research papers
Towards an extremely high resolution broadband flatfield spectrometer in the `water window'^{1}
^{a}Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Jiading District, Shanghai 201800, People's Republic of China, ^{b}Shanghai Synchrotron Radiation Facility, Shanghai Advanced Research Institute, Zhangjiang Laboratory, Chinese Academy of Sciences, Pudong District, Shanghai 201204, People's Republic of China, ^{c}School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, People's Republic of China, and ^{d}University of Chinese Academy of Sciences, No. 19(A) Yuquan Road, Shijingshan District, Beijing 100049, People's Republic of China
^{*}Correspondence email: libin1995@sinap.ac.cn
The optical design of a novel spectrometer is presented, combining a cylindrically convex premirror with a cylindrically concave variedlinespacing grating (both in the meridional) to deliver a
of 100000–200000 in the `water window' (2–5 nm). Most remarkably, the extremely high spectral resolution is achieved for an effective meridional source size of 50 µm (r.m.s.); this property could potentially be applied to diagnose SASEFEL and well resolve individual single spikes in its The overall optical aberrations of the system are well analysed and compensated, providing an excellent flatfield at the detector domain throughout the whole spectral range. Also, a machinelearning scheme – SVM – is introduced to explore and reconstruct the optimal system with high efficiency.Keywords: spectrometer; Xray optics; resolution enhancement; geometric optics; ray tracing; diffraction principle; optical aberration analysis and optimization.
1. Introduction
In the past few decades, Xray spectrometers have accomplished rapid development driven by advanced light sources such as synchrotron radiation facilities and freeelectron lasers (FELs), and have been widely used for exploring various intriguing research topics especially in the extreme ultraviolet or soft Xray regimes, e.g. applications of tokamak plasmas and magnetic confinement fusion (Schwob et al., 1987), laserproduced warm dense matter and extreme energy density states (Schwanda et al., 1993), stellar or planetary interior properties (Xiong et al., 2011), instrument development and applications for advanced light sources (Koike et al., 2003). The technique is necessary for providing high spectroscopic resolution in physical, chemical, photonic and biological research. Pursuing better spectral resolution always remains a strong motivation for researchers, helping them to envision subtler details in materials, and explore previously unobserved phenomena. The `water window', spanning the wavelength range 2–5 nm, is able to provide excellent contrast imaging for C or O atoms and related structures; this outstanding property could be utilized to image and analyze biological cells or microstructures in vitro and potentially in vivo. `Water window' spectroscopy is also a novel probe for material properties and electron energy states.
Previously, highresolution spectrometers in this spectral range have included the following designs: grating on Rowland circle structure (Namioka, 1959); singleplane grating grooved in varied line spacing (VLS) (Fan et al., 1992; Xiong et al., 2011); single concave VLS grating (Harada & Kita, 1980; Nakano et al., 1984); concave mirror prefocusing the incident beam upstream of a plane grating, creating a real secondary source (Choi et al., 1997); beam prefocused by a spherical mirror to converge beyond the VLS grating, creating a virtual source, i.e. Hettrick–Underwood design (Hettrick et al., 1988), which exists in different versions: e.g. (i) Hague et al. (2005) employed a Kirkpatrick–Baez (KB) mirror for prefocusing to correct for spectral astigmatism; (ii) Tondello (1979) replaced the KB mirror with a toroidal mirror; (iii) Dvorak et al. (2016) added a deflection mirror downstream of the grating to level the outgoing beam; (iv) the Hettrick–Underwood scheme implements a Woltertype focusing system (Warwick et al., 2014), etc. Beside these, Y. D. Chuang and Y. C. Shao have designed a modular spectrometer whose modules can be conveniently adapted to various research requirements (Chuang et al., 2017).
In the past, convex mirrors were rarely used. Only the Wolter III focusing system consisting of a hyperbolically convex mirror and an elliptically concave mirror has been adopted in Xray imaging and microscopy (Wolter, 1952), where the incoming beam is grazing incident on the convex mirror and the reflected beam is diverging. Its reverse extension lines are converged at one focus of the concave elliptical mirror; the reflected beam from the ellipse is propagating backward and then focused on the other focus. Except for a few reports (Saha, 1985, 1988), the characteristics of the Wolter III mirrors have been rarely studied, resulting in a lack of deep and clear understanding. Inspired by the Wolter configuration and based on this previous work, we formulated a delicate highresolution flatfield spectrometer design for the `water window', combining an upstream predivergent convex mirror and a downstream concave VLS grating, which is demonstrated to enhance the considerably while maintaining a decent flatfield condition throughout the whole spectral range.
