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Tejaswi Venumadhav

Publications and source records attributed to Tejaswi Venumadhav.

At least 37 records · Page 2Linked to original sources

Data-driven extraction and phenomenology of eccentric harmonics in eccentric spinning binary black hole mergers

Newtonian and post-Newtonian (PN) calculations indicate that the phenomenology of eccentric binary black hole (BBH) merger waveforms is significantly more complex than that of their quasi-circular counterparts. Each spherical harmonic mode of the radiation can be further decomposed into several eccentricity-induced components, referred to as eccentric harmonics. Unlike the (cumulative) spherical harmonic modes, these constituent eccentric harmonics exhibit monotonically time-varying amplitudes and frequencies. However, these eccentric harmonics are not directly accessible in numerical relativity (NR) simulations or current eccentric waveform models. Using the recently developed data-driven framework gwMiner, which combines singular value decomposition, input from post-Newtonian theory, and signal processing techniques, we extract eccentric harmonics from eccentric, aligned-spin waveforms for six different spherical harmonic modes: (2,1), (2,2), (3,2), (3,3), (4,3), (4,4). We demonstrate that the phase (frequency) of each eccentric harmonic takes the form $j\,ϕ_{\ell,m,λ} + ϕ_{\ell,m,\rm ecc}$ ($j\,f_{\ell,m,λ} + f_{\ell,m,\rm ecc}$), where $ϕ_{\ell,m,λ}$ ($f_{\ell,m,λ}$) corresponds to the secular orbital phase (frequency), and $ϕ_{\ell,m,\rm ecc}$ ($f_{\ell,m,\rm ecc}$) is an additional contribution that depends solely on the eccentricity. We further find that $ϕ_{\ell,m,λ}$ is the same across different spherical harmonic modes $(\ell, m)$, whereas the eccentric correction term $ϕ_{\ell,m,\rm ecc}$ scales with $\ell$. Using effective-one-body dynamics, we further show that $ϕ_{\ell,m,λ}$ is nothing but the relativistic anomaly and $ϕ_{\ell,m,\rm ecc}$ is related to the precession advances.

gr-qc↗

Primordial magnetic fields and modified recombination histories

Recent cosmological data and astrophysical observations, such as the Hubble tension and the increasing preference from galaxy surveys for dynamical dark energy, have begun to challenge the standard $Λ$-cold dark matter cosmological model. Primordial magnetic fields (PMFs) offer a mechanism to alleviate these tensions within the framework of the standard model. These fields source excess small-scale baryon clumping, which can speed up recombination and shrink the comoving sound horizon at the surface of last scattering. Computing the modified recombination history requires coupling the radiative transport of Lyman-$α$ photons to compressible magnetohydronamic simulations. Since doing so is generically computationally intractable, we have developed a linearized treatment which self-consistently computes the modified recombination history in the presence of PMF induced baryon clumping for fields with red-tilted spectra. The clumping factors we find are too small to alleviate outstanding cosmological tensions, but our general framework can be applied to other PMF spectra, and provides a significant theoretical step towards a complete account of recombination in the presence of small-scale baryon clumping.

astro-ph.CO↗

Sampler-free gravitational wave inference using matrix multiplication

Parameter estimation (PE) for compact binary coalescence (CBC) events observed by gravitational wave (GW) laser interferometers is a core task in GW astrophysics. We present a method to compute the posterior distribution efficiently without relying on stochastic samplers. First, we show how to select sets of intrinsic and extrinsic parameters that efficiently cover the relevant phase space. We then show how to compute the likelihood for all combinations of these parameters using dot products. We describe how to assess and tune the integration accuracy, making the outcome predictable and adaptable to different applications. The low computational cost allows full PE in minutes on a single CPU, with the potential for further acceleration using multiple CPUs or GPUs. We implement this method in the $\texttt{dot-PE}$ package, enabling sensitive searches using the full evidence integral for precessing CBCs and supporting large waveform banks ($\sim10^5$--$10^6$ waveforms), regardless of waveform generation cost.

