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1. Do black holes remember what they are made of?

   https://doi.org/10.1088/1361-6382/ad56ed  

   Bandyopadhyay, Harshraj; Radice, David; Prakash, Aviral; Dhani, Arnab; Logoteta, Domenico; Perego, Albino; Kashyap, Rahul (June 2024, Classical and Quantum Gravity)

   Abstract We study the ringdown signal of black holes formed in prompt-collapse binary neutron star mergers. We analyze data from 47 numerical relativity simulations. We show that the $(\ell =2,m=2)$and $(\ell =2,m=1)$multipoles of the gravitational wave signal are well fitted by decaying damped exponentials, as predicted by black-hole perturbation theory. We show that the ratio of the amplitude in the two modes depends on the progenitor binary mass ratioqand reduced tidal parameter $\tilde{\mathrm{\Lambda }}$. Unfortunately, the numerical uncertainty in our data is too large to fully quantify this dependency. If confirmed, these results will enable novel tests of general relativity in the presence of matter with next-generation gravitational-wave observatories. 

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2. Physics-inspired spatiotemporal-graph AI ensemble for the detection of higher order wave mode signals of spinning binary black hole mergers

   https://doi.org/10.1088/2632-2153/ad4c37  

   Tian, Minyang; Huerta, E_A; Zheng, Huihuo; Kumar, Prayush (June 2024, Machine Learning: Science and Technology)

   Abstract We present a new class of AI models for the detection of quasi-circular, spinning, non-precessing binary black hole mergers whose waveforms include the higher order gravitational wave modes $(\ell ,|m|)=\left\{\right(2,2),(2,1),(3,3),(3,2),(4,4\left)\right\}$, and mode mixing effects in the $\ell =3,|m|=2$harmonics. These AI models combine hybrid dilated convolution neural networks to accurately model both short- and long-range temporal sequential information of gravitational waves; and graph neural networks to capture spatial correlations among gravitational wave observatories to consistently describe and identify the presence of a signal in a three detector network encompassing the Advanced LIGO and Virgo detectors. We first trained these spatiotemporal-graph AI models using synthetic noise, using 1.2 million modeled waveforms to densely sample this signal manifold, within 1.7 h using 256 NVIDIA A100 GPUs in the Polaris supercomputer at the Argonne Leadership Computing Facility. This distributed training approach exhibited optimal classification performance, and strong scaling up to 512 NVIDIA A100 GPUs. With these AI ensembles we processed data from a three detector network, and found that an ensemble of 4 AI models achieves state-of-the-art performance for signal detection, and reports two misclassifications for every decade of searched data. We distributed AI inference over 128 GPUs in the Polaris supercomputer and 128 nodes in the Theta supercomputer, and completed the processing of a decade of gravitational wave data from a three detector network within 3.5 h. Finally, we fine-tuned these AI ensembles to process the entire month of February 2020, which is part of the O3b LIGO/Virgo observation run, and found 6 gravitational waves, concurrently identified in Advanced LIGO and Advanced Virgo data, and zero false positives. This analysis was completed in one hour using one NVIDIA A100 GPU. 

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3. Measurement of off-shell Higgs boson production in the $H^{\ast }\to ZZ\to 4\ell$ decay channel using a neural simulation-based inference technique in 13 TeV pp collisions with the ATLAS detector

   https://doi.org/10.1088/1361-6633/adcd9a  

   The_ATLAS_Collaboration (May 2025, Reports on Progress in Physics)

   Abstract A measurement of off-shell Higgs boson production in the $H^{\ast }\to ZZ\to 4\ell$decay channel is presented. The measurement uses 140 fb⁻¹of proton–proton collisions at $\sqrt{s}=13$TeV collected by the ATLAS detector at the Large Hadron Collider and supersedes the previous result in this decay channel using the same dataset. The data analysis is performed using a neural simulation-based inference method, which builds per-event likelihood ratios using neural networks. The observed (expected) off-shell Higgs boson production signal strength in the $ZZ\to 4\ell$decay channel at 68% CL is ${0.87}_{-0.54}^{+0.75}$( ${1.00}_{-0.95}^{+1.04}$). The evidence for off-shell Higgs boson production using the $ZZ\to 4\ell$decay channel has an observed (expected) significance of 2.5σ(1.3σ). The expected result represents a significant improvement relative to that of the previous analysis of the same dataset, which obtained an expected significance of 0.5σ. When combined with the most recent ATLAS measurement in the $ZZ\to 2\ell 2\nu$decay channel, the evidence for off-shell Higgs boson production has an observed (expected) significance of 3.7σ(2.4σ). The off-shell measurements are combined with the measurement of on-shell Higgs boson production to obtain constraints on the Higgs boson total width. The observed (expected) value of the Higgs boson width at 68% CL is ${4.3}_{-1.9}^{+2.7}$( ${4.1}_{-3.4}^{+3.5}$) MeV. 