Resonant inelastic Xray scattering (RIXS) spectrometers usually have a very high et al., 2016). Its detection arm can scan a wide angular range corresponding to various momentum transfers in between the photon and the sample materials. RIXS can be implemented to investigate the energy, momentum and polarization dependence of photon–matter interactions or scattering processes, and hence to reflect the intrinsic properties of charge, spin, orbital, excitation etc. (Ament et al., 2011). With improvements in for instance, charge transfer and d–d excitations (Kuiper et al., 1998; Harada et al., 2000), spin excitations in cuprates (Braicovich et al., 2010; Guarise et al., 2010) and iron pnictides (Zhou et al., 2013), highenergy phonons (Braicovich et al., 2010) and vibrations in single molecules (Hennies et al., 2010; Pietzsch et al., 2011) could be thoroughly investigated.
benefiting from an excellent upstream monochromator system, via confining or focusing the beam width down to a few micrometres, producing a small secondary light source for the spectrometer downsteam (DvorakOur efforts are completely different, aiming to achieve such a high i.e. inserting a convex mirror upstream of the concave VLS grating. Then, the intrinsic optical nature of the system, and the primary factors influencing the quality and resolution are explicitly analysed to exploit its best performance. This kind of spectrometer can be used to diagnose the radiation properties of FELs, especially for the selfamplied (SASE) mode. In a SASE process, radiation gain and saturation originate from small random phase noise, and mutual interaction in between the electron bunch and the radiation. The resulting radiation is closely correlated to the electron bunch properties, e.g. bunch charge beam longitudinal and transverse profiles, beam emittance, and spread, etc. Typically, a saturated FEL radiation pulse possesses high transverse coherence and partial longitudinal coherence, where the pulse's longitudinal profile in the time domain includes multiple individual coherent spikes which are mutually uncorrelated and incoherent. However, it is extremely difficult to directly measure the temporal profile of a SASE pulse precisely, thus a highresolution spectrometer could alternatively be used to measure the corresponding spectrum of the SASE pulse. For example, for the FEL radiation in the photon energy range of the `water window' (280–600 eV) up to 1 keV the entire SASE bandwidth (ΔE/E) is about 1/1000–1/200, while the bandwidth for a typical coherent spike in a SASE pulse is an order less, spanning only 1/20000–1/5000. Resolving well a single spike can not only provide the detailed structures in the SASE radiation spectral domain but also reflect the minimal SASE pulse length in the time domain simutaneously (since Heisenberg's uncertainty law or transform limit implicates that the SASE pulse length should not be shorter than the reciprocal of the spectral bandwidth of an individual coherence spike in its spectrum) (Engel et al., 2016). So, the current spectrometer design, providing a very high spectral of 100000–200000, could determine critical parameters of FEL radiation. In particular, we achieve an extreme spectral resolution for a relatively large source size (50 µm r.m.s.); this exceptional property could enhance the spectral intensity and substantially, which exhibits a promising photon diagnostic scheme for a FEL light source.
by utilizing a scheme similar to Wolter configurations,The manuscript is organized as follows:
(a) The second section presents a numerical simulation and algorithm to prove that the convex premirror is a good choice for enhancing the of a spectrometer. Besides the resolution enhancement, a decent flatfield could be achieved at the detector, since the optical aberrations of the convex mirror propagate downstream to compensate those of the concave grating, thus optimizing the primary aberrations of the overall system.
(b) The third section explicitly discusses the optimization algorithm, where the machinelearning tool Support Vector Machine (SVM) is introduced and implemented to achieve a set of optimal parameters in the spectrometer design, while the quality evaluation parameter for the spectral imaging is well defined and discussed.
(c) The fourth section mainly discusses the key parameters of the system (e.g. source size, optical aberrations, fabrication errors, etc.) determining the ultimate spectral resolution, which is verified by ray tracing. In particular, critical requirements for the slope errors of the optical elements in the highresolution spectrometer are analysed.
(d) Finally, we make a more general and summarizing remark regarding our design, and discuss potential future research and development.
2. Numerical simulation
First, we list a set of parameters fixed in the simulation and discussion throughout this article: (i) light source intensity distribution: Gaussian profile; (ii) size of light source: σ_{s} = 50 µm (r.m.s); (iii) divergence angle of light source: 20 µrad (r.m.s); (iv) grooved density of VLS grating at the centre: D_{0} = 24000 lines cm^{−1}; (v) grating diffraction order: m = 1; (vi) wavelength range: 2–5 nm (water window); (vii) distance from original light source to grating: L = 30 m. Here, we are mainly concerned with the beam properties in its meridional coordinate, thus cylindrical substrates (tangentially convex or concave profiles) are adopted for all the optical elements in the system. This is sensible, since the beam divergence of synchrotron radiation or freeelectron lasers is quite small; a freely propagating beam in sagittal coordinates would not lead to a large footprint in that direction.
2.1. Four types of spectrometer
The single concave VLS grating spectrometer is shown in Fig. 1(a), and its ideal is given by (Li & Li, 2018)
where λ is the wavelength, r is the object distance of the grating, D_{0} (grating groove density) has been defined previously, is the original source size in FWHM ( ≃ ), and α is the incident angle of the grating. Since L is the distance from the original light source to the grating, then r = L for this case (denoted by the dotted line arrow). According to equation (1), the is proportional to the wavelength, the groove density of the grating, the grating object distance r (or L), inversely proportional to the light source size, and prefers a larger incident angle (or a smaller grazing incidence angle).
As shown in Fig. 1(b), the concave VLS grating is combined with a prefocusing concave mirror, forming a real secondary source for the grating, i.e. the meridional beam focuses upstream of the grating and illuminates it. So, the is calculated by
where r_{c} and are the object and image distances of the prefocusing mirror, whose magnification is denoted by M_{c} = (since > 0 for this case), and d is the separation in between the concave mirror and the grating. So, the object distance of the concave mirror is r_{c} = L − d, the grating object distance can be expressed as > 0, and the effective source size of the grating is .
