Fix far by relative fewer

PAPER/paper.md

@@ -37,7 +37,7 @@ This paper presents an overview of GO-RXR, highlighting its functionality, examp
 
 # Statement of Need
 
-RXR offers unique insights into the depth-dependent crystal, electronic, and magnetic structures of quantum materials, enabling the investigation of nanoscale characteristics of new candidate materials with a precision unmatched by any other current experimental technique [@borrero_etal_2017;@fursich_etal_2018; @vanderMinne_etal_2023]. Despite its potential, RXR remains significantly underutilized, with far fewer publications compared to techniques such as x-ray absorption spectroscopy. The main challenge hindering the widespread adoption of RXR lies in the difficulty of data analysis, which requires both large-scale computational quantum mechanics simulations and the fitting of reflectivity models with numerous parameters, such as layer thickness, interfacial roughness, and complex refractive indices, that vary with energy. Each of these parameters must be finely adjusted to match experimental data, making the process intricate and labor-intensive. For example, the oscillations in the Kiessig fringes during theta/two-theta reflectivity scans are directly related to the thickness of the film, while the decay of these fringes indicates the roughness of various interfaces within the material. This makes the analysis process for each sample highly demanding and time-consuming. Consequently, experimental advancements have far outpaced the progress in analytical methods, leaving a substantial amount of collected data unexplored.
+RXR offers unique insights into the depth-dependent crystal, electronic, and magnetic structures of quantum materials, enabling the investigation of nanoscale characteristics of new candidate materials with a precision unmatched by any other current experimental technique [@borrero_etal_2017;@fursich_etal_2018; @vanderMinne_etal_2023]. Despite its potential, RXR remains significantly underutilized, with relatively fewer publications compared to techniques such as x-ray absorption spectroscopy. The main challenge hindering the widespread adoption of RXR lies in the difficulty of data analysis, which requires both large-scale computational quantum mechanics simulations and the fitting of reflectivity models with numerous parameters, such as layer thickness, interfacial roughness, and complex refractive indices, that vary with energy. Each of these parameters must be finely adjusted to match experimental data, making the process intricate and labor-intensive. For example, the oscillations in the Kiessig fringes during theta/two-theta reflectivity scans are directly related to the thickness of the film, while the decay of these fringes indicates the roughness of various interfaces within the material. This makes the analysis process for each sample highly demanding and time-consuming. Consequently, experimental advancements have far outpaced the progress in analytical methods, leaving a substantial amount of collected data unexplored.
 
 The complexity of RXR analysis extends beyond computational demands, requiring a deep understanding of material properties, such as ferromagnetic order or electronic reconstruction in polar-mismatched heterostructures, and the physics of light-matter interactions, such as energy-dependent absorption edge shifts and dichroism effects. This expertise is pivotal because it provides intuition about parameter adjustments and guides the direction of data analysis to achieve meaningful outcomes. In addressing this challenge, GO-RXR integrates global optimization algorithms, thereby lowering the expertise threshold necessary for effective data analysis. Through extensive development, diverse global optimization algorithms and novel objective functions were thoroughly explored. The software's capability to extract features from experimental data such as chemical composition and oxidation states, magnetic properties, and depth-dependent structural changes, without exhaustive parameter adjustments, significantly reduces the specialized expertise required. It effectively models the oscillations in Kiessig fringes, which are directly linked to film thickness, while preserving the overall shape of the theta/two-theta reflectivity scans using a total variation penalty term. This ensures that both fine details and broader trends in the data are accurately represented. Additionally, GO-RXR offers enhanced flexibility in modeling strain at interfaces by allowing different form factors to be applied to distinct layers within the same element, enabling a more precise depiction of complex interfacial phenomena. GO-RXR serves as a valuable scientific tool for material scientists, offering advanced capabilities to streamline data analysis, reduce the expertise barrier, and ultimately facilitate breakthrough discoveries in the field of materials science.