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4. Test of lepton flavor universality in ${\text{B}}^{\pm }\to {\text{K}}^{\pm }{\mu }^{+}{\mu }^{-}$ and ${\text{B}}^{\pm }\to {\text{K}}^{\pm }{\text{e}}^{+}{\text{e}}^{-}$ decays in proton-proton collisions at $\sqrt{\text{s}}=\text{13}\text{TeV}$

   https://doi.org/10.1088/1361-6633/ad4e65  

   CMS_Collaboration, The (July 2024, Reports on Progress in Physics)

   Abstract A test of lepton flavor universality in ${\text{B}}^{\pm }\to {\text{K}}^{\pm }{\mu }^{+}{\mu }^{-}$and ${\text{B}}^{\pm }\to {\text{K}}^{\pm }{\text{e}}^{+}{\text{e}}^{-}$decays, as well as a measurement of differential and integrated branching fractions of a nonresonant ${\text{B}}^{\pm }\to {\text{K}}^{\pm }{\mu }^{+}{\mu }^{-}$decay are presented. The analysis is made possible by a dedicated data set of proton-proton collisions at $\sqrt{s}=13\text{TeV}$recorded in 2018, by the CMS experiment at the LHC, using a special high-rate data stream designed for collecting about 10 billion unbiased b hadron decays. The ratio of the branching fractions $B({\text{B}}^{\pm }\to {\text{K}}^{\pm }{\mu }^{+}{\mu }^{-})$to $B({\text{B}}^{\pm }\to {\text{K}}^{\pm }{\text{e}}^{+}{\text{e}}^{-})$is determined from the measured double ratio $R\left(\text{K}\right)$of these decays to the respective branching fractions of the ${\text{B}}^{\pm }\to \text{J}/\text{ψ}{\text{K}}^{\pm }$with $\text{J}/\text{ψ}\to {\mu }^{+}{\mu }^{-}$and ${\text{e}}^{+}{\text{e}}^{-}$decays, which allow for significant cancellation of systematic uncertainties. The ratio $R\left(\text{K}\right)$is measured in the range $1.1<q^2<6.0{\text{GeV}}^2$, whereqis the invariant mass of the lepton pair, and is found to be $R\left(\text{K}\right)={0.78}_{-0.23}^{+0.47}$, in agreement with the standard model expectation $R\left(\text{K}\right)\approx 1$. This measurement is limited by the statistical precision of the electron channel. The integrated branching fraction in the sameq²range, $B({\text{B}}^{\pm }\to {\text{K}}^{\pm }{\mu }^{+}{\mu }^{-})=(12.42\pm 0.68)\times {10}^{-8}$, is consistent with the present world-average value and has a comparable precision. 

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5. What Does the Geometry of the Hβ BLR Depend On?

   https://doi.org/10.3847/1538-4357/accc84  

   Villafaña, Lizvette; Williams, Peter R.; Treu, Tommaso; Brewer, Brendon J.; Barth, Aaron J.; U, Vivian; Bennert, Vardha N.; Guo, Hengxiao; Bentz, Misty C.; Canalizo, Gabriela; et al (May 2023, The Astrophysical Journal)

   Abstract We combine our dynamical modeling black-hole mass measurements from the Lick AGN Monitoring Project 2016 sample with measured cross-correlation time lags and line widths to recover individual scale factors,f, used in traditional reverberation-mapping analyses. We extend our sample by including prior results from Code for AGN Reverberation and Modeling of Emission Lines (caramel) studies that have utilized our methods. Aiming to improve the precision of black-hole mass estimates, as well as uncover any regularities in the behavior of the broad-line region (BLR), we search for correlations betweenfand other AGN/BLR parameters. We find (i) evidence for a correlation between the virial coefficient ${\log }_{10}\left(f_{\mathrm{mean},\sigma }\right)$and black-hole mass, (ii) marginal evidence for a similar correlation between ${\log }_{10}\left(f_{\mathrm{rms},\sigma }\right)$and black-hole mass, (iii) marginal evidence for an anticorrelation of BLR disk thickness with ${\log }_{10}\left(f_{\mathrm{mean},\mathrm{FWHM}}\right)$and ${\log }_{10}\left(f_{\mathrm{rms},\mathrm{FWHM}}\right)$, and (iv) marginal evidence for an anticorrelation of inclination angle with ${\log }_{10}\left(f_{\mathrm{mean},\mathrm{FWHM}}\right)$, ${\log }_{10}\left(f_{\mathrm{rms},\sigma }\right)$, and ${\log }_{10}\left(f_{\mathrm{mean},\sigma }\right)$. Last, we find marginal evidence for a correlation between line-profile shape, when using the root-mean-square spectrum, ${\log }_{10}{\left(\mathrm{FWHM}/\sigma \right)}_{\mathrm{rms}}$, and the virial coefficient, ${\log }_{10}\left(f_{\mathrm{rms},\sigma }\right)$, and investigate how BLR properties might be related to line-profile shape usingcaramelmodels. 

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