Fig. 1(c) presents a similar configuration to Fig. 1(b), while the preconcave mirror forms a virtual source for the grating, i.e. the meridional beam focuses behind the grating. This recalls the typical Hettrick–Underwood scheme, associated with a of
where M_{c} > 0 (since > d > 0), the (−1) term in the numerator indicates the virtual source for the grating, and its object distance is r = < 0 (virtual source). The rest of the variables in equation (3) are defined in a similar way as for equation (2).
Finally, in Fig. 1(d) the VLS grating is combined with a preconvex mirror. The incident beam is diverged meridionally by the cylindrical convex mirror, and the virtual image of the convex mirror represents the real source of the grating effectively. The of the system is
where M_{c} < 0 since the preconvex mirror generates a virtual image (the image distance < 0) and the object distance of the grating r = > d > 0. Similarly, the (−1) term in the denominator of equation (4) is due to the virtual image of the convex mirror.
In order to evaluate the performance of these four systems, their resolving powers [refer to equations (1)–(4)] are plotted against M_{c} in Fig. 2, for a set of three different optical element spacings, d = 6, 10 and 14 m. Again, the preset parameters at the beginning of Section 2 are used for the calculation, e.g. L = 30 m, σ_{s} = 50 µm (r.m.s), α = 89°, D_{0} = 24000 lines cm^{−1}. Since the is wavelength dependent, here only the results for λ = 5 nm are presented.
In Fig. 2, A_{1} (blue) is the baseline case and has a constant resolution of ∼170000, where d or M_{c} are not applicable since only a single concave grating is used in the system. For the other three configurations, only if the values of A_{2}–A_{4} are greater than A_{1} is the `enhanced'. For A_{2} (three green curves crossing the centre of the graph vertically, with only minor differences in colour), the resolving powers monotonously decrease with M_{c} for each d, only if M_{c} is less than 0.304 (for d = 14 m), the resolution would be greater than A_{1} (while for d = 10 m, M_{c} < 0.200; for d = 6 m, M_{c} < 0.111). On the other hand, with M_{c} increasing, the focus of the prefocusing mirror will gradually move to the surface of the grating; in that circumstance the declines down to zero. Further increasing M_{c}, the system will transit to A_{3}, i.e. the Hettrick–Underwood design (yellow curves in bottomright corner), where the focal spot behind the grating corresponds to a virtual source of the grating. A_{3} monotonously increases with M_{c} for all d values, and an apparently smaller d is associated with a relatively higher However, since A_{3} is always less than A_{1} for any case, A_{3} is unable to enhance the For A_{4} (three red curves in topleft corner), the preconvex mirror generates a virtual image, i.e. < 0 and M_{c} < 0. It is observed that A_{4} monotonously decreases with M_{c} for all d values. When M_{c} < 1, the would be enhanced (A_{4} > A_{1}). Especially when M_{c} becomes smaller than 0.3 (the region confined by vertical dashed lines), A_{4} gains significantly (similar as A_{2}). But it needs to be pointed out that a too small value of M_{c} is generally associated with unacceptably large optical aberrations delivered by the prefocusing (A_{2}) or diverging (A_{4}) mirrors, and should be avoided in the system design. According to Fig. 2 and the discussion above, A_{2} can only achieve enhancement within the region M_{c} < 0.3, while A_{4} could achieve this outside the region, having a larger flexibility for the system design. Therefore, configuration A_{4} with a preconvex mirror was chosen to develop an optimal spectrometer with enhanced (with respect to A_{1}).
2.2. Resolution enhanced flatfield spectrometer
We need to proceed in the following steps to design an enhanced flatfield spectrometer, using configuration A_{4}.
(a) Establish a set of fundamental parameters (refer to the beginning of Section 2). Gaussianlight source; source size: σ_{s} = 50 µm (r.m.s); source divergence angle: 20 µrad (r.m.s); D_{0} = 24000 lines cm^{−1}; m = 1; wavelength range: 2–5 nm; L = 30 m; and optical elements with meridionally cylindrical profiles.
(b) Determine the image distance of grating r′. The magnification of a diffraction grating is
where the minimum value of is set to the pixel size of the CCD, which is the spatial limit to resolve the α and β are the incidence and diffraction angles of the grating, respectively.
at the detector; represents the effective source size of the grating created by the preconvex mirror;From the previous discussion, the object distance of the grating is r = d − (L − d)M_{c} (where −1 < M_{c} < 0 for configuration A_{4} in Fig. 2). Then the image distance of the grating should meet the following requirement,
So, r′ is a function of d and M_{c}, and could be interpreted as follows: an upstream preconvex mirror creates a new light source with a new effective object distance for the grating, which determines the minimal image distance the grating should have.
(c) Achieve the `flat field'. The groove density of a VLS grating is
where the VLS coefficients D_{i} could be optimized through the elimination of optical aberrations in various orders for the system, using the scheme we developed previously (Li & Li, 2018). In addition, the grating on a cylindrically concave substrate with optimized VLS coefficients allows the achievement of an excellent meridional `flatfield' at its detector plane.
