Spectrum Reconstruction Research Articles

NeMu: a comprehensive pipeline for accurate reconstruction of neutral mutation spectra from evolutionary data.

The recognized importance of mutational spectra in molecular evolution is yet to be fully exploited beyond human cancer studies and model organisms. The wealth of intraspecific polymorphism data in the GenBank repository, covering a broad spectrum of genes and species, presents an untapped opportunity for detailed mutational spectrum analysis. Existing methods fall short by ignoring intermediate substitutions on the inner branches of phylogenetic trees and lacking the capability for cross-species mutational comparisons. To address these challenges, we present the NeMu pipeline, available at https://nemu-pipeline.com, a tool grounded in phylogenetic principles designed to provide comprehensive and scalable analysis of mutational spectra. Utilizing extensive sequence data from numerous available genome projects, NeMu rapidly and accurately reconstructs the neutral mutational spectrum. This tool, facilitating the reconstruction of gene- and species-specific mutational spectra, contributes to a deeper understanding of evolutionary mechanisms across the broad spectrum of known species.

Miniature spectrometer based on graded bandgap perovskite filter

Abstract Miniature spectrometer is powerful tool for scientific research and industrial inspection. Here, we report the fabrication of graded perovskite filters with tunable bandgap and their application in constructing miniature spectrometer. The graded perovskite filters were fabricated using a Finkelstein reaction between in-situ formed halogen ion with a preformed MAPbX3 film. The graded bandgap of perovskite films can be well tunned from 400 to 750 nm by controlling the volume ratio between 5,5-dimethyl-1-pyrroline N-oxide and benzyl chloride(bromide). By combining a deep residual network, graded bandgap perovskite film and commercial CMOS sensor chip, a miniature spectrometer is demonstrated, achieving an accurate spectrum reconstruction (PSNR = 40.749) with monochromatic spectral resolution of 1.31 nm.

Energy spectra of elemental groups of cosmic rays with the KASCADE experiment data and machine learning

We report the reconstruction of the mass component spectra of cosmic rays (protons, helium, carbon, silicon and iron) and their mean mass composition, at energies from 1.4 to 100 PeV. The results are derived from the archival data of the extensive air shower experiment KASCADE. We use a novel machine learning technique developed specifically for this reconstruction, and post-LHC hadronic interaction models: QGSJet-II.04, EPOS-LHC and Sibyll 2.3c. We have found an excess of the proton component and a deficit of intermediate and heavy nuclei components compared to the original KASCADE results. The spectra of protons and helium show a knee-like behavior at ∼ 4.4 PeV and ∼ 11 PeV, with significances 5.2σ and 3.9σ, respectively. The spectrum of the iron component has a hint (2.4σ) of a hardening at ∼ 4.5 PeV, which can be interpreted as a counterpart of a hardening in the proton spectrum at 166 TeV, recently reported by the GRAPES-3 experiment. The systematic uncertainties of our analysis were found to be smaller than those of the original KASCADE, as well as those of IceTop and TALE experiments, over the most part of the energy range studied. We also estimated separately the uncertainty related to the difference between the three mentioned hadronic interaction models. We also compute a mean logarithm mass of CR flux as a function of energy. It is in agreement with the results of IceTop, TALE and LHAASO within the uncertainties.

Spectral recovery of broadband waveforms via cross-phase modulation based tunable Talbot amplifier

Physical processes in the Fourier domain play a crucial role in various applications such as spectroscopy, quantum technology, ranging, radio-astronomy, and telecommunications. However, the presence of stochastic noise poses a significant challenge in the detection of broadband spectral waveforms, especially those with limited power. In this study, we propose and experimentally demonstrate a cross-phase modulation (XPM) based spectral Talbot amplifier to recover the broadband spectral waveforms in high fidelity. Through the combination of spectral phase filtering and XPM nonlinear effect in an all-fiber configuration, we demonstrate spectral purification of THz-bandwidth spectral waveforms submerged in strong noise. The proposed spectral Talbot amplifier provides tunable amplification factors from 3 to 10, achieved by flexible control on the temporal waveform of the pump and the net dispersion. We demonstrate up to 10-dB remarkable improvement on optical signal-to-noise ratio (OSNR) while preserving the spectral envelope. Furthermore, our system allows frequency-selective reconstruction of noisy input spectra, introducing a new level of flexibility for spectral recovery and information extraction. We also evaluate numerically the impact of pump intensity deviation on the reconstructed spectral waveforms. Our all-optical approach presents a powerful means for effective recovery of broadband spectral waveforms, enabling information extraction from a noise-buried background.

