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The MIGHTEE survey utilizes the South African MeerKAT radio telescope to observe four extragalactic deep fields, with the aim of advancing our understanding of the formation and evolution of galaxies across cosmic time. MIGHTEE’s frequency coverage encompasses the $\textrm {H}\scriptstyle \mathrm{I}$ line to a redshift of z $\simeq$ 0.58, and OH megamasers to z $\simeq$ 0.9. We present the MIGHTEE-$\textrm {H}\scriptstyle \mathrm{I}$ imaging products for the COSMOS field, using a total of 94.2 h on-target and a close-packed mosaic of 15 individual pointings. The spectral imaging covers two broad, relatively interference-free regions (960–1150 and 1290–1520 MHz) within MeerKAT’s L-band, with up to 26 kHz spectral resolution (5.5 km s$^{-1}$ at z = 0). The median noise in the highest spectral resolution data is 74 μJy beam$^{-1}$, corresponding to a 5$\sigma$$\textrm {H}\scriptstyle \mathrm{I}$ mass limit of 10$^{8.5}$ M$_{\odot }$ for a 300 km s$^{-1}$ line at z = 0.07. The mosaics cover $\gt $4 deg$^{2}$, provided at multiple angular resolution / sensitivity pairings, with an angular resolution for $\textrm {H}\scriptstyle \mathrm{I}$ at z = 0 of 12 arcsec. We describe the spectral line processing workflow that will be the basis for future MIGHTEE-$\textrm {H}\scriptstyle \mathrm{I}$ products, and validation of, and some early results from, the spectral imaging of the COSMOS field. We find no evidence for line emission at the position of the z = 0.376 $\textrm {H}\scriptstyle \mathrm{I}$ line reported from the CHILES survey at a $\gt $94 per cent confidence level, placing a 3$\sigma$ upper limit of 8.1 $\times$ 10$^{9}$ M$_{\odot }$ on $M_{\mathrm{HI}}$ for this galaxy. A public data release accompanies this article.

Identification and follow-up observations of the host galaxies of fast radio bursts (FRBs) not only help us understand the environments in which the FRB progenitors reside, but also provide a unique way of probing the cosmological parameters using the dispersion measures (DMs) of FRBs and distances to their origin. A fundamental requirement is an accurate distance measurement to the FRB host galaxy, but for some sources viewed through the Galactic plane, optical/near-infrared spectroscopic redshifts are extremely difficult to obtain due to dust extinction. Here we report the first radio-based spectroscopic redshift measurement for an FRB host galaxy, through detection of its neutral hydrogen (Hi) 21 cm emission using MeerKAT observations. We obtain an Hi–based redshift ofz= 0.0357 ± 0.0001 for the host galaxy of FRB 20230718A, an apparently nonrepeating FRB detected in the Commensal Real-time ASKAP Fast Transients survey and localized at a Galactic latitude of –0.°367. Our observations also reveal that the FRB host galaxy is interacting with a nearby companion, which is evident from the detection of an Hibridge connecting the two galaxies. A subsequent optical spectroscopic observation confirmed an FRB host galaxy redshift of 0.0359 ± 0.0004. This result demonstrates the value of Hito obtain redshifts of FRBs at low Galactic latitudes and redshifts. Such nearby FRBs whose DMs are dominated by the Milky Way can be used to characterize these components and thus better calibrate the remaining cosmological contribution to dispersion for more distant FRBs that provide a strong lever arm to examine the Macquart relation between cosmological DM and redshift.

