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Subleading power resummation of rapidity logarithms: the energy-energy correlator in $ \mathcal{N} $ = 4 SYM
We derive and solve renormalization group equations that allow for the resummation of subleading power rapidity logarithms. Our equations involve operator mixing into a new class of operators, which we term the “rapidity identity operators”, that will generically appear at subleading power in problems involving both rapidity and virtuality scales. To illustrate our formalism, we analytically solve these equations to resum the power suppressed logarithms appearing in the back-to-back (double light cone) limit of the Energy-Energy Correlator (EEC) in \( \mathcal{N} \) = 4 super-Yang-Mills. These logarithms can also be extracted to \( \mathcal{O}\left({\alpha}_s^3\right) \) from a recent perturbative calculation, and we find perfect agreement to this order. Instead of the standard Sudakov exponential, our resummed result for the subleading power logarithms is expressed in terms of Dawson’s integral, with an argument related to the cusp anomalous dimension. We call this functional form “Dawson’s Sudakov”. Our formalism is widely applicable for the resummation of subleading power rapidity logarithms in other more phenomenologically relevant observables, such as the EEC in QCD, the p T spectrum for color singlet boson production at hadron colliders, and the resummation of power suppressed logarithms in the Regge limit.
The Energy-Energy Correlation in the back-to-back limit at N 3 LO and N 3 LL'
We present the analytic formula for the Energy-Energy Correlation (EEC) in electron-positron annihilation computed in perturbative QCD to next-to-next-to-next-to-leading order (N 3 LO) in the back-to-back limit. In particular, we consider the EEC arising from the annihilation of an electron-positron pair into a virtual photon as well as a Higgs boson and their subsequent inclusive decay into hadrons. Our computation is based on a factorization theorem of the EEC formulated within Soft-Collinear Effective Theory (SCET) for the back-to-back limit. We obtain the last missing ingredient for our computation — the jet function — from a recent calculation of the transverse-momentum dependent fragmentation function (TMDFF) at N 3 LO. We combine the newly obtained N 3 LO jet function with the well known hard and soft function to predict the EEC in the back-to-back limit. The leading transcendental contribution of our analytic formula agrees with previously obtained results in N = 4 supersymmetric Yang-Mills theory. We obtain the N = 2 Mellin moment of the bulk region of the EEC using momentum sum rules. Finally, we obtain the first resummation of the EEC in the back-to-back limit at N 3 LL' accuracy, resulting in a factor of ~ 4 reduction of uncertainties in the peak region compared to N 3 LL predictions.
The transverse energy-energy correlator at next-to-next-to-next-to-leading logarithm
Abstract We present an operator based factorization formula for the transverse energy-energy correlator in the back-to-back (dijet) region, and uncover its remarkable perturbative simplicity and relation to transverse momentum dynamics. This simplicity enables us to achieve next-to-next-to-next-to leading logarithmic (N 3 LL) accuracy for a hadron collider dijet event shape for the first time. Our factorization formula applies toW/Z/γ+ jet, and dijet production, providing a natural generalization of transverse momentum observables to one- and two-jet final states. This provides a laboratory for precision studies of QCD and transverse momentum dynamics at hadron colliders, as well as an opportunity for understanding factorization and its violation in a perturbatively well controlled setting.
Using pile-up collisions as an abundant source of low-energy hadronic physics processes in ATLAS and an extraction of the jet energy resolution
During the 2015–2018 data-taking period, the Large Hadron Collider delivered proton-proton bunch crossings at a centre-of-mass energy of 13 TeV to the ATLAS experiment at a rate of roughly 30 MHz, where each bunch crossing contained an average of 34 independent inelastic proton-proton collisions. The ATLAS trigger system selected roughly 1 kHz of these bunch crossings to be recorded to disk. Offline algorithms then identify one of the recorded collisions as the collision of interest for subsequent data analysis, and the remaining collisions are referred to as pile-up. Pile-up collisions represent a trigger-unbiased dataset, which is evaluated to have an integrated luminosity of 1.33 pb -1 in 2015–2018. This is small compared with the normal trigger-based ATLAS dataset, but when combined with vertex-by-vertex jet reconstruction it provides up to 50 times more dijet events than the conventional single-jet-trigger-based approach, and does so without adding any additional cost or requirements on the trigger system, readout, or storage. The pile-up dataset is validated through comparisons with a special trigger-unbiased dataset recorded by ATLAS, and its utility is demonstrated by means of a measurement of the jet energy resolution in dijet events, where the statistical uncertainty is significantly reduced for jet transverse momenta below 65 GeV.
