Momentum and Energy Dependent Resolution Function of the ARCS Neutron Chopper Spectrometer at High Momentum Transfer: Comparing Simulation and Experimen

preprint2016arXivOpen access

Abstract

Inelastic neutron scattering at high momentum transfers (i.e. Q≥20Q\ge20 Å) or DINS provides direct observation of the momentum distribution of light atoms, making it a powerful probe for studying single-particle motions in liquids and solids. The quantitative analysis of DINS data requires an accurate knowledge of the instrument resolution function Ri(Q,E)R_{i}({Q},E) at each QQ and energy transfer EE, where the label ii indicates whether the resolution was experimentally observed i=obsi={obs} or simulated i=simi=sim. Here, we describe two independent methods for determining the total resolution function Ri(Q,E)R_{i}({Q},E) of the ARCS neutron instrument at the Spallation Neutron Source, Oak Ridge National Laboratory. The first method uses experimental data from an archetypical system (liquid 4^4He) studied with DINS, which are then numerically deconvoluted using its previously determined intrinsic scattering function to yield Robs(Q,E)R_{obs}({Q},E). The second approach uses accurate Monte Carlo simulations of the ARCS spectrometer, which account for all instrument contributions, coupled to a representative scattering kernel to reproduce the experimentally observed response S(Q,E)S({Q},E). Using a delta function as scattering kernel, the simulation yields a resolution function Rsim(Q,E)R_{sim}({Q},E) with comparable lineshape and features as Robs(Q,E)R_{obs}({Q},E), but somewhat narrower due to the ideal nature of the model. Using each of these two Ri(Q,E)R_{i}({Q},E) separately, we extract characteristic parameters of liquid 4^4He such as the intrinsic linewidth α2α_2 (which sets the atomic kinetic energy ⟨K⟩∼α2\langle K\rangle\simα_2) in the normal liquid and the Bose-Einstein condensate parameter n0n_0 in the superfluid phase. The extracted α2α_2 values agree well with previous measurements, independently of which Ri(Q,y)R_i(Q,y) is used to analyze the data.

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