Fast radio bursts (FRBs) are transient radio signals of extragalactic origins that are subjected to propagation effects such as dispersion and scattering. It follows then that these signals hold information regarding the medium they have traversed and are hence useful as cosmological probes of the Universe. Recently, FRBs were used to make an independent measure of the Hubble constant H0, promising to resolve the Hubble tension given a sufficient number of detected FRBs. Such cosmological studies are dependent on FRB population statistics, cosmological parameters, and detection biases, and thus it is important to accurately characterize each of these. In this work, we empirically characterize the sensitivity of the Fast Real-time Engine for Dedispersing Amplitudes (FREDDA) which is the current detection system for the Australian Square Kilometre Array Pathfinder (ASKAP). We coherently redisperse high-time resolution data of 13 ASKAP-detected FRBs and inject them into FREDDA to determine the recovered signal-to-noise ratios as a function of dispersion measure. We find that for 11 of the 13 FRBs, these results are consistent with injecting idealized pulses. Approximating this sensitivity function with theoretical predictions results in a systematic error of 0.3 km s−1 Mpc−1 on H0 when it is the only free parameter. Allowing additional parameters to vary could increase this systematic by up to $\sim 1\,$ km s−1 Mpc−1. We estimate that this systematic will not be relevant until ∼400 localized FRBs have been detected, but will likely be significant in resolving the Hubble tension.

Fast radio bursts (FRBs) are millisecond-duration pulses of radio emission originating from extragalactic distances. Radio dispersion is imparted on each burst by intervening plasma, mostly located in the intergalactic medium. In this work, we observe the burst FRB 20220610A and localize it to a morphologically complex host galaxy system at redshift 1.016 ± 0.002. The burst redshift and dispersion measure are consistent with passage through a substantial column of plasma in the intergalactic medium and extend the relationship between those quantities measured at lower redshift. The burst shows evidence for passage through additional turbulent magnetized plasma, potentially associated with the host galaxy. We use the burst energy of 2 × 10⁴²erg to revise the empirical maximum energy of an FRB.

We report on the commensal ASKAP detection of a fast radio burst (FRB), FRB 20211127I, and the detection of neutral hydrogen (Hi) emission in the FRB host galaxy, WALLABY J131913–185018 (hereafter W13–18). This collaboration between the CRAFT and WALLABY survey teams marks the fifth, and most distant, FRB host galaxy detected in Hi, not including the Milky Way. We find that W13–18 has an Himass ofM_HI= 6.5 × 10⁹M_⊙, an Hi-to-stellar mass ratio of 2.17, and coincides with a continuum radio source of flux density at 1.4 GHz of 1.3 mJy. The Higlobal spectrum of W13–18 appears to be asymmetric, albeit the Hiobservation has a low signal-to-noise ratio (S/N), and the galaxy itself appears modestly undisturbed. These properties are compared to the early literature of Hiemission detected in other FRB hosts to date, where either the Higlobal spectra were strongly asymmetric, or there were clearly disrupted Hiintensity map distributions. W13–18 lacks a sufficient S/N to determine whether it is significantly less asymmetric in its Hidistribution than previous examples of FRB host galaxies. However, there are no strong signs of a major interaction in the optical image of the host galaxy that would stimulate a burst of star formation and hence the production of putative FRB progenitors related to massive stars and their compact remnants.

We constrain the Hubble constant H0 using Fast Radio Burst (FRB) observations from the Australian Square Kilometre Array Pathfinder (ASKAP) and Murriyang (Parkes) radio telescopes. We use the redshift-dispersion measure (‘Macquart’) relationship, accounting for the intrinsic luminosity function, cosmological gas distribution, population evolution, host galaxy contributions to the dispersion measure (DMhost), and observational biases due to burst duration and telescope beamshape. Using an updated sample of 16 ASKAP FRBs detected by the Commensal Real-time ASKAP Fast Transients (CRAFT) Survey and localized to their host galaxies, and 60 unlocalized FRBs from Parkes and ASKAP, our best-fitting value of H0 is calculated to be $73_{-8}^{+12}$ km s−1 Mpc−1. Uncertainties in FRB energetics and DMhost produce larger uncertainties in the inferred value of H0 compared to previous FRB-based estimates. Using a prior on H0 covering the 67–74 km s−1 Mpc−1 range, we estimate a median ${\rm DM}_{\rm host}= 186_{-48}^{+59}\,{\rm pc \, cm^{-3}}$, exceeding previous estimates. We confirm that the FRB population evolves with redshift similarly to the star-formation rate. We use a Schechter luminosity function to constrain the maximum FRB energy to be log10Emax$=41.26_{-0.22}^{+0.27}$ erg assuming a characteristic FRB emission bandwidth of 1 GHz at 1.3 GHz, and the cumulative luminosity index to be $\gamma =-0.95_{-0.15}^{+0.18}$. We demonstrate with a sample of 100 mock FRBs that H0 can be measured with an uncertainty of ±2.5 km s−1 Mpc−1, demonstrating the potential for clarifying the Hubble tension with an upgraded ASKAP FRB search system. Last, we explore a range of sample and selection biases that affect FRB analyses.