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A New Search Pipeline for Short Gamma Ray Bursts in Fermi/GBM Data -- A 50% Increase in the Number of Detections

In this paper, we present the development and the results of a new search pipeline for short gamma-ray bursts (sGRBs) in the publicly available data from the Gamma-Ray Burst Monitor (GBM) on board the Fermi satellite. This pipeline uses rigorous statistical methods that are designed to maximize the information extracted from the Fermi/GBM detectors. Our approach differs substantially from existing search efforts in several aspects: The pipeline includes the construction of template banks, Poisson matched filtering, background estimation, background misestimation correction, automatic routines to filter contaminants, statistical estimation of the signal location and a quantitative estimator of the signal probability to be of a cosmological, terrestrial, or solar origin. Our analysis also includes operating the pipeline on "time-slided" copies of the data, which allows exact significance assessment and $p_{\text{astro}}$ computation, akin to the state-of-the-art gravitational waves (GW) data analysis pipelines. Depending on the spectral properties of the bursts, our pipeline achieves a signal-to-noise ratio (SNR) improvement by a factor of 2 to 15 over the onboard GBM triggering algorithm. This enhancement increases the detectable volume for sGRBs and results in an approximate 50% increase in sGRB detections in the 2014 GBM dataset. As a further consequence of the sensitivity increase, we detect hundreds of soft gamma-ray flares of galactic origin. This improved sensitivity enhances the chances of detecting fainter, off-axis GRBs that would likely fall below the standard triggering thresholds. Applying this pipeline to the full GBM archive is expected to expand further the joint sGRB-GW detection volume.

astro-ph.HE↗

New black hole mergers in the LIGO-Virgo O3 data from a gravitational wave search including higher-order harmonics

Nearly all of the previous gravitational wave (GW) searches in the LIGO-Virgo data included GW waveforms with only the dominant quadrupole harmonic, i.e., omitting higher-order harmonics which are predicted by general relativity. We improved the IAS pipeline by efficiently introducing higher harmonics in the GW templates using the techniques in Wadekar et al. [1, 2]. Using the IAS-HM pipeline on the public LIGO-Virgo data from the O3 run, we find 11 new candidate BBH mergers with $0.52\leq p_\mathrm{astro}\leq 0.88$ (we use the detection threshold as the astrophysical probability, $p_\mathrm{astro}$, being over 0.5, following the approach of other pipelines). We broadly recover the high-significance events from earlier catalogs, except a few which were vetoed. We also find that including higher harmonics in our search raises the significance of a few previously reported marginal events (e.g., GW190711_030756). A few notable properties of our new candidate events are as follows. At $>95$% credibility, 4 candidates have primary masses in the intermediate-mass black hole (IMBH) range (i.e., above $\sim$100 $M_\odot$). 5 candidates have median mass ratio $q \leq 0.5$. 5 candidates have median redshift $z \geq 0.8$. 3 candidates have non-zero $χ_{\rm eff}$ at $>95\%$ credibility. While our new candidate events have modest false alarm rates ($\gtrsim 1.5 $/yr), a population inference study including these can better inform the parameter space of BHs corresponding to the pair instability mass gap, high redshifts and asymmetric mass ratios.

gr-qc↗

Improving gravitational wave search sensitivity with TIER: Trigger Inference using Extended strain Representation

We introduce a machine learning (ML) framework called $\texttt{TIER}$ for improving the sensitivity of gravitational wave search pipelines. Typically, search pipelines only use a small region of strain data in the vicinity of a candidate signal to construct the detection statistic. However, extended strain data ($\sim 10$ s) in the candidate's vicinity can also carry valuable complementary information. We show that this information can be efficiently captured by ML classifier models trained on sparse summary representation/features of the extended data. Our framework is easy to train and can be used with already existing candidates from any search pipeline, and without requiring expensive injection campaigns. Furthermore, the output of our model can be easily integrated into the detection statistic of a search pipeline. Using $\texttt{TIER}$ on triggers from the $\texttt{IAS-HM}$ pipeline, we find up to $\sim 20\%$ improvement in sensitive volume time in LIGO-Virgo-Kagra O3 data, with improvements concentrated in regions of high masses and unequal mass ratios. Applying our framework increases the significance of several near-threshold gravitational-wave candidates, especially in the pair-instability mass gap and intermediate-mass black hole (IMBH) ranges.

