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Abstract We measured the rotationally resolved infrared spectra of helium solvated methyl fluoride at 3 μm and 10 μm, wherein lies C−H and C−F stretching bands, respectively. The linewidths (FWHM) were found to increase with increasing vibrational energy and range from 0.002 cm⁻¹in thev₃band (C−F stretch) at ~1047 cm⁻¹, to 0.65 cm⁻¹in thev₄band (asymmetric C−H stretch) at ~2997 cm⁻¹. In between these two bands we observed the lower and upper components of the Fermi triad bands (ν₁/2ν₂/2ν₅) at ~2859 and ~2961 cm⁻¹. We carried out Stark spectroscopy on the lower band on account of its narrower linewidths (0.04 vs. 0.14 cm⁻¹, respectively). The objective of performing Stark spectroscopy was to see if there is any evidence for a rotational linewidth dependence on the external field strength, due to a reduced difference in between methyl fluorides rotational energy gap and the roton‐gap of superfluid helium. We did not find any evidence for such an effect, which we largely attribute to the rotational energy gap not increasing significantly enough by the external field. We point to another molecule (formaldehyde) whose energy levels are predicted to show a more promising response to application of an external field. 

Abstract The discovery of a new kind of experience can teach an agent what that kind of experience is like. Such a discovery can be epistemically transformative, teaching an agent something they could not have learned without having that kind of experience. However, learning something new does not always require new experience. In some cases, an agent can merely expand their existing knowledge using, e.g., inference or imagination that draws on prior knowledge. We present a computational framework, grounded in the language of partially observable Markov Decision Processes (POMDPs), to formalize this distinction. We propose that epistemically transformative experiences leave a measurable “signature” distinguishing them from experiences that are not epistemically transformative. For epistemically transformative experiences, learning in a new environment may be comparable to “learning from scratch” (since prior knowledge has become obsolete). In contrast, for experiences that are not transformative, learning in a new environment can be facilitated by prior knowledge of that same kind (since new knowledge can be built upon the old). We demonstrate this in a synthetic experiment inspired by Edwin Abbott’s Flatland, where an agent learns to navigate a 2D world and is subsequently transferred either to a 3D world (epistemically transformative change) or to an expanded 2D world (epistemically non-transformative change). Beyond the contribution to understanding epistemic change, our work shows how tools in computational cognitive science can formalize and evaluate philosophical intuitions in new ways. 

Abstract Navigating uncertainty is a critical challenge in all fields of science, especially when translating knowledge into real-world policies or management decisions. However, the wide variance in concepts and definitions of uncertainty across scientific fields hinders effective communication. As a microcosm of diverse fields within Earth Science, NASA’s Carbon Monitoring System (CMS) provides a useful crucible in which to identify cross-cutting concepts of uncertainty. The CMS convened the Uncertainty Working Group (UWG), a group of specialists across disciplines, to evaluate and synthesize efforts to characterize uncertainty in CMS projects. This paper represents efforts by the UWG to build a heuristic framework designed to evaluate data products and communicate uncertainty to both scientific and non-scientific end users. We consider four pillars of uncertainty: origins, severity, stochasticity versus incomplete knowledge, and spatial and temporal autocorrelation. Using a common vocabulary and a generalized workflow, the framework introduces a graphical heuristic accompanied by a narrative, exemplified through contrasting case studies. Envisioned as a versatile tool, this framework provides clarity in reporting uncertainty, guiding users and tempering expectations. Beyond CMS, it stands as a simple yet powerful means to communicate uncertainty across diverse scientific communities. 

The formic acid-ammonia dimer is an important example of a hydrogen-bonded complex in which a double proton transfer can occur. Its microwave spectrum has recently been reported and rotational constants and quadrupole coupling constants were determined. Calculated estimates of the double-well barrier and the internal barriers to rotation were also reported. Here we report a full-dimensional potential energy surface (PES) for this complex, using two closely related Δ-machine learning methods to bring it to the CCSD(T) level of accuracy. The PES dissociates smoothly and accurately. Using a 2d quantum model the ground vibrational-state tunneling splitting is estimted to be less than 10−4 cm−1. The dipole moment along the intrinsic reaction coordinate is calculated along with a Mullikan charge analysis and supports mildly ionic character of the minimum and strongly ionic character at the double-well barrier.