arXiv subjects

Sofia Alvarez-Lopez

Publications and source records attributed to Sofia Alvarez-Lopez.

6 recordsLinked to original sources

arXiv2026

GSpyNetTree-O4: an event validation tool used in the fourth LIGO-Virgo-KAGRA observing run

The frequent presence of non-Gaussian transient noise, or glitches, in gravitational-wave detector data can affect gravitational-wave searches, parameter estimation, and downstream analyses. To identify and mitigate transient noise near gravitational-wave candidates in a timely manner, the LIGO-Virgo-KAGRA Collaboration employs the Data Quality Report. In the fourth observing run, GSpyNetTree-O4 was deployed within this framework as a tool for glitch classification and event validation. We describe GSpyNetTree-O4 and the main developments relative to its predecessor, GSpyNetTree. The most important update was a new architecture that allowed the simultaneous identification of glitches and gravitational-wave signals when both were present in the same input. We also expanded and augmented the training set with examples in which simulated gravitational-wave signals overlapped with real glitches, and applied $60\,\mathrm{Hz}$ calibration corrections to better match the data expected during the fourth observing run. On test data, the low-mass, high-mass, and extremely high-mass classifiers identified $97.9\%$, $97.7\%$, and $95.4\%$ of glitches, respectively. Among samples without a glitch, including gravitational-wave-only and No Glitch samples, the classifiers correctly reported no data-quality issues in $97.1\%$, $96.6\%$, and $96.0\%$ of cases, respectively. We further assessed the robustness of GSpyNetTree-O4 on unseen glitch morphologies, a small set of Virgo glitches from the fourth observing run, and different choices of the $Q$-value used to construct the time-frequency inputs. GSpyNetTree-O4 was successfully deployed as a Data Quality Report tool and increased automation in gravitational-wave event validation workflows.

gr-qc↗

arXiv2026

Nowhere left to hide: revealing realistic gravitational-wave populations in high dimensions and high resolution with PixelPop

The origins of merging compact binaries observed by the LIGO-Virgo-KAGRA gravitational-wave detectors remain uncertain, with multiple astrophysical channels possibly contributing to the merger rate. Formation processes can imprint nontrivial correlations in the underlying distribution of source properties, but current understanding of the overall population relies heavily on simplified and uncorrelated parametric models. In this work, we use PixelPop-a high-resolution Bayesian nonparametric model with minimal assumptions-to analyze multidimensional correlations in the astrophysical distribution of masses, spins, and redshifts of black-hole mergers from mock gravitational-wave catalogs constructed using population-synthesis simulations. With full parameter estimation on 400 detections at current sensitivities, we show explicitly that neglecting population-level correlations biases inference. In contrast, modeling all significant correlations with PixelPop allows us to correctly measure the astrophysical merger rate across all source parameters. We then propose a nonparametric method to distinguish between different formation channels by comparing the PixelPop results back to astrophysical simulations. For our simulated catalog, we find that only formation channels with significantly different physical processes are distinguishable, whereas channels that share evolutionary stages are not. Given the substantial uncertainties in source formation, our results highlight the necessity of multidimensional astrophysics-agnostic models like PixelPop for robust interpretation of gravitational-wave catalogs.

astro-ph.HE↗

arXiv2026

Evidence for additional structure in the effective spin distribution hints at multiple formation pathways in GWTC-5.0

The distribution of the effective inspiral spin ($χ_\mathrm{eff}$) of the binary black holes detected by LIGO-Virgo-KAGRA can shed light on their formation pathways. We analyze the GWTC-5.0 dataset with two models-one flexible, one fully parametric-that jointly describe $χ_\mathrm{eff}$ and primary mass. We clarify that the previously-reported skewness in the $χ_\mathrm{eff}$ distribution is better understood as additional structure beyond a non-skewed Gaussian bulk centered at small $χ_\mathrm{eff}$. This additional structure extends to larger $|χ_\mathrm{eff}|$, a result previously reported using GWTC-4.0 data. We measure the asymmetry of the distribution of $χ_\mathrm{eff}$ outside the Gaussian bulk from the data. With both the parametric and the flexible analyses, we find suggestive evidence for a mass-dependent excess of positive $χ_\mathrm{eff}$ over negative ones outside the Gaussian bulk. We find only a mild statistical preference for a negative $χ_\mathrm{eff}$ component outside the Gaussian bulk. This preference is highest at $m_1 \in [47,65]\,M_\odot$, with $13\text{:}1$ odds. Excluding the event GW191109, whose data have known data-quality issues, reduces these odds to $5\text{:}1$. If $χ_\mathrm{eff}$ outside the Gaussian bulk are produced by hierarchical mergers-as has been suggested-then a fraction of those mergers may be produced in environments that can generate a surplus of binaries with positive $χ_\mathrm{eff}$, such as the disks of active galactic nuclei.