Research on Azimuth DBF Method of HRWS SPC MAB SAR Imaging Mode with Non-Ideal Antenna Mode

Single-phase center multiple azimuth beam (SPC MAB) mode is an effective method for high-resolution wide-swath (HRWS) SAR imaging. The traditional azimuth spectrum reconstruction method for SPC MAB mode is based on the combination scheme from which fake targets along the azimuth direction arise because the inter-beam interference is not considered. When the real antenna mode is inconsistent with the ideal one, the disadvantages of the combination scheme become more serious. In this paper, based on the basic theory of the low-pass, band-limited, multiple-channel under-sampling and reconstruction, a novel digital beam-forming method is proposed for the SPC MAB imaging mode with ideal antenna mode first. The method analyzes the system functions of the sub-beams, based on which digital beam-forming filters are designed for all the sub-beams. The designed filters can reconstruct the correct wide-bandwidth azimuth spectrum and suppress the inter-beam interference simultaneously. Furthermore, the proposed method is extended to SPC MAB mode with the non-ideal antenna mode. The simulation experiments prove the validities of the proposed method both for azimuth spectral reconstruction and the inter-beams interfering suppressing, no matter that the SPC MAB’s antenna mode is ideal or non-ideal.

Transient Interference Excision and Spectrum Reconstruction with Partial Samples Using Modified Alternating Direction Method of Multipliers-Net for the Over-the-Horizon Radar.

Transient interference often submerges the actual targets when employing over-the-horizon radar (OTHR) to detect targets. In addition, modern OTHR needs to carry out multi-target detection from sea to air, resulting in the sparse sampling of echo data. The sparse OTHR signal will raise serious grating lobes using conventional methods and thus degrade target detection performance. This article proposes a modified Alternating Direction Method of Multipliers (ADMM)-Net to reconstruct the target and clutter spectrum of sparse OTHR signals so that target detection can be performed normally. Firstly, transient interferences are identified based on the sparse basis representation and then excised. Therefore, the processed signal can be seen as a sparse OTHR signal. By solving the Doppler sparsity-constrained optimization with the trained network, the complete Doppler spectrum is reconstructed effectively for target detection. Compared with traditional sparse solution methods, the presented approach can balance the efficiency and accuracy of OTHR signal spectrum reconstruction. Both simulation and real-measured OTHR data proved the proposed approach's performance.

Reconstruction of indoor gamma-ray background spectrum for HPGe detectors

Background reconstruction stands as a critical technique for diverse domains, such as signal isolation for gamma spectroscopy, pre-testing of radiation shielding designs, and environmental monitoring. Traditional methods aim to seek the optimal intensity of the background source under a fixed spatial distribution. However, their efficacy is often constrained due to the limited number of variable parameters, resulting in inadequate degrees of freedom and suboptimal performance. This paper introduces an innovative method for background reconstruction, aiming to overcome the limitations of existing approaches. The proposed technique involves the simultaneous adjustment of both intensities and shell thicknesses of a series of monoenergetic sources, modeled as spherical shells. The reconstructed spectrum is conceptualized as a superposition of simulated spectra from these sources, with the optimized parameters being the intensity and thickness of each source. The objective is to minimize the disparity in regions of interest (ROIs) between reconstructed and target spectra through a multivariate nonlinear optimization process, incorporating distinct weight factors for each ROI. The efficacy of the proposed method is validated using a simulation-generated theoretical spectrum. Results demonstrate successful reconstruction, with a high overall concordance between reconstructed and target spectra, showcasing reliability of the method. Subsequently, the method is applied to the reconstruction of an experimental spectrum, and a comparative analysis with a fixed spherical shell thickness method reveals the superior agreement of our proposed method with the experimental spectra.

Application of neural networks for the reconstruction of supernova neutrino energy spectra following fast neutrino flavor conversions

Application of neural networks for the reconstruction of supernova neutrino energy spectra following fast neutrino flavor conversions

