arXiv subjects

Collin Capano

Publications and source records attributed to Collin Capano.

14 recordsLinked to original sources

arXiv2026

DiSCoKit: An Open-Source Toolkit for Deploying Live LLM Experiences in Survey Research

Advancing social-scientific research of human-AI interaction dynamics and outcomes often requires researchers to deliver experiences with live large-language models (LLMs) to participants through online survey platforms. However, technical and practical challenges (from logging chat data to manipulating AI behaviors for experimental designs) often inhibit survey-based deployment of AI stimuli. We developed DiSCoKit--an open-source toolkit for deploying live LLM experiences (e.g., ones based on models delivered through Microsoft Azure portal) through JavaScript-enabled survey platforms (e.g., Qualtrics). This paper introduces that toolkit, explaining its scientific impetus, describes its architecture and operation, as well as its deployment possibilities and limitations.

cs.HC↗

arXiv2025

Direct Inference of Nuclear Equation-of-State Parameters from Gravitational-Wave Observations

The observation of neutron star mergers with gravitational waves (GWs) has provided a new method to constrain the dense-matter equation of state (EOS) and to better understand its nuclear physics. However, inferring nuclear microphysics from GW observations necessitates the sampling of EOS model parameters that serve as input for each EOS used during the GW data analysis. The sampling of the EOS parameters requires solving the Tolman-Oppenheimer-Volkoff (TOV) equations a large number of times -- a process that slows down each likelihood evaluation in the analysis on the order of a few seconds. Here, we employ emulators for the TOV equations built using multilayer perceptron neural networks to enable direct inference of nuclear EOS parameters from GW strain data. Our emulators allow us to rapidly solve the TOV equations, taking in EOS parameters and outputting the associated tidal deformability of a neutron star in only a few tens of milliseconds. We implement these emulators in \texttt{PyCBC} to directly infer the EOS parameters using the event GW170817, providing posteriors on these parameters informed solely by GWs. We benchmark these runs against analyses performed using the full TOV solver and find that the emulators achieve speed ups of nearly \emph{two orders of magnitude}, with negligible differences in the recovered posteriors. Additionally, we constrain the slope and curvature of the symmetry energy at the 90\% upper credible interval to be $L_{\rm sym}\lesssim106$ MeV and $K_{\rm sym}\lesssim26$ MeV.

astro-ph.HE↗

arXiv2025

Multiband parameter estimation with phase coherence and extrinsic marginalization: Extracting more information from low-SNR CBC signals in LISA data

This paper presents a novel coherent multiband analysis framework for characterizing stellar- and intermediate-mass binary black holes using LISA and next-generation ground-based detectors (ET and CE), leveraging the latest developments in the \texttt{PyCBC} pipeline. Given the population parameters inferred from LVK results and LISA's sensitivity limits at high frequencies, most stellar-mass binary black holes would likely have SNRs below 5 in LISA, but the most state-of-the-art multiband parameter estimation methods, such as those using ET and CE posteriors as priors for LISA, typically struggle to analyze sources with a LISA SNR less than 5. We present a novel coherent multiband parameter estimation method that directly calculates a joint likelihood, which is highly efficient; this efficiency is enabled by multiband marginalization of the extrinsic parameter space, implemented using importance sampling, which can work robustly even when the LISA SNR is as low as 3. Having an SNR of $\sim 3$ allows LISA to contribute nearly double the number of multiband sources. Even if LISA only observes for one year, most of the multiband detector-frame chirp mass's 90\% credible interval (less than $10^{-4} \mathrm{M}_\odot$) is still better than that of the most accurately measured events for ET+2CE network in 7.5 years of observation, by at least one order of magnitude. For the first time, we show efficient multiband Bayesian parameter estimation results on the population scale, which paves the way for large-scale astrophysical tests using multibanding.

