Search for: All records

Atmospheric rivers (ARs) are filamentary structures within the atmosphere that account for a substantial portion of poleward moisture transport and play an important role in Earth's hydroclimate. However, there is no one quantitative definition for what constitutes an atmospheric river, leading to uncertainty in quantifying how these systems respond to global change. This study seeks to better understand how different AR detection tools (ARDTs) respond to changes in climate states utilizing single‐forcing climate model experiments under the aegis of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP). We compare a simulation with an early Holocene orbital configuration and another with CO₂levels of the Last Glacial Maximum to a preindustrial control simulation to test how the ARDTs respond to changes in seasonality and mean climate state, respectively. We find good agreement among the algorithms in the AR response to the changing orbital configuration, with a poleward shift in AR frequency that tracks seasonal poleward shifts in atmospheric water vapor and zonal winds. In the low CO₂simulation, the algorithms generally agree on the sign of AR changes, but there is substantial spread in their magnitude, indicating that mean‐state changes lead to larger uncertainty. This disagreement likely arises primarily from differences between algorithms in their thresholds for water vapor and its transport used for identifying ARs. These findings warrant caution in ARDT selection for paleoclimate and climate change studies in which there is a change to the mean climate state, as ARDT selection contributes substantial uncertainty in such cases. 

Elevation is a major driver of plant ecology and sediment dynamics in tidal wetlands, so accurate and precise spatial data are essential for assessing wetland vulnerability to sea-level rise and making forecasts. We performed survey-grade elevation and vegetation surveys of the Global Change Research Wetland, a brackish microtidal wetland in the Chesapeake Bay estuary, Maryland (USA), to both intercompare unbiased digital elevation model (DEM) creation techniques and to describe niche partitioning of several common tidal wetland plant species. We identified a tradeoff between scalability and performance in creating unbiased DEMs, with more data-intensive methods such as kriging performing better than 3 more scalable methods involving post-processing of light detection and ranging (LiDAR)-based DEMs. The LiDAR Elevation Correction with Normalized Difference Vegetation Index (LEAN) method provided a compromise between scalability and performance, although it underpredicted variability in elevation. In areas where native plants dominated, the sedge Schoenoplectus americanus occupied more frequently flooded areas (median: 0.22, 95% range: 0.09 to 0.31 m relative to North America Vertical Datum of 1988 [NAVD88]) and the grass Spartina patens , less frequently flooded (0.27, 0.1 to 0.35 m NAVD88). Non-native Phragmites australis dominated at lower elevations more than the native graminoids, but had a wide flooding tolerance, encompassing both their ranges (0.19, -0.05 to 0.36 m NAVD88). The native shrub Iva frutescens also dominated at lower elevations (0.20, 0.04 to 0.30 m NAVD88), despite being previously described as a high marsh species. These analyses not only provide valuable context for the temporally rich but spatially restricted data collected at a single well-studied site, but also provide broad insight into mapping techniques and species zonation. 

A<sc>bstract</sc> A measurement of the angular structure of inclusive jets and those containing a prompt D⁰meson in proton-proton collisions at the LHC at a center-of-mass energy of 5.02 TeV is presented. The data corresponding to an integrated luminosity of 301 pb⁻¹were collected by the CMS experiment in 2017. Two jet grooming algorithms, late-k_Tand soft drop, are used to study the intrajet radiation pattern using iterative Cambridge-Aachen declustering. The splitting-angle distributions of jets with transverse momentum (p_T) of around 100 GeV, obtained with these two algorithms, show that there is a shift of the distribution for jets containing a prompt D⁰meson with respect to inclusive jets. The suppression of emissions at small angles observed in the late-k_Tgrooming approach is consistent with the dead-cone effect, whereas the similar suppression for splittings selected with the soft-drop algorithm appears to be induced by gluon splitting to charm quark-antiquark pairs at large angles. The measured distributions are corrected to the particle level and can be used to constrain model predictions for the substructure of high-p_Tcharm quark jets. 

