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Abstract Self-splicing group I and II introns are selfish genetic elements that are widely yet patchily distributed across the tree of life. Their selfish behavior comes from super-Mendelian inheritance behaviors, collectively called “homing”, which allow them to rapidly spread within populations to the specific genomic sites they home into. Observations of self-splicing intron evolutionary dynamics have led to the formulation of an intron “lifecycle” model where, once fixed in a population, the introns lose selection for homing and undergo an extensive period of degradation until their eventual loss. Here, we find that self-splicing introns are common in the mitochondrial genomes of Epichloë species, endophytic fungi that live in symbioses with grasses. However, these introns show substantial intron presence–absence polymorphism, with our analyses suggesting that these result from a combination of vertical intron inheritance coupled with multiple invasion and loss events over the course of Epichloë evolution. Surprisingly, we find little evidence for the extensive intron degradation expected under the existing intron lifecycle model. Instead, these introns in Epichloë appear to be lost soon after fixation, suggesting that Epichloë self-splicing introns have a different lifecycle. However, rapid intron loss alone cannot explain our results, indicating that additional factors, such as the evolution of homing suppressors, also contribute to Epichloë self-splicing intron dynamics. This work shows that self-splicing introns have more diverse evolutionary dynamics than previously appreciated. 

The tools and techniques such as imaging and machine learning used in the measurement of many material and microstructural properties are rapidly evolving. In metals, the grain size is routinely measured to estimate the yield strength. This paper describes some of the algorithms used in processing the microstructures to conduct quantitative measurements. The image processing methods provide the possibility to go beyond calculating the ASTM grain size number and calculate the actual surface area of each grain, grain boundary length, and the shape of the grains. The image analysis methods can be very helpful in conducting detailed quantitative analysis with greater accuracy than many labour-intensive manual methods currently in use. The work describes the complexities in applying the imaging methods and approaches in the metallurgical and materials fields. Successful application of such methods can reduce the time and effort required to characterise microstructures and can provide more precise information. 

Abstract Among known neutron stars, the Vela pulsar is one of the best targets for gravitational-wave searches. It is also one of the most prolific in terms of glitches, which are sudden frequency changes in a pulsar’s rotation. Such glitches could cause a variety of transient gravitational-wave signals. Here, we search for signals associated with a Vela glitch on 2024 April 29 in data of the two LIGO detectors from the fourth LIGO–Virgo–KAGRA observing run. We search both for seconds-scale burst-like emission, primarily from fundamental (f-)mode oscillations, and for longer quasi-monochromatic transients up to 4 months in duration, primarily from quasi-static quadrupolar deformations. We find no significant detection candidates, but for the first time we set direct observational upper limits on gravitational strain amplitude that are stricter than what can be indirectly inferred from the overall glitch energy scale. We discuss the short- and long-duration observational constraints in the context of specific emission models. These results demonstrate the potential of gravitational-wave probes of glitching pulsars as detector sensitivity continues to improve. 

Abstract Version 4.0 of the Gravitational-Wave Transient Catalog (GWTC-4.0) adds new candidates detected by the LIGO, Virgo, and KAGRA observatories through the first part of the fourth observing run (O4a: 2023 May 24 15:00:00 to 2024 January 16 16:00:00 UTC) and a preceding engineering run. In these new data, we find 128 compact binary coalescence candidates that are identified by at least one of our search algorithms with a probability of astrophysical originp_astro≥ 0.5 and that are not vetoed during event validation. We also provide detailed source property measurements for 86 of these that have a false-alarm rate <1 yr⁻¹. Based on the inferred component masses, these candidates are consistent with signals from binary black holes (BBHs) and neutron star–black hole binaries (GW230518_125908 and GW230529_181500). Median-inferred component masses of BBHs in the catalog now range from 5.79M⊙ (GW230627_015337) to 137 $M_{\odot }$(GW231123_135430), while GW231123_135430 was probably produced by the most massive binary observed in the catalog. For the first time, we have discovered BBH signals with network signal-to-noise ratio exceeding 30, GW230814_230901 and GW231226_101520, enabling high-fidelity studies of the waveforms and astrophysical properties of these systems. Combined with the 90 candidates included in GWTC-3.0, the catalog now contains 218 candidates withp_astro≥ 0.5 and not otherwise vetoed, more than doubling the size of the catalog and further opening our view of the gravitational-wave Universe. 