According to Fermat's principle for geometrical optics, the optimal imaging in meridional coordinates could be achieved through zeroing the firstorder derivative of the lightpath function connecting the light source and the image via optics (since the grating is a dispersive optic, various wavelengths are associated with different preferable optical paths) (Samson et al., 1998). In particular, the F terms, e.g. the first few dominants, should satisfy the following equations crossing the wavelength range,
where R is the cylindrical radius of the grating. More specifically, the equation of F_{100} is actually the grating formula; F_{200} is related to the meridional focus, and could be utilized to characterize the `defocus' over the whole spectral range; and F_{300} and F_{400} are associated with the `coma' and `spherical aberration', respectively.
The imaging distance of the grating which achieves the optimal flatfield for the entire spectral range, according to (Li & Li, 2018)
Each set of parameters would lead to a unique optimal meridional radius R and coefficient D_{1} only, then D_{2} and D_{3} could be derived at the central wavelength λ_{0} by letting F_{300}(λ_{0}) = 0 and F_{400}(λ_{0}) = 0 via equations (10) and (11).
(d) Correction of aberrations. The above discussion is only applicable to a single concave grating. In the case where a prefocusing (divergent) mirror is implemented in the system, the optical aberrations propagation from the upstream mirror need to be taken into account.
The primary aberrations of an upstream convex mirror could be calculated in a similar way as equations (9)–(11), using the optical path function and the relevant Fterms,
where the reflection angle from the convex mirror is equal to the incident angle α_{c}, r_{c} and are the object and image distance of the convex mirror, respectively, and R_{c} is its meridional radius.
Setting F_{200_c} = 0 leads to
since r_{c} = L − d and = (L − d)M_{c} (−1 < M_{c} < 0), where the convex mirror forms a reduced virtual image. So, the overall F terms for the system consisting of a preconvex mirror and a concave VLS grating could be recalculated by
where M_{g} is the magnification of the grating [refer to equation (5)], since the F term is proportional to the line width of the spectrum; the (−1) term in the formula is due to the virtual image created by the convex mirror (while it represents the real source of the grating effectively).
When the beam passes through the optical system, the optical aberrations will broaden the beam size from the ideal spectral imaging distribution, the aberration broadening effect in the detector domain could be expressed as
where w is the illuminated meridional length of the grating, l is the illuminated sagittal length, and F_{ijk} defines the optical aberrations in various orders, e.g. in equations (17)–(19) (the subscript i or j denotes the meridional or sagittal coordinate, respectively, k represents the orthogonal coordinate with i and j).
Therefore, the meridional radius R and coefficient D_{1} of the VLS grating could be reoptimized by letting r = d − > 0 in equation (12) to obtain the best flatfield for the whole spectral range, while D_{2} and D_{3} should be modified as well by solving equations (18)–(19) at the centre wavelength λ_{0}.
From the above discussion, most of the parameters in the optical system could be determined, while among them d and M_{c} are special variables. In the next section, we will introduce a scheme to explore the desirable values of d and M_{c} to optimize the system design.
3. System optimization
3.1. Spot diagram and quality
In a system with prefocusing (diverging) mirror and VLS grating, the optical aberration distribution is more complicated and difficult to calculate precisely. Even implementing the VLS grating, the perfect aberration compensation is difficult to achieve, so the residual aberration terms would spread the spectral line width to reduce the
of the system.According to the discussion in the previous sections (refer to A_{4} in Fig. 2), we find out:
(a) The decreases with M_{c} (magnification of the preconvex mirror) monotonously for all spacing values of d, while too small M_{c} should be eliminated in the design since the corresponding aberrations would be too large to compensate.
(b) The system prefers a larger d to deliver a relatively higher The larger the value of d, the further the preconvex mirror is separated from the grating, leading to a larger illuminated area on it. As a result, advanced grating manufacturing techniques are needed to enhance the effective optical area with considerably small fabrication errors.
Keeping these in mind, a resolutionenhanced spectrometer could be developed via implementing a predivergent mirror, and the system optimization should at least minimize optical aberrations to maintain a decent y_{i}) of the outgoing rays and the line width of the diffracted beam distributed at the detector is used to calibrate the imaging quality at each specific wavelength,
In order to evaluate the of the system for different parameter sets, we refer to a raytracing program and analyze the spot diagram on the detector plane. The ratio of standard deviation of the meridional coordinates (where is the average value of y_{i}, N is the total number of diffracted rays in the simulation (here it is set to 10000), and the denominator of equation (21) represents the ideal line width of the beam footprint on the detector, and could be calculated by (Li & Li, 2018)
where θ is defined as the angle in between the central diffraction beam and the normal of the Xray detector, r and r′(λ) are the object and image distances of the grating, respectively, and and M_{g}(λ) are the primary source size and effective magnification of the grating defined in equation (5), respectively.
Generally, the larger the value of Q, the greater the optical dispersion and the worse the imaging quality; and vice versa. The spot diagrams at 5 nm for three different sets of d and M_{c} were obtained from the SHADOW raytracing program (Sanchez del Rio et al., 2011) and presented in Fig. 3 for comparison, where the Q value for each case was calculated to evaluate the corresponding spectral imaging quality.
