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Abstract Infrasound may be used to detect the approach of hazardous volcanic mudflows, known as lahars, tens of minutes before their flow fronts arrive. We have analyzed signals from more than 20 secondary lahars caused by precipitation events at Fuego Volcano during Guatemala’s rainy season in May through October of 2022. We are able to quantify the capabilities of infrasound monitoring through comparison with seismic data, time lapse camera imagery, and high-resolution video of a well-recorded event on August 17. We determine that infrasound sensors, deployed adjacent to the lahar path and in small-aperture (10 s of meters) arrays, are particularly sensitive to remote detection of lahars, including small-sized events, at distances of at least 5 km. At Fuego Volcano these detections could be used to provide timely alerts of up to 30 min before lahars arrive at a downstream monitoring site, such as in the frequently impacted Ceniza drainage. We propose that continuous infrasound monitoring, from locations adjacent to a drainage, may complement seismic monitoring and serve as a valuable tool to help identify approaching hazards. On the other hand, infrasound arrays located a kilometer or more from the lahar path can be effectively used to track a lahar’s progression. 

Using the example of metal clusters, an experimental setup and procedure is presented, which allows for the generation of size and charge-state selected polyanions from monoanions in a molecular beam. As a characteristic feature of this modular setup, the further charging process via sequential electron attachment within a three-state digital trap takes place after mass-selection. In contrast to other approaches, the rf-based concept permits access to heavy particles. The procedure is highly flexible with respect to the preparation process and potentially suitable for a wide variety of anionic species. By adjusting the storage conditions, i.e., the radio frequency, to the change in the mass-to-charge ratio, we succeeded in producing clusters in highly negative charge states, i.e., [Formula: see text]. The capabilities of the setup are demonstrated by experiments extracting electronic and optical properties of polyanionic metal clusters by analyzing the corresponding photoelectron spectra.

 

A description is presented of the algorithms used to reconstruct energy deposited in the CMS hadron calorimeter during Run 2 (2015–2018) of the LHC. During Run 2, the characteristic bunch-crossing spacing for proton-proton collisions was 25 ns, which resulted in overlapping signals from adjacent crossings. The energy corresponding to a particular bunch crossing of interest is estimated using the known pulse shapes of energy depositions in the calorimeter, which are measured as functions of both energy and time. A variety of algorithms were developed to mitigate the effects of adjacent bunch crossings on local energy reconstruction in the hadron calorimeter in Run 2, and their performance is compared.

 

A search for decays to invisible particles of Higgs bosons produced in association with a top-antitop quark pair or a vector boson, which both decay to a fully hadronic final state, has been performed using proton-proton collision data collected at$${\sqrt{s}=13\,\text {Te}\hspace{-.08em}\text {V}}$$$\sqrt{s}=13\text{Te}\text{V}$by the CMS experiment at the LHC, corresponding to an integrated luminosity of 138$$\,\text {fb}^{-1}$$${\text{fb}}^{-1}$. The 95% confidence level upper limit set on the branching fraction of the 125$$\,\text {Ge}\hspace{-.08em}\text {V}$$$\text{Ge}\text{V}$Higgs boson to invisible particles,$${\mathcal {B}({\textrm{H}} \rightarrow \text {inv})}$$$B(\text{H}\to \text{inv})$, is 0.54 (0.39 expected), assuming standard model production cross sections. The results of this analysis are combined with previous$${\mathcal {B}({\textrm{H}} \rightarrow \text {inv})}$$$B(\text{H}\to \text{inv})$searches carried out at$${\sqrt{s}=7}$$$\sqrt{s}=7$, 8, and 13$$\,\text {Te}\hspace{-.08em}\text {V}$$$\text{Te}\text{V}$in complementary production modes. The combined upper limit at 95% confidence level on$${\mathcal {B}({\textrm{H}} \rightarrow \text {inv})}$$$B(\text{H}\to \text{inv})$is 0.15 (0.08 expected).