Abstract LIGO, Virgo, and KAGRA form a network of gravitational-wave observatories. Data and analysis results from this network are made publicly available through the Gravitational Wave Open Science Center. This paper describes open data from this network, including the addition of data from the first part of the fourth observing run and selected periods from the preceding engineering run, collected from 2023 May to 2024 January. The public dataset includes calibrated strain time series for each instrument, data from additional channels used for noise subtraction and detector characterization, and analysis data products from version 4.0 of the Gravitational-Wave Transient Catalog. 

Abstract GW230814, detected by the LIGO Livingston observatory with a signal-to-noise ratio of 42.4, represents the loudest gravitational-wave signal in the GWTC-4.0 catalog. Its source is consistent with a binary black hole coalescence with component masses $m_1=33.7_{-2.2}^{+2.9}{M}_{\odot }$and $m_2=28.2_{-3.1}^{+2.2}{M}_{\odot }$and a small effective inspiral spin ${\chi }_{\mathrm{eff}}=-0.01_{-0.07}^{+0.06}$. The high signal-to-noise ratio enabled us to detect anℓ = ∣m∣ = 4 mode in the inspiral–merger–ringdown signal for the first time (with Bayes factor ≈10), as well as enabling a range of tests of consistency between theoretical predictions and the observed waveform. While most of these tests show agreement with theoretical predictions, there are suggestions of minor deviations in the ringdown phase. Simulations that incorporate general relativity and realistic detector noise reproduce similar deviations, suggesting that they do not constitute evidence for a breakdown of general relativity. The observation of GW230814 demonstrates that the unprecedented sensitivity of the detectors enables highly significant detections with a single observatory. However, without corroborating data from a multidetector network, the ability to draw rigorous conclusions about fundamental physics remains severely limited. 

Abstract The Gravitational-Wave Transient Catalog (GWTC) is a collection of candidate gravitational-wave transient signals identified and characterized by the LIGO–Virgo–KAGRA Collaboration. Producing the contents of the GWTC from detector data requires complex analysis methods. These comprise techniques to model the signal; identify the transients in the data; evaluate the quality of the data and mitigate possible instrumental issues; infer the parameters of each transient; compare the data with the waveform models for compact binary coalescences; and handle the large amount of results associated with all of these different analyses. In this paper, we describe the methods employed to produce the catalog’s fourth release, GWTC-4.0, focusing on the analysis of the first part of the fourth observing run of Advanced LIGO, Advanced Virgo and KAGRA. 

Abstract We present results from directed searches for continuous gravitational waves from a sample of 15 nearby supernova remnants, likely hosting young neutron star candidates, using data from the first eight months of the fourth observing run (O4) of the LIGO–Virgo–KAGRA Collaboration. The analysis employs five pipelines: four semicoherent methods—the Band-Sampled-Data directed pipeline, Weave, and two Viterbi pipelines (single- and dual-harmonic)—and PyStoch, a cross-correlation-based pipeline. These searches cover wide frequency bands and do not assume prior knowledge of the targets’ ephemerides. No evidence of a signal is found from any of the 15 sources. We set 95% confidence-level upper limits on the intrinsic strain amplitude, with the most stringent constraints reaching ∼4 × 10⁻²⁶near 300 Hz for the nearby source G266.2−1.2 (Vela Jr.). We also derive limits on neutron star ellipticity andr-mode amplitudes for the same source, with the best constraints reaching ≲10⁻⁷and ≲10⁻⁵, respectively, at frequencies above 400 Hz. For frequencies above 200 Hz, these results represent the most sensitive wide-band directed searches for continuous gravitational waves from supernova remnants to date.