Abstract P30: MUTATIONAL SIGNATURE ASSIGNMENT HETEROGENEITY IS WIDESPREAD AND CAN BE ADDRESSED BY ENSEMBLE APPROACHES

Abstract Single base substitution (SBS) mutational signatures have become standard practice in cancer genomics. In lieu of de novo signature extraction, reference signature assignment allows users to estimate the activities of pre-established SBS signatures within individual malignancies. Several tools have been developed for this purpose, each with differing methodologies. However, due to a lack of standardization, there may be inter-tool variability in signature assignment. We deeply characterized three assignment strategies and five SBS signature assignment tools. We observed that assignment strategy choice can significantly influence results and interpretations. Despite varying recommendations by tools, Refit performed best by reducing overfitting and maximizing reconstruction of the original mutational spectra. Even after uniform application of Refit, tools varied remarkably in signature assignments both qualitatively (Jaccard Index = 0.38 – 0.83) and quantitatively (Kendall tau-b = 0.18 – 0.76). This phenomenon was exacerbated for “flat” signatures such as the homologous-recombination repair deficiency (HRD) signature SBS3. An ensemble approach (EnsembleFit), which leverages output from all five tools, increased SBS3 assignment accuracy in BRCA1/2-deficient breast carcinomas. After generating synthetic mutational profiles for thousands of pan-cancer tumors, EnsembleFit reduced signature activity assignment error 15.9 – 24.7% on average using COSMIC and non-standard reference signature sets. We have also released the EnsembleFit web portal (https://www.ensemblefit.pittlabgenomics.com) for users to generate or download ensemble-based SBS signature assignments using any strategy and combination of tools. Overall, we show that signature assignment heterogeneity across tools and strategies is non-negligible and propose a viable, ensemble solution. Citation Format: Wu Jialun Andy, Akila Perera, Linganesan Kularatnarajah, Anna Korsakova, Jason James Pitt. MUTATIONAL SIGNATURE ASSIGNMENT HETEROGENEITY IS WIDESPREAD AND CAN BE ADDRESSED BY ENSEMBLE APPROACHES [abstract]. In: Proceedings of Frontiers in Cancer Science; 2023 Nov 6-8; Singapore. Philadelphia (PA): AACR; Cancer Res 2024;84(8_Suppl):Abstract nr P30.

Evidence of Short-Lived High-Energy Emissive State and Triplet Character of the Self-Trapped Exciton in Cs3Cu2I5 Perovskite.

Cs3Cu2I5 perovskite displays a Stokes-shifted photoluminescence (PL) at 445 nm, attributed to the self-trapped excitons (STEs). Unlike that observed in other perovskite materials, the free-exciton emission is not evidenced in this case. Herein, we reveal the existence of a short-lived high-energy emission centered around 375 nm through the reconstruction of time-resolved emission spectra (TRES), which is independent of the shape/size of Cs3Cu2I5 perovskite. This high-energy emission is proposed to originate from the free-exciton-derived distorted S1 state of the 0D Cs3Cu2I5 moiety. Moreover, STE PL (∼445 nm) was found to have phosphorescence characteristics. Theoretical calculation confirms a facile intersystem crossing at the Franck-Condon geometry, indicating the high lifetime of the STE and its triplet nature. The existence of a high-energy emissive state and the phosphorescent nature of the STE PL band provide valuable insights that could advance our understanding of the photophysics in these materials.

Satellite wave 2D spectrum partition based on the PI-vit-GAN(physically-informed ViT-GAN) method

The abundant spectral data provided by satellite technology are crucial for interpreting the complex marine environment, and the effective and accurate analysis of these data is particularly important for coastal engineering. In this regard, this study proposes a Physically Informed ViT-GAN (PI-ViT-GAN) automatic partitioning method, based on CFOSAT satellite wave spectrum data. Specifically, the model consists of a generator and discriminator. The generator utilizes a contrastive learning strategy as pretraining and through the self-attention mechanism of the ViT model, it focuses on key parts of the spectrum to extract wave group features and wave element parameters. Partitioning-head joint training realizes the output of wave group partition element indices. Subsequently, the discriminator uses the wave group features and a parametric model for spectrum reconstruction and computes the error with the original observed spectrum to evaluate the partition and reconstruction effects. Additionally, this model incorporates two physically corrected functions, wave system classification loss and merging loss, based on the wave age criterion, thereby guiding the training process, and enhancing model efficiency. The results indicate that the reconstructed theoretical spectrum, obtained through the utilization of this method, aligns well with the original sea wave spectrum, demonstrating a precision superior to the spectral partitioning product of CFOSAT's own SWIM. Combining the robust learning capability of the transformer and the regularization of physical prior knowledge, this model can achieve precise, low-cost automated analysis of satellite wave spectra, providing a new scalable method for big data analysis in marine and coastal engineering.