gr-qc↗

arXiv2025

Black hole spectroscopy: from theory to experiment

The "ringdown" radiation emitted by oscillating black holes has great scientific potential. By carefully predicting the frequencies and amplitudes of black hole quasinormal modes and comparing them with gravitational-wave data from compact binary mergers we can advance our understanding of the two-body problem in general relativity, verify the predictions of the theory in the regime of strong and dynamical gravitational fields, and search for physics beyond the Standard Model or new gravitational degrees of freedom. We summarize the state of the art in our understanding of black hole quasinormal modes in general relativity and modified gravity, their excitation, and the modeling of ringdown waveforms. We also review the status of LIGO-Virgo-KAGRA ringdown observations, data analysis techniques, and the bright prospects of the field in the era of LISA and next-generation ground-based gravitational-wave detectors.

gr-qc↗

arXiv2024

Ameliorating transient noise bursts in gravitational-wave searches for intermediate-mass black holes

The direct observation of intermediate-mass black holes (IMBH) populations would not only strengthen the possible evolutionary link between stellar and supermassive black holes, but unveil the details of the pair-instability mechanism and elucidate their influence in galaxy formation. Conclusive observation of IMBHs remained elusive until the detection of gravitational-wave (GW) signal GW190521, which lies with high confidence in the mass gap predicted by the pair-instability mechanism. Despite falling in the sensitivity band of current GW detectors, IMBH searches are challenging due to their similarity to transient bursts of detector noise, known as glitches. In this proof-of-concept work, we combine a matched-filter algorithm with a Machine Learning (ML) method to differentiate IMBH signals from non-transient burst noise, known as glitches. In particular, we build a multi-layer perceptron network to perform a multi-class classification of the output triggers of matched-filter. In this way we are able to distinguish simulated GW IMBH signals from different classes of glitches that occurred during the third observing run (O3) {in single detector data}. {We train, validate, and test our model on O3a data, reaching a true positive rate of over $90\%$ for simulated IMBH signals. To test the generalization ability over the evolutionary observing run, we test on the useen data of O3b, which yields a true positive rate of over $70\%$} . We also combine data from multiple detectors to search for simulated IMBH signals in real detector noise, providing a significance measure for the output of our ML method.

astro-ph.IM↗

arXiv2024

Towards accelerated nuclear-physics parameter estimation from binary neutron star mergers: Emulators for the Tolman-Oppenheimer-Volkoff equations

Gravitational-wave observations of binary neutron-star (BNS) mergers have the potential to revolutionize our understanding of the nuclear equation of state (EOS) and the fundamental interactions that determine its properties. However, Bayesian parameter estimation frameworks do not typically sample over microscopic nuclear-physics parameters that determine the EOS. One of the major hurdles in doing so is the computational cost involved in solving the neutron-star structure equations, known as the Tolman-Oppenheimer-Volkoff (TOV) equations. In this paper, we explore approaches to emulating solutions for the TOV equations: Multilayer Perceptrons (MLP), Gaussian Processes (GP), and a data-driven variant of the reduced basis method (RBM). We implement these emulators for three different parameterizations of the nuclear EOS, each with a different degree of complexity represented by the number of model parameters. We find that our MLP-based emulators are generally more accurate than the other two algorithms whereas the RBM results in the largest speedup with respect to the full, high-fidelity TOV solver. We employ these emulators for a simple parameter inference using a potentially loud BNS observation, and show that the posteriors predicted by our emulators are in excellent agreement with those obtained from the full TOV solver.

astro-ph.HE↗

arXiv2022

Apple Silicon Performance in Scientific Computing

With the release of the Apple Silicon System-on-a-Chip processors, and the impressive performance shown in general use by both the M1 and M1 Ultra, the potential use for Apple Silicon processors in scientific computing is explored. Both the M1 and M1 Ultra are compared to current state-of-the-art data-center GPUs, including an NVIDIA V100 with PCIe, an NVIDIA V100 with NVLink, and an NVIDIA A100 with PCIe. The scientific performance is measured using the Scalable Heterogeneous Computing (SHOC) benchmark suite using OpenCL benchmarks. We find that both M1 processors outperform the GPUs in all benchmarks.

cs.DC↗

arXiv2019

1-OGC: The first open gravitational-wave catalog of binary mergers from analysis of public Advanced LIGO data