As depicted in Fig. 3, the imaging quality of (a) is quite good, exhibiting an evenly distributed and symmetric feature, while the image qualities of (b) and (c) are a lot worse; where the distribution of the outgoing beam deviates from an ideal Gaussian peak, showing certain degrees of asymmetry. The Q values of the latter two (Q_{b} = 0.795, Q_{c} ≃ 1.923) are much larger than for the first one (Q_{a} = 0.441), implicating that the system is not always optimized, especially when the optical aberrations are not well corrected. Generally, the actual is significantly less than the ideal case, so we establish the criteria Q to identify the realistic spectral quality for various cases. However, the parameters of M_{c} and d are dependent on each other, and searching for an optimal set of parameters is not straightforward. Thus, a machinelearning scheme is introduced to narrow down the pool for exploring the various variables in demand and to improve the efficiency for identification of the optimal system, which will be discussed next.
3.2. System optimization through machinelearning scheme
Following the previous section, the machinelearning scheme is organized as follows: d and M_{c} are set as the input variables, the rest of parameters of the optical system are either fixed or determined according to the input variables associatively, while the imaging quality Q is the output. Through iterative modelling and learning, the machine could nicely predict the imaging quality of the system with different sets of parameters, thus approaching the best values of d and M_{c}.
More specifically, the Support Vector Machine (SVM) is introduced to do the job through implementing the structural risk minimization inductive principle to obtain generalization from a limited number of learning patterns to predict further results (Vapnik, 1963; Vapnik & Chervonenkis, 1964). SVM has two main categories: Support Vector Classification (SVC) and Support Vector Regression (SVR) (Vapnik, 2001); here the latter is utilized to minimize the system errors to achieve generalized performance, where the computation is based on a linear regression function in a multidimensional space (≫3) while the input data are mapped via a nonlinear scheme. In current research, we adopted the powerful software LIBSVM and model developed by Chang & Lin (2011).
Again, the parameters described at the beginning of Section 2 were used: wavelength range, 2–5 nm; size of light source, 50 µm (r.m.s); beam divergence angle, 20 µrad (r.m.s); Gaussian type; D_{0} = 24000 lines cm^{−1}, both the incident angles for grating and convex mirror set to 89°, L = 30 m etc. Multiple sets of d and M_{c} were used as the two input variables of the support vector machine for training. Besides the preset parameters for each set, the rest of the parameters of the spectrometer, e.g. VLS coefficients, radii of mirrors etc., could be determined associatively to achieve the system optimization. Then the and image quality were evaluated by the raytracing spot diagram and the justified standard deviation Q [defined by equation (21)]. There are 233 sets of samples generated in total, within certain restrictions (given below), where i is the index of the samples; among them, the first 200 samples selected randomly were input to LIBSVM for training and calibration, and the last 33 were used as verification. For a system with only two featured input variables, LIBSVM can easily gain convergence. An equation of Q(d,M_{c}) could be obtained to predict the spectral image quality to reconstruct an optimal system specifically, thus various input quantities of M_{c} and d would lead to different Q values. Then the optimal set possessing the highest ideal while satisfying the Q constraint could be identified. The general restrictions for the system optimization are described below,
Using a simple grid searching scheme, the best set of parameters were found: M_{c} = −0.427, d = 14.02 m. The optimization process is demonstrated in Fig. 4. The blue mesh in Fig. 4(a) shows the Q distribution profile with dependence on d and M_{c}, and the regime for Q(d,M_{c}) < 0.51 (empirical value) meets the restriction for system optimization. By projecting it onto the plane Q = 0, the effective domain for valid d and M_{c} is determined. When M_{c} is small (M_{c} < 0.3), the optical elements spacing d also needs to be small to meet the constraint. On the other hand, when M_{c} is relatively larger, the choices of `d' are more flexible. The distribution profile of A_{4}(d, M_{c}) is plotted in Fig. 4(b); there is a trend of higher for smaller M_{c} and larger d. The colour curves in the plane of A_{4} = 0 are associated with the equalresolution contour from the A_{4} profile, i.e. casting all available sets of d and M_{c} with identical ideal Meanwhile the valid domain obtained from Fig. 4(a) is plotted on the plane of A_{4} = 0 against various contour lines of A_{4}. It is not difficult to find out that the optimization approaches the contour line with a of 285000, which intersects with the effective domain to identify an optimal set of parameters: M_{c} = −0.427, d = 14.02 m. The other parameters of the system were determined associatively and are listed in Table 1.

It should be pointed out that the results above were obtained by machine learning for the quality of Q function) at 5 nm. Similarly, the machinelearning scheme could be applied to the other wavelengths in the spectral range. Fig. 4(c) demonstrates the Q distribution with different sets of M_{c} and d at wavelengths of 2 nm, 3.5 nm and 5 nm. The vertical axial range is set to 0.41 < Q < 0.55, as the `zoomin' feature of Fig. 4(a) to highlight and compare the magnitudes of the Q values for the optimized system at different wavelengths. It can be seen that, within the effective domain (for system optimization), Q_{2nm} (black stars) and Q_{3.5nm} (red circles, central wavelength) have similar distribution profiles, while Q_{5nm} (blue squares) are slightly larger than the other two, implicating that the image quality for 5 nm is lowest throughout the wavelength range. This indicates that optimization of Q_{5nm} is not just achieving an optimal system at the single wavelength of 5 nm; the process would lead to an optimal system spanning the entire `water window', i.e. 2–5 nm.