Computing demands for large scientific experiments, such as the CMS experiment at the CERN LHC, will increase dramatically in the next decades. To complement the future performance increases of software running on central processing units (CPUs), explorations of coprocessor usage in data processing hold great potential and interest. Coprocessors are a class of computer processors that supplement CPUs, often improving the execution of certain functions due to architectural design choices. We explore the approach of Services for Optimized Network Inference on Coprocessors (SONIC) and study the deployment of this as-a-service approach in large-scale data processing. In the studies, we take a data processing workflow of the CMS experiment and run the main workflow on CPUs, while offloading several machine learning (ML) inference tasks onto either remote or local coprocessors, specifically graphics processing units (GPUs). With experiments performed at Google Cloud, the Purdue Tier-2 computing center, and combinations of the two, we demonstrate the acceleration of these ML algorithms individually on coprocessors and the corresponding throughput improvement for the entire workflow. This approach can be easily generalized to different types of coprocessors and deployed on local CPUs without decreasing the throughput performance. We emphasize that the SONIC approach enables high coprocessor usage and enables the portability to run workflows on different types of coprocessors.

A measurement is performed of Higgs bosons produced with high transverse momentum (p_T) via vector boson or gluon fusion in proton-proton collisions. The result is based on a data set with a center-of-mass energy of 13 TeV collected in 2016–2018 with the CMS detector at the LHC and corresponds to an integrated luminosity of 138 fb⁻¹. The decay of a high-p_THiggs boson to a boosted bottom quark-antiquark pair is selected using large-radius jets and employing jet substructure and heavy-flavor taggers based on machine learning techniques. Independent regions targeting the vector boson and gluon fusion mechanisms are defined based on the topology of two quark-initiated jets with large pseudorapidity separation. The signal strengths for both processes are extracted simultaneously by performing a maximum likelihood fit to data in the large-radius jet mass distribution. The observed signal strengths relative to the standard model expectation are$$ {4.9}_{-1.6}^{+1.9} $$${4.9}_{-1.6}^{+1.9}$and$$ {1.6}_{-1.5}^{+1.7} $$${1.6}_{-1.5}^{+1.7}$for the vector boson and gluon fusion mechanisms, respectively. A differential cross section measurement is also reported in the simplified template cross section framework.

This paper describes theCombinesoftware package used for statistical analyses by the CMS Collaboration. The package, originally designed to perform searches for a Higgs boson and the combined analysis of those searches, has evolved to become the statistical analysis tool presently used in the majority of measurements and searches performed by the CMS Collaboration. It is not specific to the CMS experiment, and this paper is intended to serve as a reference for users outside of the CMS Collaboration, providing an outline of the most salient features and capabilities. Readers are provided with the possibility to runCombineand reproduce examples provided in this paper using a publicly available container image. Since the package is constantly evolving to meet the demands of ever-increasing data sets and analysis sophistication, this paper cannot cover all details ofCombine. However, the online documentation referenced within this paper provides an up-to-date and complete user guide.