervised models trained from scratch using labeled datasets. These approaches suffer from a significant ``label bottleneck," requiring massive, expertly annotated datasets to achieve high accuracy and often struggling to generalize to new glitch morphologies or exotic GW signals encountered in observing runs. In this work, we present a novel cross-domain framework that treats GW strain data through the lens of audio processing. We utilize the Audio Spectrogram Transformer (AST), a model pre-trained on large-scale audio datasets, and adapt it to the GW domain. Instead of learning time-frequency features from scratch, our method exploits the strong inductive bias inherent in pre-trained audio models, transferring learned representations of natural sound to the characterization of detector noise and GW signals, including IMBHs. We validate this approach by analyzing strain data from the third (O3) and fourth (O4) observing runs of the LIGO detectors. We used t-Distributed Stochastic Neighbor Embedding (t-SNE), an unsupervised clustering technique, to visualize the AST-derived embeddings of signals and glitches, revealing well-separated groups that align closely with independently validated Gravity Spy glitch classes. Our results indicate that the inductive bias from audio pre-training allows superior feature extraction compared to traditional supervised techniques, offering a robust, data-efficient pathway for discovering new, anomalous transients, and classifying complex noise artifacts in the era of next-generation detectors.

astro-ph.IM↗

arXiv2025

An Analysis of LIGO Glitches Using t-SNE During the First Part of the Fourth LIGO-Virgo-KAGRA Observing Run

This paper presents an analysis of noise transients observed in LIGO data during the first part of the fourth observing run, using the unsupervised machine learning technique t-distributed Stochastic Neighbor Embedding (t-SNE) to examine the behavior of glitch groups. Based on the t-SNE output, we apply Agglomerative Clustering in combination with the Silhouette Score to determine the optimal number of groups. We then track these groups over time and investigate correlations between their occurrence and environmental or instrumental conditions. At the Livingston observatory, the most common glitches during O4a were seasonal and associated with ground motion, whereas at Hanford, the most prevalent glitches were related to instrumental conditions.

astro-ph.IM↗

arXiv2025

Using t-SNE for characterizing glitches in LIGO detectors

Glitches are non-Gaussian noise transients originating from environmental and instrumental sources that contaminate data from gravitational wave detectors. Some glitches can even mimic gravitational wave signals from compact object mergers, which are the primary targets of terrestrial observatories. In this study, we present a method to analyze noise transients from the LIGO observatories using Q-transform information combined with t-Distributed Stochastic Neighbor Embedding (t-SNE). We implement classification techniques, examine the influence of parameters on glitch classification, and conduct a week-long daily analysis to track outlier transients over time.

astro-ph.IM↗

arXiv2025

Parameter Estimation Precision with Geocentric Gravitational Wave Interferometers: Monochromatic Signals

We present a Fisher information matrix study of the parameter estimation precision achievable by a class of future space-based, "mid-band", gravitational wave (GW) interferometers observing monochromatic signals. The mid-band is the frequency region between that accessible by LISA and ground-based interferometers. We analyze monochromatic signals from TianQin, gLISA, and gLISA$_d$, a descoped gLISA which has acceleration noise three orders of magnitude worse than gLISA. We find that all three missions achieve their best angular source reconstruction precision in the higher part of their accessible frequency band, with an error box better than $10^{-10}$ sr in the frequency band [$10^{-1},10$] Hz when observing a monochromatic GW signal of amplitude $h_0=10^{-21}$ that is incoming from a given direction. In terms of their reconstructed frequencies and amplitudes, TianQin achieves its best precision values in both quantities in the frequency band [$10^{-2}, 4\times 10^{-1}$] Hz, with a frequency precision $σ_{f_{gw}}=2\times 10^{-11}$ Hz and an amplitude precision $σ_{h_0}=2\times 10^{-24}$. gLISA matches these precisions in a frequency band slightly higher than that of TianQin, [$3\times 10^{-2},1$] Hz, as a consequence of its smaller arm length. gLISA$_d$, on the other hand, matches the performance of gLISA only over the narrower frequency region, [$7\times 10^{-1},1$] Hz, as a consequence of its higher acceleration noise at lower frequencies. The angular, frequency, and amplitude precisions as functions of the source sky location are derived assuming an average SNR of 10 at selected GW frequencies within TianQin and gLISA's bands. The same is done for gLISA$_d$ using amplitudes yielding an SNR of 10 for gLISA. We find that, for any given source location, all three missions display a marked precision improvement in the three reconstructed parameters at higher GW frequencies.

gr-qc↗