(4. More comments on raytracing – aberration and fabrication errors
In the previous section, we formulated a novel scheme for the design of a resolutionenhanced spectrometer, by implementing a preconvex mirror to generate a reduced virtual image, which acts as an effectively real source for the VLS grating downstream. The aberrations of the convex mirror should also be considered and combined with the grating in system design and optimization. The SVM is used to explore the optimal parameters more efficiently, and to eliminate the system's primary aberrations throughout the wavelength range to achieve extremely high
with excellent simultaneously.In order to evaluate the actual A_{4}, a number of primary factors need to be considered and analysed. First, the spectral line width at the detector due to the light source size is (i.e. the ideal line width) (Li & Li, 2018)
of a realistic spectrometerThus, the ideal spectral resolution could be calculated by A_{ideal} = λ/Δλ, assuming a Gaussian beam in an aberrationfree optical system, whose is mainly limited by the light source size , enhanced by a factor of 1/M_{c} from A_{1}. In a real optical system, the optical aberrations are nonnegligible, which will broaden the spectral width distribution of an ideal Gaussian beam substantially, according to
where Δy_{ijk} is the meridional beam size at the detector [refer to equation (20)], and the first few dominant aberration terms are (only for the meridional components, thus the sagittal index l = 0)
The explicit expressions of F_{200_sum}, F_{300_sum} and F_{400_sum} were already given in equations (17)–(19), which are independent of either w or l.
For an optical system aiming for exceptionally high spectral resolution, the requirements for the fabrication error (or height error) are very critical, including the slope error and surface roughness etc. for both the preconvex mirror and the grating, which broadens the spectral line width by
where SE_{CM} and SE_{G} represent the meridional slope error of the convex mirror and grating, respectively. Assuming that they have an identical value, i.e. SE_{CM} = SE_{G}, then the accumulative slope error of the system is
The upper bound of the spectral width due to the slope error [refer to equations (28)–(30)] could be set to that of the source size [refer to equation (23)], then the slope error of the optical element should be
Using the source size and diffraction angle β at 5 nm, the expected slope error should be smaller than 0.1 µrad. Currently the fabrication requirement for SE = 0.1 µrad is very challenging and rare, even for the most advanced grating manufacturing techniques [there are reports about achieving an optical slope error of better than 0.05 µrad though (Dvorak et al., 2016)]. Since our ultimate goal is to develop a broadband spectrometer with exceptional resolution over the whole spectral range (>100000), it is worthwhile demanding cuttingedge grating fabrication technology.
When all effects in a realistic spectrometer are included, the resolution can be recalculated,
The spectrometer model in Table 1 could be used to calculate the various terms via implementing equations (23), (25)–(27) and (28)–(30), and the results are shown in Fig. 5(a). The source size term Δλ_{s} seems to be dominating, almost constant within the spectral range (since the source size is assumed to be constant throughout the spectral range). The slopeerror term Δλ_{SE} is the second largest component. The spectral broadenings due to three primary aberration components (Δλ_{200}, Δλ_{300} or Δλ_{400}) are relatively small and well confined.
The corresponding resolving powers for various terms in Fig. 5(a) are exhibited in Fig. 5(b), where the ideal spectral resolution A_{ideal} = λ/Δλ_{s} (thick black), the theoretical resolution (thick red) A_{theory} = λ/Δλ_{sum}, and the result from the raytracing program A_{trace} (discrete blue disks) and a control group A_{control} (grey) calculated by equation (1) using an identical L, are overlaid for comparison. Obviously, the theoretical of a realistic spectrometer A_{4} (thick red), including the contribution from slope error and optical aberrations, is still considerably larger than the ideal of a singlegrating spectrometer A_{1} (grey). This indicates that, if the precision of grating manufacturing were pushed to the extreme limit, the system would achieve even higher spectral resolution, approaching the ideal value A_{ideal} (black).
Additionally, the raytracing results for the spectrometer with configuration in Table 1 are presented in Fig. 6. The bottom part of the figure shows the spectral distributions at the optimal detector plane throughout the `waterwindow' range (i.e. 2–5 nm), where the length scales in the meridional (2000 mm) and sagittal (20 mm) directions are quite different. Figs. 6(a)–6(d) exhibit the and resolution at each individual wavelength (2, 3, 4 and 5 nm in terms of λ and λ + Δλ), each in an identical detector domain of a rectangle of dimensions 20 mm (sagittal) × 0.1 mm (meridional). In particular, the FWHM beam widths for each wavelength in the meridional coordinate are illustrated in specific subplots, which are set to be larger than the typical pixel size of a CCD detector, ∼10 µm, to guarantee the realization of the spectral resolution. According to equation (6), the image distance of the grating r′ should be at least about 30 m for an optimal spectrometer A_{4} to achieve the ideal of 300000. This means that the length scale of the outgoing beam of the spectrometer would be very large, and hence so would the detector range. While our design delivers an excellent flatfield crossing throughout the spectral range, the CCD detector could be mounted and scanned on a more or less straight guiderail to cover the entire spectrum.