Bridging nuclear physics across energy scales: from neutrinoless double-beta decay to high-energy heavy-ion collisions
This paper exemplifies how connecting methods at disparate energy scales can illuminate fundamental questions. By demonstrating that nuclear wave function properties governing rare decay processes also influence collective behavior at extreme temperatures and densities, the authors have opened a new pathway for constraining physics beyond the Standard Model. With multiple ton-scale 0νββ experiments under construction, any method reducing NME uncertainties will directly impact our ability to interpret discoveries or constrain neutrino properties. The general principle—that collective phenomena in high-energy collisions can illuminate subtle features of many-body correlations in the colliding nuclei—may find applications across nuclear and particle physics. Furthermore, this intersection of nuclear structure theory, heavy-ion physics, and fundamental symmetry tests represents fertile ground for future discoveries in modern physics.
A conservative level set method for N-phase flows with a free-energy-based surface tension model
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Sensitivity and low-energy response of the Small Anode Germanium well detector with ceramic insert
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The Leading Modes of Asian Summer Monsoon Variability as Pulses of Atmospheric Energy Flow
Monsoon rainfall anomalies are often organized into dynamical modes that are more predictable than rainfall at specific locations. Here a linear response function (LRF) is constructed to extract the neutral modes (NM) of the dynamical atmospheric system and their optimal forcing that govern precipitation variability, providing a dynamical underpinning for the empirically derived, recurrent rainfall patterns. The leading mode (NM1) features a shift in the Atlantic ITCZ, a drying over the Indian Ocean and Indian subcontinent, and a “South-Flood-North Drought (SFND)” pattern over eastern China, a pattern inferable from changes in the atmospheric energy flow forced by the corresponding optimal forcing and further enhanced by the water vapor and cloud feedbacks. Moreover, the slow evolving component of the NM1 in response to CO2 forcing can be skillfully predicted by running the ocean heat uptake through the LRF, implicating a deterministic ocean dynamic source for the NM1-like trend under climate warming.
Flow and extraction of energy and charge carriers in hybrid plasmonic nanostructures
Strong interactions of electromagnetic fields with plasmonic nanomaterials have been exploited in various applications. These applications have centred on plasmon-enhanced scattering rates in nearby molecules or plasmon-induced heating. A question that has emerged recently is whether it is possible to use plasmonic nanostructures in a range of hot electron (hole) applications, including photocatalysis, photovoltaics and photodetection. These applications require coupling of a plasmonic component, which amplifies the interaction of light with the material, to an attached non-plasmonic component that extracts this energy in the form of electronic excitations to perform a function. Here, in this Perspective, we discuss recent work in the emerging field of hybrid plasmonics. We focus on fundamental questions related to the nanoscopic flow of energy and excited charge carriers in these multicomponent materials. We also address critical misconceptions, challenges and opportunities that require more attention.
An energy-optimization method to study gel-swelling in confinement
Using our open-source programming environment Morpho, an energy-optimization method is developed to compute equilibrium shapes of an arbitrarily confined swollen hydrogel. The internal strains and contact forces are measured, probing its mechanics.
XFEL imaging techniques for high energy density and inertial fusion energy research at HED-HiBEF
The imaging platform developed at the High Energy Density-Helmholtz International Beamline for Extreme Fields (HED-HiBEF) instrument at the European X-ray Free Electron Laser (XFEL) and its applications to HED and fusion related research are presented. The platform combines the XFEL beam with the high-intensity short-pulse laser ReLaX and the high-energy nanosecond-pulse laser DiPOLE-100X. The spatial resolution is better than 500 nm and the temporal resolution of the order of 50 fs. The influence of the XFEL source in the x-ray imaging method is discussed. Free-propagation x-ray phase contrast imaging and Talbot-Lau imaging setups are shown. We show examples of blast waves and converging cylindrical shocks in aluminum, resonant absorption measurements of specific charged states in copper with ReLaX and planar shocks in polystyrene material generated by DiPOLE-100X. For the first time, we show the application of Talbot-Lau interferometry to convergent cylindrical shocks as well as resonant absorption processes. We also discuss the possibilities introduced by combining this imaging platform with a kJ-class laser.
Energy resolution of the LZ detector for high-energy electronic recoils
The LUX-ZEPLIN (LZ) detector is a dual-phase liquid xenon time projection chamber (TPC) installed at the Sanford Underground Research Facility (Lead, South Dakota) at a depth of 1478 meters. Although the main objective of LZ is the direct detection of dark matter, its low background environment allows for the search of other rare processes, such as the neutrinoless double beta decay of xenon isotopes 134 Xe and 136 Xe with the respective Q-values of 826 keV and 2458 keV. The sensitivity of the detector to these decays is directly determined by the energy resolution, which, in turn, is degraded by non-uniformities in detector response. In this work, we present a novel method to correct, in the data, the non-uniformity of the light collected by an array of photosensors in a scintillation detector. This method is based on the knowledge of the light response functions of individual photosensors. With these techniques, we report, at a very early phase of the detector operations, a state-of-the-art energy resolution (σ/μ) of (0.67 ± 0.01)% at 2614 keV for the fiducial volume of 5.6 tonnes of liquid xenon.