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Searching for intermediate mass ratio binary black hole mergers in the third observing run of LIGO-Virgo-KAGRA

Intermediate mass ratio inspirals (IMRIs) of binary black holes with mass ratios $10^{-4}\lesssim q \lesssim 0.1$ are astrophysically interesting sources of gravitational waves. Mergers of intermediate-mass black holes (IMBHs) with stellar-mass black holes would be IMRIs, so their detection can help us probe the formation mechanisms of IMBHs. They can also help us perform precise tests of general relativity due to the presence of strong higher-order mode emission. We perform a search for aligned-spin IMRIs within the data of the two LIGO detectors in the third observing run (O3) of the LIGO-Virgo-KAGRA (LVK) collaboration, including higher modes in the template banks for the first time. We use the IAS-HM pipeline for our search and construct template banks in the range $1/100 < q<1/18$ using the SEOBNRv5HM waveform model. Our banks retain a similar level of effectualness for IMRPhenomXHM and BHPTNRSur2dq1e3 waveforms, making our search results relatively robust against waveform systematics. We show that the sensitivity volume of the search increases by up to $\sim 500\%$ upon inclusion of higher modes. We do not find any significant candidates with inverse false alarm rate (IFAR) $> 1$ year in the O3 data. This gives us upper limits on the IMRI merger rate in the local Universe, ranging from $\sim 30$ to $10^3$ Gpc$^{-3}$ yr$^{-1}$ depending on the masses of the black holes in the binary. These constraints are consistent with rate predictions in the literature. Our projections indicate that we would be able to detect IMRIs or constrain some of their proposed formation channels in the fourth (O4) and fifth (O5) observing runs.

gr-qc↗

gwharmone: first data-driven surrogate for eccentric harmonics in binary black hole merger waveforms

We present gwharmone, the first data-driven surrogate model for eccentric harmonics (as well as the full radiation content) of the dominant quadrupolar mode in eccentric, non-spinning binary black hole mergers. Our model is trained on 173 waveforms, each $100,000M$ long (where $M$ is the total mass), generated for mass ratios $q \in [1,3.5]$ and eccentricities $e_{\rm ref} \in [0,0.2]$ (at the start of the waveform). The eccentric harmonics are extracted from the effective-one-body waveforms using the \texttt{gwMiner} package. We apply a singular value decomposition (SVD) to obtain a set of reduced basis vectors, necessary to construct a lower-dimensional representation of data, and use Gaussian Process Regression (GPR) to interpolate SVD coefficients across parameter space, allowing for prediction at new parameter points. The model includes the effect of mean anomaly, its evaluation cost is only $\sim 0.1$ second and it achieves an average time-domain (validation) error of ~0.001 and frequency-domain (validation) mismatches below 0.01 for advanced LIGO sensitivity. Our model can therefore be useful in efficient searches and parameter estimation of eccentric mergers. gwharmone will be publicly available through the gwModels package.