5. Discussion and conclusion
In summary, we report a novel spectrometer design in combination with a cylindrically convex premirror and a cylindrically concave VLS grating (both in the meridional). The design could not only provide a decent flatfield at the detector domain but also enhance the e.g. the magnification, creating a real or virtual image), in order to calculate and compensate the overall aberration of the system accurately. (3) A realistic optical system always possesses errors, e.g. optical aberrations and fabrication errors, thus the beam would be broader than and deviate from an aberrationfree ideal Gaussian distribution; and the standard deviation of the outgoing beam's spot diagram could be used to reflect the image quality. (4) The support vector machines can quickly learn from the input data and reconstruct the prediction formula to explore the optimal system with excellent imaging quality. By implementing a nonlinear programming script, an optimized parameter set of M_{c} and d, associated with the highest could be identified. (5) A spectrometer system with extremely high always has very high demands for precise manufacturing of optical components, i.e. requiring exceptionally small slope errors and surface roughness for the optical elements in the system.
substantially. Our main findings in the current research are: (1) If a convex mirror is inserted in between the light source and the grating to create a reduced virtual image (acting as a secondary real source point for the grating), the resolution of the system would be enhanced. (2) Generally, if a premirror (convex or concave) is inserted upstream of the grating, its optical aberration should be included and justified (The position and magnification of the preconvex mirror are the crucial parameters in the current spectrometer design, which also constrain the selection for the object and image distances of the grating, thus reducing the number of variables for system optimization. Implementation of a machinelearning scheme could explore and identify the optimal system delivering an excellent resolution while maintaining minimal optical aberrations with fairly high efficiency. In general, by implementing the SVM in a single PC with a fourcore CPU, it would take roughly an hour to explore and establish an optimal system with appropriate parameters. Although we mainly discussed a spectrometer design for the `water window', the algorithm owns universal adaptability, which could be easily extended to a much broader photon energy range through an appropriate modification of the design parameters. We are planning to utilize the current scheme to develop a highresolution spectrometer spanning the ∼keV range in the near future. It is worthwhile mentioning that the scheme could be applied straightforwardly to many types of experiments which pursue highest spectral resolution through the introduction of the L, obviously). More remarkably, in the current spectrometer design, the extremely high (100000–200000) could be realized at a rather large source size (50 µm r.m.s.), which is not possible for any type of previous designs.
enhancement structure to grating diffractionbased instruments. It could provide a relatively higher compared with a singlegrating spectrometer (assuming both systems possess an identical primary object distance ofFootnotes
^{1}This article will form part of a virtual special issue containing papers presented at the PhotonDiag2018 workshop.
Acknowledgements
The authors thank for the staff and facility support from the Department of
Science & Technology, Shanghai Institute of Applied Physics, Shanghai Synchrotron Radiation Facility, Chinese Academy of Sciences.Funding information
The following funding is acknowledged: National Science Foundation of China (NSFC) (11475249); Youth 1000Talent Program in China (Y326021061).
References
Ament, L. J. P., van Veenendaal, M., Devereaux, T. P., Hill, J. P. & van den Brink, J. (2011). Rev. Mod. Phys. 83, 705–767. Web of Science CrossRef CAS Google Scholar
Braicovich, L., van den Brink, J., Bisogni, V., Sala, M. M., Ament, L., Brookes, N., De Luca, G., Salluzzo, M., Schmitt, T., Strocov, V. & Ghiringhelli, G. (2010). Phys. Rev. Lett. 104, 077002. Web of Science CrossRef PubMed Google Scholar
Chang, C. C. & Lin, C. J. (2011). ACM Trans. Intell. Systems Technol. 2, 27. Google Scholar
Choi, I. W., Lee, J. U. & Nam, C. H. (1997). Appl. Opt. 36, 1457–1466. CrossRef PubMed CAS Web of Science Google Scholar