Short-term impacts of 2017 western North American wildfires on meteorology, the atmosphere’s energy budget, and premature mortality
Abstract Western North American fires have been increasing in magnitude and severity over the last few decades. The complex coupling of fires with the atmospheric energy budget and meteorology creates short-term feedbacks on regional weather altering the amount of pollution to which Americans are exposed. Using a combination of model simulations and observations, this study shows that the severe fires in the summer of 2017 increased atmospheric aerosol concentrations leading to a cooling of the air at the surface, reductions in sensible heat fluxes, and a lowering of the planetary boundary layer height over land. This combination of lower-boundary layer height and increased aerosol pollution from the fires reduces air quality. We estimate that from start of August to end of October 2017, ∼400 premature deaths occurred within the western US as a result of short-term exposure to elevated PM 2.5 from fire smoke. As North America confronts a warming climate with more fires the short-term climate and pollution impacts of increased fire activity should be assessed within policy aimed to minimize impacts of climate change on society.
Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging
There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or an investigation for maximizing their effectiveness. We carry out a comparison between several common machine learning methods for galaxy classification [Convolutional Neural Network (CNN), K-nearest neighbour, logistic regression, Support Vector Machine, Random Forest, and Neural Networks] by using Dark Energy Survey (DES) data combined with visual classifications from the Galaxy Zoo 1 project (GZ1). Our goal is to determine the optimal machine learning methods when using imaging data for galaxy classification. We show that CNN is the most successful method of these ten methods in our study. Using a sample of ~2800 galaxies with visual classification from GZ1, we reach an accuracy of ~0.99 for the morphological classification of ellipticals and spirals. The further investigation of the galaxies that have a different ML and visual classification but with high predicted probabilities in our CNN usually reveals the incorrect classification provided by GZ1. We further find the galaxies having a low probability of being either spirals or ellipticals are visually lenticulars (S0), demonstrating that supervised learning is able to rediscover that this class of galaxy is distinct from both ellipticals and spirals. We confirm that ~2.5 percent galaxies are misclassified by GZ1 in our study. After correcting these galaxies’ labels, we improve our CNN performance to an average accuracy of over 0.99 (accuracy of 0.994 is our best result).
Combination of cluster number counts and two-point correlations: validation on mock Dark Energy Survey
ABSTRACT We present a method of combining cluster abundances and large-scale two-point correlations, namely galaxy clustering, galaxy–cluster cross-correlations, cluster autocorrelations, and cluster lensing. This data vector yields comparable cosmological constraints to traditional analyses that rely on small-scale cluster lensing for mass calibration. We use cosmological survey simulations designed to resemble the Dark Energy Survey Year 1 (DES-Y1) data to validate the analytical covariance matrix and the parameter inferences. The posterior distribution from the analysis of simulations is statistically consistent with the absence of systematic biases detectable at the precision of the DES-Y1 experiment. We compare the χ2 values in simulations to their expectation and find no significant difference. The robustness of our results against a variety of systematic effects is verified using a simulated likelihood analysis of DES-Y1-like data vectors. This work presents the first-ever end-to-end validation of a cluster abundance cosmological analysis on galaxy catalogue level simulations.
Dark Energy Survey Year 3 results: calibration of lens sample redshift distributions using clustering redshifts with BOSS/eBOSS
ABSTRACT We present clustering redshift measurements for Dark Energy Survey (DES) lens sample galaxies used in weak gravitational lensing and galaxy clustering studies. To perform these measurements, we cross-correlate with spectroscopic galaxies from the Baryon Acoustic Oscillation Survey (BOSS) and its extension, eBOSS. We validate our methodology in simulations, including a new technique to calibrate systematic errors that result from the galaxy clustering bias, and we find that our method is generally unbiased in calibrating the mean redshift. We apply our method to the data, and estimate the redshift distribution for 11 different photometrically selected bins. We find general agreement between clustering redshift and photometric redshift estimates, with differences on the inferred mean redshift found to be below |Δz| = 0.01 in most of the bins. We also test a method to calibrate a width parameter for redshift distributions, which we found necessary to use for some of our samples. Our typical uncertainties on the mean redshift ranged from 0.003 to 0.008, while our uncertainties on the width ranged from 4 to 9 per cent. We discuss how these results calibrate the photometric redshift distributions used in companion papers for DES Year 3 results.