astro-ph.HE↗

Resonance Locking of Anharmonic $g$-Modes in Coalescing Neutron Star Binaries

Neutron stars in coalescing binaries deform due to the tidal gravitational fields generated by their companions. During the inspiral phase, the tidal deformation is dominated by the fundamental oscillation ($f$-) mode of the stars. The tide also has sub-dominant gravity ($g$-) modes that are resonantly excited when the linear tidal forcing sweeps through their eigenfrequencies. Beyond the linear order in perturbed fluid displacement, the $g$-modes are anharmonic, i.e., their oscillation frequencies depend on the mode energy. For the lowest-order $g$-mode, we show that when the tidal forcing reaches its linear eigenfrequency, the mode starts to dynamically adjust its energy so that its nonlinearly shifted oscillation frequency always matches that of the driving field. This phenomenon, which we term `resonance locking', persists through the rest of the inspiral, and hence, the mode grows to substantially larger energies than in the linear theory. Using a $1.4$--$1.4\, M_{\odot}$ binary neutron star system with the SLy4 equation of state, we find this results in an extra correction to the frequency-domain gravitational wave (GW) phase of $|ΔΨ|\approx 3\,{\rm rad}$ accumulated from the onset of resonance locking at the GW frequency of $94\,{\rm Hz}$ to the merger at $1.05\,{\rm kHz}$. This effect probes details of the internal structure of merging neutron stars beyond their bulk properties such as tidal deformability.

gr-qc↗

Resonance locking: radian-level phase shifts due to nonlinear hydrodynamics of $g$-modes in merging neutron star binaries

A neutron star (NS) in a binary system deforms due to the companion's tidal gravitational field. As the binary inspirals due to gravitational wave (GW) emission, the NS's deformation evolves; this evolution is typically modeled as the star's linear response to the companion's time-evolving tidal potential. In principle, the fluid elements' displacements can be excited and evolve nonlinearly since the equations of hydrodynamics and the tidal forcing have nonlinear terms. Recently, Kwon, Yu, and Venumadhav (KYV I [arXiv:2410.03831]) showed that nonlinear terms in the hydrodynamic equations of motion make the low-frequency response of NSs, characterized by gravity ($g$-) modes, behave in an anharmonic manner. The anharmonicity is dominantly generated by the mutual coupling of the four lowest-order ($n=1$, $l=|m|=2$) $g$-modes, and allows them to stay locked in a resonant state that oscillates phase-coherently with the orbit throughout the inspiral. As a result, the $g$-modes grow to larger amplitudes than the linear response suggests, leading to an extra phase correction to the frequency-domain GW signal $|ΔΨ|\approx 3\,{\rm rad}$ at a GW frequency of $1.05\,{\rm kHz}$. This effect is part of the truly dynamical tide, in the sense that the amplitude depends not just on the binary's instantaneous frequency but the entire history of the inspiral. In this paper, we explain the phenomenology of resonance locking in detail and analytically validate the numerical dephasing calculations in KYV I. We also demonstrate that the effect is only significant for the lowest-order $g$-modes.

gr-qc↗

Significant increase in sensitive volume of a gravitational wave search upon including higher harmonics

Most gravitational wave searches to date have included only the quadrupole mode in their search templates. Here, we demonstrate that incorporating higher harmonics improves the search sensitive volume for detecting binary black hole mergers, challenging the conclusion of previous studies. Using the $\tt{IAS-HM}$ detection pipeline, and the simulated (injection) signals from the LIGO-Virgo-Kagra (LVK) collaboration, we quantify the improvement in sensitivity due to the inclusion of higher harmonics. This improvement is significant for systems with higher mass ratios and larger total masses, with gains in sensitivity even exceeding $100\%$ at certain high masses. We also show that, due to using a marginalized detection statistic, the $\tt{IAS-HM}$ pipeline performs roughly as well as its quadrupole-mode-only counterpart even for equal mass-ratio mergers, and its sensitive volume is either better than or comparable to that of the individual LVK pipelines.

gr-qc↗

New binary black hole mergers in the LIGO-Virgo O3b data

We report the detection of 6 new candidate binary black hole (BBH) merger signals in the publicly released data from the second half of the third observing run (O3b) of advanced LIGO and advanced Virgo. The LIGO-Virgo-KAGRA (LVK) collaboration reported 35 compact binary coalescences (CBCs) in their analysis of the O3b data [1], with 30 BBH mergers having coincidence in the Hanford and Livingston detectors. We confirm 17 of these for a total of 23 detections in our analysis of the Hanford-Livingston coincident O3b data. We identify candidates using a search pipeline employing aligned-spin quadrupole-only waveforms. Our pipeline is similar to the one used in our O3a coincident analysis [2], except for a few improvements in the veto procedure and the ranking statistic, and we continue to use an astrophysical probability of one half as our detection threshold, following the approach of the LVK catalogs. Most of the new candidates reported in this work are placed in the upper and lower-mass gap of the black hole (BH) mass distribution. We also identify a possible neutron star-black hole (NSBH) merger. We expect these events to help inform the black hole mass and spin distributions inferred in a full population analysis.

