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Abstract By measuring a linear response function directly, such as the dynamic susceptibility, one can understand fundamental material properties. However, a fresh perspective can be offered by studying fluctuations. This can be related back to the dynamic susceptibility through the fluctuation–dissipation theorem, which relates the fluctuations in a system to its response, an alternate route to access the physics of a material. Here, we describe a new X-ray tool for material characterization that will offer an opportunity to uncover new physics in quantum materials using this theorem. We provide details of the method and discuss the requisite analysis techniques in order to capitalize on the potential to explore an uncharted region of phase space. This is followed by recent results on a topological chiral magnet, together with a discussion of current work in progress. We provide a perspective on future measurements planned for work in unconventional superconductivity. Graphical abstract We describe a new X-ray tool for material characterization that will offer an opportunity to uncover new physics in quantum materials using coherent, short-pulsed X-rays. We provide details of the method and discuss the requisite analysis techniques in order to capitalize on the potential to explore an uncharted region of phase space. This is followed by recent results on a topological chiral magnet, together with a discussion of current work in progress. We provide a perspective on future measurements planned for work in unconventional superconductivity. 

Abstract Observing exoplanets through transmission spectroscopy supplies detailed information about their atmospheric composition, physics and chemistry. Before the James Webb Space Telescope (JWST), these observations were limited to a narrow wavelength range across the near-ultraviolet to near-infrared, alongside broadband photometry at longer wavelengths. To understand more complex properties of exoplanet atmospheres, improved wavelength coverage and resolution are necessary to robustly quantify the influence of a broader range of absorbing molecular species. Here we present a combined analysis of JWST transmission spectroscopy across four different instrumental modes spanning 0.5–5.2 μm using Early Release Science observations of the Saturn-mass exoplanet WASP-39 b. Our uniform analysis constrains the orbital and stellar parameters within subpercentage precision, including matching the precision obtained by the most precise asteroseismology measurements of stellar density to date, and it further confirms the presence of Na, K, H₂O, CO, CO₂and SO₂as atmospheric absorbers. Through this process, we have improved the agreement between the transmission spectra of all modes, except for the NIRSpec PRISM, which is affected by partial saturation of the detector. This work provides strong evidence that uniform light curve analysis is an important aspect to ensuring reliability when comparing the high-precision transmission spectra provided by JWST. 

How social development in early-life affects fitness remains poorly understood. 2. Though there is growing evidence that early-life relationships can affect fitness, little research has investigated how social positions develop or whether there are particularly important periods for social position development in an animal's life history. In long-lived species in particular, understanding the lasting consequences of early-life social environments requires detailed, long-term datasets. 3. Here we used a 25-year dataset to test whether social positions held during early development predicted adult fitness. Specifically, we quantified social position using three social network metrics: degree, strength and betweenness. We determined the social position of each individual in three types of networks during each of three stages of ontogeny to test whether they predict annual reproductive success (ARS) or longevity among adult female spotted hyenas Crocuta crocuta. 4. The social positions occupied by juvenile hyenas did predict their fitness, but the effects of social position on fitness measures differed between stages of early development. Network metrics when individuals were young adults better predicted ARS, but network metrics for younger animals, particularly when youngsters were confined to the communal den, better predicted longevity than did metrics assessed during other stages of development. 5. Our study shows how multiple types of social bonds formed during multiple stages of social development predict lifetime fitness outcomes. We suggest that social bonds formed during specific phases of development may be more important than others when considering fitness outcomes. 

The development of new modes at x-ray free electron lasers has inspired novel methods for studying fluctuations at different energies and timescales. For closely spaced x-ray pulses that can be varied on ultrafast time scales, we have constructed a pair of advanced instruments to conduct studies targeting quantum materials. We first describe a prototype instrument built to test the proof-of-principle of resonant magnetic scattering using ultrafast pulse pairs. This is followed by a description of a new endstation, the so-called fluctuation–dissipation measurement instrument, which was used to carry out studies with a fast area detector. In addition, we describe various types of diagnostics for single-shot contrast measurements, which can be used to normalize data on a pulse-by-pulse basis and calibrate pulse amplitude ratios, both of which are important for the study of fluctuations in materials. Furthermore, we present some new results using the instrument that demonstrates access to higher momentum resolution. 

Abstract While a stimulating effect of plant primary productivity on soil carbon dioxide (CO₂) emissions has been well documented, links between gross primary productivity (GPP) and wetland methane (CH₄) emissions are less well investigated. Determination of the influence of primary productivity on wetland CH₄emissions (FCH₄) is complicated by confounding influences of water table level and temperature on CH₄production, which also vary seasonally. Here, we evaluate the link between preceding GPP and subsequent FCH₄at two fens in Wisconsin using eddy covariance flux towers, Lost Creek (US‐Los) and Allequash Creek (US‐ALQ). Both wetlands are mosaics of forested and shrub wetlands, with US‐Los being larger in scale and having a more open canopy. Co‐located sites with multi‐year observations of flux, hydrology, and meteorology provide an opportunity to measure and compare lag effects on FCH₄without interference due to differing climate. Daily average FCH₄from US‐Los reached a maximum of 47.7 ηmol CH₄m⁻² s⁻¹during the study period, while US‐ALQ was more than double at 117.9 ηmol CH₄ m⁻² s⁻¹. The lagged influence of GPP on temperature‐normalized FCH₄(T_air‐FCH₄) was weaker and more delayed in a year with anomalously high precipitation than a following drier year at both sites. FCH₄at US‐ALQ was lower coincident with higher stream discharge in the wet year (2019), potentially due to soil gas flushing during high precipitation events and lower water temperatures. Better understanding of the lagged influence of GPP on FCH₄due to this study has implications for climate modeling and more accurate carbon budgeting. 

Ultrafast resonant soft x-ray scattering is used to monitor the dynamics of the charge density wave order in YBa 2 Cu 3 O 6+x . 

Abstract The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20% increase in the efficiency of sub-1 cm vertex reconstruction across all neutrino flavours.