Low-rank reconstruction for mid-infrared spectroscopy using broadband ultra-wide-angle photonic filters and IReg algorithm

In this study, a broadband mid-infrared photonic filter is designed. It has the advantages of polarization insensitivity, ultrawide angle, high manufacturability, and easy integration. For infrared spectrum reconstruction, the use of filters as spectral sampling channels can eliminate the influence of slits or complex optical splitting elements in spectral imaging technology. Further, we propose a low-rank matrix logarithm regularization algorithm based on a three-segment filter function (IReg algorithm) combined with the designed filter for a variety of mid-infrared detector front ends, which can significantly suppress noise interference. Here, we demonstrate that the stability of the spectral reconstruction accuracy under the large disturbance environment obtained by the IReg algorithm is much higher than those of the L2 and L1 algorithms. In the low-dimensional matrix state, the accuracy and stability of the ill-conditioned linear equations are guaranteed, the anti-noise ability is enhanced, the effect of data dimensionality reduction is realized, and the accuracy of mid-infrared spectral reconstruction is improved.

Reconstructing the mid-infrared spectra of galaxies using ultraviolet to submillimeter photometry and Deep Generative Networks

The mid-infrared spectra of galaxies are rich in features such as the Polycyclic Aromatic Hydrocarbon (PAH) and silicate dust features which give valuable information about the physics of galaxies and their evolution. For example they can provide information about the relative contribution of star formation and accretion from a supermassive black hole to the power output of galaxies. However, the mid-infrared spectra are currently available for a very small fraction of galaxies that have been detected in deep multi-wavelength surveys of the sky. In this paper we explore whether Deep Generative Network methods can be used to reconstruct mid-infrared spectra in the 5–35μm range using the limited multi-wavelength photometry in ∼20 bands from the ultraviolet to the submillimeter which is typically available in extragalactic surveys. For this purpose we use simulated spectra computed with a combination of radiative transfer models for starbursts, active galactic nucleus (AGN) tori and host galaxies. We find that our method using Deep Generative Networks, namely Generative Adversarial Networks and Generative Latent Optimization models, can efficiently produce high quality reconstructions of mid-infrared spectra in ∼ 60% of the cases. We discuss how our method can be improved by using more training data, photometric bands, model parameters or by employing other generative networks.

Proton spectroscopy for 11B(p,α)2α fusion reaction with RCF films: calibration and unfolding procedure

The reaction occurring between protons and 11B isotope (p+11B → 3α+8.7 MeV) has recently attracted attention as a possible candidate to overcome the generation of high-energy neutrons via the more studied Deuterium-Tritium fusion reaction. Since the early 2000s, several experiments have been carried out to investigate the viability of triggering this aneutronic reaction in laser-target interaction schemes. During these experiments, the total number of escaping α particles is measured to infer fusion reaction efficiency. However, the accurate detection of α particles in such experiments poses a real challenge.In this scenario, RadioChromic Films(RCFs) arranged in a stack configuration can be used for the fluence and energy spectra reconstruction of generated protons, being this mandatory information in both “pitcher-catcher” and “in plasma” p-11B irradiation schemes. Nevertheless, RCF response exhibits a dependence on Linear Energy Transfer (LET), which leads to an underestimation of the response in high-LET conditions. This can result in dosimetric errors if not properly taken into account.In this work, an analytical procedure able to reconstruct the incident energy spectra in an RCF stack was developed and validated thanks to a calibration procedure that was established for high and low proton energy (4–60 MeV) beams to properly reconstruct the incident spectra in the “pitcher-catcher” irradiation scheme.

From Pseudo to Real: Generalized Subspace Method for Power Spectrum Reconstruction

Subspace methods are widely used in statistical signal processing owing to their super-resolution. Conventional subspace methods, such as multiple signal classification (MUSIC) identify parameters containing frequency and direction of arrival by calculating pseudo-spectra without physical amplitudes. However, the real value of amplitude plays a significant role in fault diagnosis and condition monitoring. Additionally, due to hardware limitations and increased frequency, uniform (Nyquist) sampling becomes challenging. Nonuniform sampling, an alternative to high-rate sampling is common in practice. Consequently, Nonuniform (sub-Nyquist) signal processing methods are urgently required. In this paper, a generalized subspace method (GSM) is proposed to bridge the pseudo spectrum with the real spectrum. The proposed method is a supplement to the existing subspace methods and is applicable to both uniform and nonuniform sampling. In the GSM, the Fourier coefficient of the autocorrelation function is derived by signal subspace from the perspective of basis matching. Then, an alias-free real power spectrum is obtained by combining the power spectrum reconstructed by GSM with the classical pseudo spectrum. Finally, simulation and experiment demonstrate the performance of the proposed method in power spectrum reconstruction. Even under the dual influence of undersampled and nonuniform, the proposed method is still effective.