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Strong-lensing cosmography using third-generation gravitational-wave detectors

We present a detailed exposition of a statistical method for estimating cosmological parameters from the observation of a large number of strongly lensed binary-black-hole (BBH) mergers observable by next (third) generation (XG) gravitational-wave (GW) detectors. This method, first presented in Jana (2023 Phys. Rev. Lett. 130 261401), compares the observed number of strongly lensed GW events and their time delay distribution (between lensed images) with observed events to infer cosmological parameters. We show that the precision of the estimation of the cosmological parameters does not have a strong dependance on the assumed BBH redshift distribution model. Using the large number of unlensed mergers, XG detectors are expected to measure the BBH redshift distribution with sufficient precision for the cosmological inference. However, a biased inference of the BBH redshift distribution will bias the estimation of cosmological parameters. An incorrect model for the distribution of lens properties can also lead to a biased cosmological inference. However, Bayesian model selection can assist in selecting the right model from a set of available parametric models for the lens distribution. We also present a way to incorporate the effect of contamination in the data due to the limited efficiency of lensing identification methods, so that it will not bias the cosmological inference.

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New approach to template banks of gravitational waves with higher harmonics: Reducing matched-filtering cost by over an order of magnitude

Searches for gravitational wave events use models, or templates, for the signals of interest. The templates used in current searches in the LIGO-Virgo-Kagra (LVK) data model the dominant quadrupole mode $(\ell,|m|)=(2,2)$ of the signals, and omit sub-dominant higher-order modes (HM) such as $(\ell,|m|)=(3,3)$, $(4,4)$, which are predicted by general relativity. This omission reduces search sensitivity to black hole mergers in interesting parts of parameter space, such as systems with high masses and asymmetric mass-ratios. We develop a new strategy to include HM in template banks: instead of making templates containing a combination of different modes, we separately store normalized templates corresponding to $(2,2)$, $(3,3)$ and $(4,4)$ modes. To model aligned-spin $(3,3)$, $(4,4)$ waveforms corresponding to a given $(2,2)$ waveform, we use a combination of post-Newtonian formulae and machine learning tools. In the matched filtering stage, one can filter each mode separately with the data and collect the timeseries of signal-to-noise ratios (SNR). This leads to a HM template bank whose matched-filtering cost is just $\approx 3\times$ that of a quadrupole-only search (as opposed to $\approx\! 100 \times$ in previously proposed HM search methods). Our method is effectual and generally applicable for template banks constructed with either stochastic or geometric placement techniques. New GW candidate events that we detect using our HM banks and details for combining the different SNR mode timeseries are presented in accompanying papers: Wadekar et al. [1] and [2] respectively. Additionally, we discuss non-linear compression of $(2,2)$-only geometric-placement template banks using machine learning algorithms.

gr-qc↗

New search pipeline for gravitational waves with higher-order modes using mode-by-mode filtering