Chuang, Y. D., Shao, Y. C., Cruz, A., Hanzel, K., Brown, A., Frano, A., Qiao, R., Smith, B., Domning, E., Huang, S. W., Wray, L. A., Lee, W. S., Shen, Z. X., Devereaux, T. P., Chiou, J. W., Pong, W. F., Yashchuk, V. V., Gullikson, E., Reininger, R., Yang, W., Guo, J., Duarte, R. & Hussain, Z. (2017). Rev. Sci. Instrum. 88, 013110. Web of Science CrossRef PubMed Google Scholar
Dvorak, J., Jarrige, I., Bisogni, V., Coburn, S. & Leonhardt, W. (2016). Rev. Sci. Instrum. 87, 115109. Web of Science CrossRef PubMed Google Scholar
Engel, R., Düsterer, S., Brenner, G. & Teubner, U. (2016). J. Synchrotron Rad. 23, 118–122. Web of Science CrossRef CAS IUCr Journals Google Scholar
Fan, P. Z., Zhang, Z. Q., Zhou, J. Z., Jin, R. S., Xu, Z. Z. & Guo, X. (1992). Appl. Opt. 31, 6720–6723. CrossRef CAS PubMed Web of Science Google Scholar
Guarise, M., Dalla Piazza, B., Moretti Sala, M., Ghiringhelli, G., Braicovich, L., Berger, H., Hancock, J. N., van der Marel, D., Schmitt, T., Strocov, V., Ament, L. J., van den Brink, J., Lin, P. H., Xu, P., Rønnow, H. M. & Grioni, M. (2010). Phys. Rev. Lett. 105, 157006. Web of Science CrossRef PubMed Google Scholar
Hague, C., Underwood, J., Avila, A., Delaunay, R., Ringuenet, H., Marsi, M. & Sacchi, M. (2005). Rev. Sci. Instrum. 76, 023110. Web of Science CrossRef Google Scholar
Harada, T. & Kita, T. (1980). Appl. Opt. 19, 3987–3993. CrossRef CAS PubMed Web of Science Google Scholar
Harada, Y., Kinugasa, T., Eguchi, R., Matsubara, M., Kotani, A., Watanabe, M., Yagishita, A. & Shin, S. (2000). Phys. Rev. B, 61, 12854–12859. Web of Science CrossRef CAS Google Scholar
Hennies, F., Pietzsch, A., Berglund, M., Föhlisch, A., Schmitt, T., Strocov, V., Karlsson, H. O., Andersson, J. & Rubensson, J.E. (2010). Phys. Rev. Lett. 104, 193002. Web of Science CrossRef PubMed Google Scholar
Hettrick, M. C., Underwood, J. H., Batson, P. J. & Eckart, M. J. (1988). Appl. Opt. 27, 200–202. CrossRef CAS PubMed Web of Science Google Scholar
Koike, M., Sano, K., Gullikson, E., Harada, Y. & Kumata, H. (2003). Rev. Sci. Instrum. 74, 1156–1158. Web of Science CrossRef CAS Google Scholar
Kuiper, P., Guo, J.H., Såthe, C., Duda, L.C., Nordgren, J., Pothuizen, J., de Groot, F. & Sawatzky, G. A. (1998). Phys. Rev. Lett. 80, 5204–5207. Web of Science CrossRef CAS Google Scholar
Li, Z. & Li, B. (2018). J. Synchrotron Rad. 25, 738–747. Web of Science CrossRef CAS IUCr Journals Google Scholar
Nakano, N., Kuroda, H., Kita, T. & Harada, T. (1984). Appl. Opt. 23, 2386–2392. CrossRef PubMed CAS Web of Science Google Scholar
Namioka, T. (1959). J. Opt. Soc. Am. 49, 446–460. CrossRef Web of Science Google Scholar
Pietzsch, A., Sun, Y.P., Hennies, F., Rinkevicius, Z., Karlsson, H. O., Schmitt, T., Strocov, V. N., Andersson, J., Kennedy, B., Schlappa, J., Föhlisch, A., Rubensson, J. E. & Gel'mukhanov, F. (2011). Phys. Rev. Lett. 106, 153004. Web of Science CrossRef PubMed Google Scholar
Saha, T. T. (1985). Appl. Opt. 24, 1856–1863. CrossRef PubMed CAS Web of Science Google Scholar
Saha, T. T. (1988). Appl. Opt. 27, 1492–1498. CrossRef CAS PubMed Web of Science Google Scholar
Samson, J. A., Ederer, D. L., Lucatorto, T. & De Graef, M. (1998). Vacuum Ultraviolet Spectroscopy I. New York: Academic Press. Google Scholar
Sanchez del Rio, M., Canestrari, N., Jiang, F. & Cerrina, F. (2011). J. Synchrotron Rad. 18, 708–716. Web of Science CrossRef CAS IUCr Journals Google Scholar
Schwanda, W., Eidmann, K. & Richardson, M. (1993). J. Xray Sci. Technol. 4, 8–17. CAS PubMed Google Scholar
Schwob, J., Wouters, A., Suckewer, S. & Finkenthal, M. (1987). Rev. Sci. Instrum. 58, 1601–1615. CrossRef CAS Web of Science Google Scholar
Tondello, G. (1979). J. Mod. Opt. 26, 357–371. Google Scholar
Vapnik, V. (1963). Autom. Remote Control, 24, 774–780. Google Scholar
Vapnik, V. (2001). Adv. Neural Inf. Process. Syst. 9, 281–287. Google Scholar
Vapnik, V. & Chervonenkis, A. (1964). Autom. Remote Control, 25, 821–837. Google Scholar
Warwick, T., Chuang, Y.D., Voronov, D. L. & Padmore, H. A. (2014). J. Synchrotron Rad. 21, 736–743. Web of Science CrossRef CAS IUCr Journals Google Scholar
Wolter, H. (1952). Ann. Phys. 445, 94–114. CrossRef Google Scholar
Xiong, G., Hu, Z., Li, H., Zhao, Y., Shang, W., Zhu, T., Wei, M., Yang, G., Zhang, J. & Yang, J. (2011). Rev. Sci. Instrum. 82, 043109. Web of Science CrossRef PubMed Google Scholar
Zhou, K. J., Huang, Y. B., Monney, C., Dai, X., Strocov, V. N., Wang, N. L., Chen, Z. G., Zhang, C., Dai, P., Patthey, L., van den Brink, J., Ding, H. & Schmitt, T. (2013). Nat. Commun. 4, 1470. Web of Science CrossRef PubMed Google Scholar
This is an openaccess article distributed under the terms of the Creative Commons Attribution (CCBY) Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.