astro-ph.HE↗

arXiv2026

Rapid data quality investigations of gravitational-wave events with the Data Quality Report Builder toolkit

We present the Data Quality Report Builder toolkit, DQRbuild, a suite of data quality tools that have been developed to vet gravitational-wave events in preparation for the fourth LIGO-Virgo-KAGRA observing run. We explain the main functionality and the many scientific tests that we support. To validate the performance of the tools included in the toolkit, we run a series of tests on all significant candidates shared as public alerts in the third observing run to compare against what was manually reported using human intervention. We find that these automated tools can now identify 96% of the problems identified by humans during this previous observing run, with a 24% false alarm rate. We conclude with a commentary on the prospects and potential challenges for fully automating the process of vetting the data quality for gravitational-wave events identified in future observing runs.

astro-ph.IM↗

arXiv2026

Measurement prospects for the pair-instability mass cutoff with gravitational waves

Pair-instability supernovae leave behind no compact remnants, resulting in a predicted gap in the distribution of stellar black-hole masses. Gravitational waves from binary black-hole mergers probe the relevant mass range and analyses of the LIGO-Virgo-KAGRA catalog (GWTC-4) indicate a possible mass cutoff at $40$-$50\,M_\odot$. However, the robustness of this result remains unclear. To this end, using full Bayesian parameter estimation, we simulate gravitational-wave catalogs with and without such a mass cutoff, then test whether its presence or absence is correctly inferred with parametric population models. For catalogs similar to GWTC-4, confident identification of a cutoff is not guaranteed, but the best constraints among our simulations are compatible with results from GWTC-4 when the model includes a cutoff. Conversely, spurious identification of a cutoff is unlikely. For catalogs expected by the end of the O4 observing run, uncertainty in the cutoff mass is reduced by $\gtrsim20\,\%$, but a cutoff at 40-50$M_\odot$ yields only a lower bound on the $^{12}\mathrm{C}(α,γ)^{16}\mathrm{O}$ reaction rate, our most stringent constraints on the S-factor at $300\,\mathrm{keV}$ being $S_{300}\gtrsim125\,\mathrm{keV}\,\mathrm{b}$ at $90\,\%$ credibility. Relative uncertainties on the Hubble parameter $H_0$ from gravitational-wave data alone can still be up to $100\,\%$. We also analyze GWTC-4 with the nonparametric PixelPop population model, finding that some mass features are more prominent than in parametric models but a sharp cutoff is not required. However, the parametric model passes a likelihood-based predictive test in GWTC-4 and the PixelPop results are consistent with those from our simulated catalogs with a cutoff. Such tests are necessary to make astrophysical claims from gravitational-wave catalogs.

astro-ph.HE↗

arXiv2023

GSpyNetTree: A signal-vs-glitch classifier for gravitational-wave event candidates

Despite achieving sensitivities capable of detecting the extremely small amplitude of gravitational waves (GWs), LIGO and Virgo detector data contain frequent bursts of non-Gaussian transient noise, commonly known as 'glitches'. Glitches come in various time-frequency morphologies, and they are particularly challenging when they mimic the form of real GWs. Given the higher expected event rate in the next observing run (O4), LIGO-Virgo GW event candidate validation will require increased levels of automation. Gravity Spy, a machine learning tool that successfully classified common types of LIGO and Virgo glitches in previous observing runs, has the potential to be restructured as a signal-vs-glitch classifier to accurately distinguish between glitches and GW signals. A signal-vs-glitch classifier used for automation must be robust and compatible with a broad array of background noise, new sources of glitches, and the likely occurrence of overlapping glitches and GWs. We present GSpyNetTree, the Gravity Spy Convolutional Neural Network Decision Tree: a multi-CNN classifier using CNNs in a decision tree sorted via total GW candidate mass tested under these realistic O4-era scenarios.

gr-qc↗