We present the first Open Gravitational-wave Catalog (1-OGC), obtained by using the public data from Advanced LIGO's first observing run to search for compact-object binary mergers. Our analysis is based on new methods that improve the separation between signals and noise in matched-filter searches for gravitational waves from the merger of compact objects. The three most significant signals in our catalog correspond to the binary black hole mergers GW150914, GW151226, and LVT151012. We assume a common population of binary black holes for these three signals by defining a region of parameter space that is consistent with these events. Under this assumption, we find that LVT151012 has a 97.6\% probability of being astrophysical in origin. No other significant binary black hole candidates are found, nor did we observe any significant binary neutron star or neutron star--black hole candidates. We make available our complete catalog of events, including the sub-threshold population of candidates.

gr-qc↗

arXiv2018

Low significance of evidence for black hole echoes in gravitational wave data

Recent detections of merging black holes allow observational tests of the nature of these objects. In some proposed models, non-trivial structure at or near the black hole horizon could lead to echo signals in gravitational wave data. Recently, Abedi et al. claimed tentative evidence for repeating damped echo signals following the gravitational-wave signals of the binary black hole merger events recorded in the first observational period of the Advanced LIGO interferometers. We reanalyse the same data, addressing some of the shortcomings of their method using more background data and a modified procedure. We find a reduced statistical significance for the claims of evidence for echoes, calculating increased p-values for the null hypothesis of echo-free noise. The reduced significance is entirely consistent with noise, and so we conclude that the analysis of Abedi et al. does not provide any observational evidence for the existence of Planck-scale structure at black hole horizons.

gr-qc↗

arXiv2016

Comments on: "Echoes from the abyss: Evidence for Planck-scale structure at black hole horizons"

Recently, Abedi, Dykaar and Afshordi claimed evidence for a repeating damped echo signal following the binary black hole merger gravitational-wave events recorded in the first observational period of the Advanced LIGO interferometers. We discuss the methods of data analysis and significance estimation leading to this claim, and identify several important shortcomings. We conclude that their analysis does not provide significant observational evidence for the existence of Planck-scale structure at black hole horizons, and suggest renewed analysis correcting for these shortcomings.

gr-qc↗

arXiv2016

Implementing a search for gravitational waves from non-precessing, spinning binary black holes

Searching for gravitational waves (GWs) from binary black holes (BBHs) with LIGO and Virgo involves matched-filtering data against a set of representative signal waveforms --- a template bank --- chosen to cover the full signal space of interest with as few template waveforms as possible. Although the component black holes may have significant angular momenta (spin), previous searches for BBHs have filtered LIGO and Virgo data using only waveforms where both component spins are zero. This leads to a loss of signal-to-noise ratio for signals where this is not the case. Combining the best available template placement techniques and waveform models, we construct a template bank of GW signals from BBHs with component spins $χ_{1,2}\in [-0.99, 0.99]$ aligned with the orbital angular momentum, component masses $m_{1,2}\in [2, 48]\,\mathrm{M}_\odot$, and total mass $M_\mathrm{total} \leq 50\,\mathrm{M}_\odot$. Using effective-one-body waveforms with spin effects, we show that less than $3\%$ of the maximum signal-to-noise ratio (SNR) of these signals is lost due to the discreetness of the bank, using the early advanced LIGO noise curve. We use simulated advanced LIGO noise to compare the sensitivity of this bank to a non-spinning bank covering the same parameter space. In doing so, we consider the competing effects between improved SNR and signal-based vetoes, and the increase in the rate of false alarms of the aligned-spin bank due to covering a larger parameter space. We find that the aligned-spin bank can be a factor of $1.3$ -- $5$ more sensitive than a non-spinning bank to BBHs with dimensionless spins $> +0.6$ and component masses $\gtrsim 20\,\mathrm{M}_\odot$. Even larger gains are obtained for systems with equally high spins but smaller component masses.