CMB-S4: Iterative Internal Delensing and r Constraints

The tightest constraints on the tensor-to-scalar ratio r can only be obtained after removing a substantial fraction of the lensing B-mode sample variance. The planned Cosmic Microwave Background (CMB)-S4 experiment (cmb-s4.org) will remove the lensing B-mode signal internally by reconstructing the gravitational lenses from high-resolution observations. We document here a first lensing reconstruction pipeline able to achieve this optimally for arbitrary sky coverage. We make it part of a map-based framework to test CMB-S4 delensing performance and its constraining power on r, including inhomogeneous noise and two non-Gaussian Galactic polarized foreground models. The framework performs component separation of the high-resolution maps, followed by the construction of lensing B-mode templates, which are then included in a parametric small-aperture map cross-spectra-based likelihood for r. We find that the lensing reconstruction and framework achieve the expected performance, compatible with the target σ(r) ≃ 5 · 10−4 in the absence of a tensor signal, after an effective removal of 92%–93% of the lensing B-mode variance, depending on the simulation set. The code for the lensing reconstruction can also be used for cross-correlation studies with large-scale structures, lensing spectrum reconstruction, cluster lensing, or other CMB lensing-related purposes. As part of our tests, we also demonstrate the joint optimal reconstruction of the lensing potential with the lensing curl potential mode at second order in the density fluctuations.

Model-Driven Deep Neural Network for Enhanced AoA Estimation Using 5G gNB

High-accuracy positioning has become a fundamental enabler for intelligent connected devices. Nevertheless, the present wireless networks still rely on model-driven approaches to achieve positioning functionality, which are susceptible to performance degradation in practical scenarios, primarily due to hardware impairments. Integrating artificial intelligence into the positioning framework presents a promising solution to revolutionize the accuracy and robustness of location-based services. In this study, we address this challenge by reformulating the problem of angle-of-arrival (AoA) estimation into image reconstruction of spatial spectrum. To this end, we design a model-driven deep neural network (MoD-DNN), which can automatically calibrate the angular-dependent phase error. The proposed MoD-DNN approach employs an iterative optimization scheme between a convolutional neural network and a sparse conjugate gradient algorithm. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed method in enhancing spectrum calibration and AoA estimation.

Research on the Processing of Image and Spectral Information in an Infrared Polarization Snapshot Spectral Imaging System

In order to solve the problems of a low target recognition rate and poor real-time performance brought about by conventional infrared imaging spectral detection technology under complex background conditions or in the detection of targets of weak radiation or long distance, a kind of infrared polarization snapshot spectral imaging system (PSIFTIS) and a spectrum information processing method based on micro-optical devices are proposed in this paper, where the synchronous acquisition of polarization spectrum information is realized through the spatial modulation of phase with a rooftop-shaped multi-stage micro-mirror and the modulation of the polarization state of light with a micro-nanowire array. For the polarization interference image information obtained, the infrared polarization spectrum decoupling is realized by image segmentation, optical path difference matching, and image registration methods, the infrared polarization spectrum reconstruction is realized by Fourier transform spectral demodulation, and the infrared polarization image fusion is realized by decomposing and reconstructing the high- and low-frequency components of the polarization image based on the Haar wavelet transform. The maximum spectral peak wavenumber error of the four polarization channels of the polarization spectrum reconstruction is less than 2 cm−1, and the polarization angle error is within 1°. Ultimately, compared with the unprocessed polarization image unit, the peak signal-to-noise ratio is improved by 45.67%, the average gradient is improved by 8.03%, and the information entropy is improved by 56.98%.

Searching for local features in primordial power spectrum using genetic algorithms

ABSTRACT We present a novel methodology for exploring local features directly in the primordial power spectrum using a genetic algorithm pipeline coupled with a Boltzmann solver and Cosmic Microwave Background data (CMB). After testing the robustness of our pipeline using mock data, we apply it to the latest CMB data, including Planck 2018 and CamSpec PR4. Our model-independent approach provides an analytical reconstruction of the power spectra that best fits the data, with the unsupervised machine learning algorithm exploring a functional space built off simple ‘grammar’ functions. We find significant improvements upon the simple power-law behaviour, by Δχ2 ≲ −21, consistently with more traditional model-based approaches. These best-fits always address both the low-ℓ anomaly in the TT spectrum and the residual high-ℓ oscillations in the TT, TE, and EE spectra. The proposed pipeline provides an adaptable tool for exploring features in the primordial power spectrum in a model-independent way, providing valuable hints to theorists for constructing viable inflationary models that are consistent with the current and upcoming CMB surveys.

Estimation Method for Nonuniform State of MOIP in Traction Transformer via Curve Reconstruction of Frequency Domain Spectrum

Estimation Method for Nonuniform State of MOIP in Traction Transformer via Curve Reconstruction of Frequency Domain Spectrum