A hot and dense state of nuclear matter, known as the quark-gluon plasma, is created in collisions of ultrarelativistic heavy nuclei. Highly energetic quarks and gluons, collectively referred to as partons, lose energy as they travel through this matter, leading to suppressed production of particles with large transverse momenta ( $p_{\mathrm{T}}$). Conversely, high- $p_{\mathrm{T}}$particle suppression has not been seen in proton-lead collisions, raising questions regarding the minimum system size required to observe parton energy loss. Oxygen-oxygen (OO) collisions examine a region of effective system size that lies between these two extreme cases. The CMS detector at the CERN LHC has been used to quantify charged-particle production in inclusive OO collisions for the first time via measurements of the nuclear modification factor ( $R_{\mathrm{AA}}$). The $R_{\mathrm{AA}}$is derived by comparing particle production to expectations based on proton-proton ( $pp$) data and has a value of unity in the absence of nuclear effects. The data for OO and $pp$collisions at a nucleon-nucleon center-of-mass energy $\sqrt{s_{\mathrm{NN}}}=5.36\mathrm{TeV}$correspond to integrated luminosities of $6.1{\mathrm{nb}}^{-1}$and $1.02{\mathrm{pb}}^{-1}$, respectively. The $R_{\mathrm{AA}}$is below unity with a minimum of $0.69\pm 0.04$around $p_{\mathrm{T}}=6\mathrm{GeV}$. The data exhibit better agreement with theoretical models incorporating parton energy loss as compared to baseline models without energy loss. 

A<sc>bstract</sc> A measurement of the substructure of bottom quark jets (b jets) in proton-proton (pp) collisions is presented. The measurement uses data collected in pp collisions at$$ \sqrt{s}=5.02 $$ $\sqrt{s}=5.02$TeV, with a low number of simultaneous interactions per bunch crossing, recorded by the CMS experiment in 2017, corresponding to an integrated luminosity of 301 pb⁻¹. An algorithm to identify and cluster the charged decay daughters of b hadrons is developed for this analysis, which facilitates the exposure of the gluon radiation pattern of b jets using iterative Cambridge-Aachen declustering. The soft-drop-groomed jet radius,R_g, and momentum balance,z_g, of b quark jets are presented. These observables can be used to test perturbative quantum chromodynamics predictions that account for mass effects. Because the b hadron is partially reconstructed from its charged decay daughters, only charged particles are used for the jet substructure studies. In addition, a jet fragmentation function,z_b,ch, is measured, which is defined as the distribution of the ratio of the transverse momentum (p_T) of the partially reconstructed b hadron with respect to the charged-particle component of the jetp_T. The substructure variable distributions are unfolded to the charged-particle level. The b jet substructure is compared to the substructure of jets in an inclusive jet sample that is dominated by light-quark and gluon jets in order to assess the role of the b quark mass. A strong suppression of emissions at smallR_gvalues is observed for b jets when compared to inclusive jets, consistent with the dead-cone effect. The measurement is also compared with theoretical predictions from Monte Carlo event generators. This is the first substructure measurement of b jets that clusters together the b hadron decay daughters independent of the b hadron species and decay channel. 

This Letter reports the first measurement of photonuclear $D^0$meson production in ultraperipheral heavy ion collisions. The study is performed using lead-lead collision data, with an integrated luminosity of $1.34{\mathrm{nb}}^{-1}$, collected by the CMS experiment at a nucleon-nucleon center-of-mass energy of 5.36 TeV. Photonuclear events, where one of the colliding nuclei breaks up and the other remains intact, are selected based on breakup neutron emissions and by requiring no particle activity in a large rapidity interval in the direction of the photon-emitting nucleus. The $D^0$mesons are reconstructed via the $D^0\to K^{-}{\pi }^{+}$decay channel, with the cross section measured as a function of $D^0$meson transverse momentum and rapidity. The results are compared with next-to-leading-order perturbative QCD calculations that employ recent parametrizations of the lead nuclear parton distribution functions, as well as with predictions based on the color glass condensate framework. This measurement is the first photonuclear collision study characterizing parton distribution functions of lead nuclei for parton fractional momenta $x$(relative to the nucleon) ranging approximately from a few ${10}^{-4}$to ${10}^{-2}$for different hard energy scale $Q^2$selections. 

The analysis of membranous extracellular vesicles, such as exosomes vesicles (EV) opens a new direction for the rapid disease diagnosis because EVs can carry molecular constituents of their originating cells. Secreted by mammalian cells, the size of most membrane-bound phospholipid EVs ranges from 50 to 150 nm in diameter. Recent studies have demonstrated the potential of using EVs for cancer diagnosis and treatment monitoring. To diagnose infectious diseases using EVs, the ability to discriminate EVs from host cells and parasites is key. Here, we report a rapid EV analysis assay that can discriminate EVs based on a host-specific transmembrane protein (CD63 antigen) using a label-free optical biosensor.