gr-qc↗

arXiv2016

Systematic errors in estimation of gravitational-wave candidate significance

We investigate the issue in determining the significance of candidate transient gravitational-wave events in a ground-based interferometer network. Given the presence of non-Gaussian noise artefacts in real data, the noise background must be estimated empirically from the data itself. However, the data also potentially contains signals, thus the background estimate may be overstated due to contributions from signals. It has been proposed to mitigate possible bias by removing single-detector data samples that pass a multi-detector consistency test from the background estimates. We conduct a high-statistics Mock Data Challenge to evaluate the effects of removing such samples, modelling a range of scenarios with plausible detector noise distributions and with a range of plausible foreground astrophysical signal rates. We consider the two different modes: one in which coincident samples are removed, and one in which all samples are retained and used. Three algorithms were operated in both modes, show good consistency with each other; however, discrepancies arise between the results obtained under the "coincidence removal" and "all samples" modes, for false alarm probabilities below a certain value. In most scenarios the median of the false alarm probability (FAP) estimator under the "all samples" mode is consistent with the exact FAP. On the other hand the "coincidence removal" mode is found to be unbiased for the mean of the estimated FAP. While the numerical values at which discrepancies become apparent are specific to the details of our experiment, we believe that the qualitative differences in the behaviour of the median and mean of the FAP estimator have more general validity. On the basis of our study we suggest that the FAP of candidates for the first detection of gravitational waves should be estimated without removing single-detector samples that form coincidences.

astro-ph.IM↗

arXiv2014

Multivariate Classification with Random Forests for Gravitational Wave Searches of Black Hole Binary Coalescence

Searches for gravitational waves produced by coalescing black hole binaries with total masses $\gtrsim25\,$M$_\odot$ use matched filtering with templates of short duration. Non-Gaussian noise bursts in gravitational wave detector data can mimic short signals and limit the sensitivity of these searches. Previous searches have relied on empirically designed statistics incorporating signal-to-noise ratio and signal-based vetoes to separate gravitational wave candidates from noise candidates. We report on sensitivity improvements achieved using a multivariate candidate ranking statistic derived from a supervised machine learning algorithm. We apply the random forest of bagged decision trees technique to two separate searches in the high mass $\left( \gtrsim25\,\mathrm{M}_\odot \right)$ parameter space. For a search which is sensitive to gravitational waves from the inspiral, merger, and ringdown (IMR) of binary black holes with total mass between $25\,$M$_\odot$ and $100\,$M$_\odot$, we find sensitive volume improvements as high as $70_{\pm 13}-109_{\pm 11}$\% when compared to the previously used ranking statistic. For a ringdown-only search which is sensitive to gravitational waves from the resultant perturbed intermediate mass black hole with mass roughly between $10\,$M$_\odot$ and $600\,$M$_\odot$, we find sensitive volume improvements as high as $61_{\pm 4}-241_{\pm 12}$\% when compared to the previously used ranking statistic. We also report how sensitivity improvements can differ depending on mass regime, mass ratio, and available data quality information. Finally, we describe the techniques used to tune and train the random forest classifier that can be generalized to its use in other searches for gravitational waves.

gr-qc↗

arXiv2014

Impact of Higher Harmonics in Searching for Gravitational Waves from Non-Spinning Binary Black Holes

Current searches for gravitational waves from coalescing binary black holes (BBH) use templates that only include the dominant harmonic. In this study we use effective-one-body multipolar waveforms calibrated to numerical-relativity simulations to quantify the effect of neglecting sub-dominant harmonics on the sensitivity of searches. We consider both signal-to-noise ratio (SNR) and the signal-based vetoes that are used to re-weight SNR. We find that neglecting sub-dominant modes when searching for non-spinning BBHs with component masses $3\,M_{\odot} \leq m_1, m_2 \leq 200\,M_{\odot}$ and total mass $M < 360\,M_{\odot}$ in advanced LIGO results in a negligible reduction of the re-weighted SNR at detection thresholds. Sub-dominant modes therefore have no effect on the detection rates predicted for advanced LIGO. Furthermore, we find that if sub-dominant modes are included in templates the sensitivity of the search becomes worse if we use current search priors, due to an increase in false alarm probability. Templates would need to be weighted differently than what is currently done to compensate for the increase in false alarms. If we split the template bank such that sub-dominant modes are only used when $M \gtrsim 100\,M_{\odot}$ and mass ratio $q \gtrsim 4$, we find that the sensitivity does improve for these intermediate mass-ratio BBHs, but the sensitive volume associated with these systems is still small compared to equal-mass systems. Using sub-dominant modes is therefore unlikely to substantially increase the probability of detecting gravitational waves from non-spinning BBH signals unless there is a relatively large population of intermediate mass-ratio BBHs in the universe.

gr-qc↗