Nearly all template-based gravitational wave (GW) searches only include the quasi-circular quadrupolar modes of the signals in their templates. Including additional degrees of freedom in the GW templates corresponding to higher-order harmonics, orbital precession, or eccentricity is challenging because: ($i$) the size of template banks and the matched-filtering cost increases significantly with the number of degrees of freedom, $(ii)$ if these additional degrees are not included properly, the search can lose sensitivity overall (due to an increase in the rate of background triggers). Here, we focus on including aligned-spin higher harmonics in GW search templates. We use a new mode-by-mode filtering approach, where we separately filter GW strain data with three harmonics [namely $(\ell, |m|)=(2,2)$, $(3,3)$ and $(4,4)$]. This results in an increase in the matched-filtering cost by only a factor of $3$ compared to that of a $(2,2)$-only search. We develop computationally cheap trigger-ranking statistics to optimally combine the different signal-to-noise ratios (SNR) timeseries from different harmonics, which ensure only physically-allowed combinations of the different harmonics are triggered on. We use an empirical template-dependent background model in our ranking statistic to account for non-Gaussian transients. In addition, we develop a tool called band eraser which specifically excises narrow time-varying noisy bands in time-frequency space (without having to excise entire time chunks in the data). New GW candidate events that we detect using our $\texttt{IAS-HM}$ search pipeline and the details of our template banks are discussed in accompanying papers: Wadekar et al. [1] and [2] respectively. Apart from higher harmonics, we expect our methodology to also be useful for cheap and optimal searches including orbital precession and eccentricity in GW waveforms.

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Probing the nature of dark matter using strongly lensed gravitational waves from binary black holes

Next-generation ground-based gravitational-wave (GW) detectors are expected to detect millions of binary black hole mergers during their operation period. A small fraction ($\sim 0.1 - 1\%$) of them will be strongly lensed by intervening galaxies and clusters, producing multiple copies of the GW signals. The expected number of lensed events and the distribution of the time delay between lensed images will depend on the mass distribution of the lenses at different redshifts. Warm dark matter or fuzzy dark matter models predict lower abundances of small mass dark matter halos as compared to the standard cold dark matter. This will result in a reduction in the number of strongly lensed GW events, especially at small time delays. Using the number of lensed events and the lensing time delay distribution, we can put a lower bound on the mass of the warm/fuzzy dark matter particle from a catalog of lensed GW events. The expected bounds from GW strong lensing from next-generation detectors are significantly better than the current constraints.

astro-ph.CO↗

Fast marginalization algorithm for optimizing gravitational wave detection, parameter estimation and sky localization

We introduce an algorithm to marginalize the likelihood for a gravitational wave signal from a quasi-circular binary merger over its extrinsic parameters, accounting for the effects of higher harmonics and spin-induced precession. The algorithm takes as input the matched-filtering time series of individual waveform harmonics against the data in all operational detectors, and the covariances of the harmonics. The outputs are the Gaussian likelihood marginalized over extrinsic parameters describing the merger time, location and orientation, along with samples from the conditional posterior of these parameters. Our algorithm exploits the waveform's known analytical dependence on extrinsic parameters to efficiently marginalize over them using a single waveform evaluation. Our current implementation achieves a 10% precision on the marginalized likelihood within $\approx 50$ ms on a single CPU core and is publicly available through the package `cogwheel`. We discuss applications of this tool for gravitational wave searches involving higher modes or precession, efficient and robust parameter estimation, and generation of sky localization maps in low latency for electromagnetic followup of gravitational-wave alerts. The inclusion of higher modes can improve the distance measurement, providing an advantage over existing low-latency localization methods.

gr-qc↗

Inferring Binary Properties from Gravitational Wave Signals

This review provides a conceptual and technical survey of methods for parameter estimation of gravitational wave signals in ground-based interferometers such as LIGO and Virgo. We introduce the framework of Bayesian inference and provide an overview of models for the generation and detection of gravitational waves from compact binary mergers, focusing on the essential features that are observable in the signals. Within the traditional likelihood-based paradigm, we describe various approaches for enhancing the efficiency and robustness of parameter inference. This includes techniques for accelerating likelihood evaluations, such as heterodyne/relative binning, reduced-order quadrature, multibanding and interpolation. We also cover methods to simplify the analysis to improve convergence, via reparametrization, importance sampling and marginalization. We end with a discussion of recent developments in the application of likelihood-free (simulation-based) inference methods to gravitational wave data analysis.

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