Real Spectrum Research Articles

A Census of Archival X-Ray Spectra for Modeling Tidal Disruption Events

Tidal disruption events (TDEs) are highly energetic phenomena that occur when a star is tidally disrupted by the central massive black hole in a galaxy. Fitting the observed X-ray spectra of TDEs with a first-principles, general-relativistic slim-disk model for the emission from the inner accretion disk can constrain the black hole mass M • and dimensionless spin a •. Multiepoch spectra can break degeneracies in parameter estimation, particularly when they include a period of super-Eddington mass accretion. Even one observed super-Eddington epoch can be useful. Constraints on {M •, a •} improve as a power law with the number of spectral counts; the power-law index is higher for a higher mass accretion rate. These results are supported by the successful modeling of real spectra in the nearby (0.0206 ≤ z ≤ 0.145) TDEs ASASSN-14li, 3XMM J150052.0+015452, and 3XMM J215022.4–055108, which were observed over multiple epochs with >1 ks exposure times. Guided by these results, we create an updated and expanded TDE catalog from the Open TDE compilation. We then explore the XMM-Newton and Chandra archives to identify 37 TDE candidates with promising spectra for constraining {M •, a •} with slim-disk model fits. At least seven TDEs are likely associated with intermediate-mass black holes. Three of the 24 TDEs with multiepoch UV/optical photometry from Swift have late-time observations that allow their light curves to be compared directly to model predictions from the X-ray spectral fits. Existing X-ray spectra for other TDEs can be augmented with future optical/UV data. Ultimately, our new TDE catalog will reveal the {M •, a •} distributions traced by TDEs, thereby discriminating among black hole growth scenarios and providing insights on general relativity and dark matter particle candidates. The new TDE catalog is here: https://github.com/aarongoldtooth/Census-of-TDE-and-Archival-X-Ray-UV-Data/blob/main/Full%20New%20TDE%20Catalog%20(Published).tsv, and the codes used to construct it are here: https://github.com/aarongoldtooth/Census-of-TDE-and-Archival-X-Ray-UV-Data.

Nonlinear T-symmetry Quartic, Sextic, Octic Oscillator Models under Real Spectra

We propose nonlinear model T-symmetry operators having quartic, sextic, octic anharmonicity and inverse quadratics under real spectra. In fact, the model operator is non-Hermitian but real in nature. A comparison with the corresponding hermitian counterpart shows higher energy levels (ET ≫ Ehermitian).

Validation and calibration of partitioned integral ocean wave parameters from co-polarized synthetic aperture radar data

The inversion of wave spectrum is the embodiment of the fine observation of ocean swells by synthetic aperture radar. Due to the nonlinear distortion from velocity bunching, the operational Sentinel-1 Wave Mode (WV) level-2 (L2) Ocean (OCN) wave products retrieved by quasi-linear inversion are still quite different from the real wave spectrum, requiring further calibration. Limited by the azimuth cut-off, the quasi-linear inversed wave spectrum mostly concentrates on swells with a wavelength longer than 150 m. 2D spectral partitioning separates different swell systems, denoted as partitions in the Sentinel-1 WV L2 OCN product. We compared and calibrated the Sentinel-1 WV L2 OCN products' swell partitions (More specifically partitioned integral ocean wave parameters: partitioned wave height, partitioned wavelength, and partitioned wave direction) with WAVEWATCH III (WW3) in different polarization modes (HH and VV polarization) and different incidence angles (∼23°-WV1 and ∼ 36°-WV2). The accuracy of swell partitions in the OCN products varies with peak wave direction relative to the Sentinel-1 flight direction, and it is difficult to calibrate the partitions derived from quasi-linear inversion unless deleting partitions severely distorted by nonlinearity and large-scale features. A machine learning method is proposed to calibrate the wave partitions. Spectral distances of collocated Sentinel-1 and WW3 partition pairs larger than two are considered “bad” partitions to be filtered. Inputting high-dimensional features (twenty-one features) derived from Sentinel-1 WV L2 OCN products into the extreme gradient boosting (XGBoost) model can effectively reduce the bias (root-mean-square error, RMSE) of significant wave height (SWH) of all the partitions from ∼0.10 m (∼0.70 m) to ∼0.00 m (∼0.46 m) and the bias (RMSE) of wavelengths of all the partitions from ∼20 m (∼35 m) to ∼0.20 m (∼26 m) compared with WW3 swell partitions. This method improves the precision of the wave partition and retains more partitions in the Sentinel-1 WV L2 OCN products than previous studies, especially for wave partitions with moderate wave height (< 4 m), potentially improving the understanding of swell dynamics globally.

On the Simultaneous Reduction of a Pair of Unitoid Matrices to Diagonal Form

Let A and B be Hermitian n*n matrices with A being nonsingular. According to a well-known theorem of matrix analysis, these matrices can be brought to diagonal form by one and the same Hermitian congruence transformation if and only if the matrix C = A-1B has a real spectrum and can be diagonalized by a similarity. An extension of this assertion to the case where two unitoid matrices are simultaneously reduced to diagonal form is stated and proved.

An Introduction to PT-Symmetric Quantum Mechanics-Time-Dependent Systems

I will provide a pedagogical introduction to non-Hermitian quantum systems that are PT-symmetric, that is they are left invariant under a simultaneous parity transformation (P) and time-reversal (T). I will explain how generalised versions of this antilinear symmetry can be utilised to explain that these type of systems possess real eigenvalue spectra in parts of their parameter spaces and how to set up a consistent quantum mechanical framework for them that enables a unitary time-evolution. In the second part I will explain how to extend this framework to explicitly time-dependent Hamiltonian systems and report in particular on recent progress made in this context. I will explain how to construct the essential key quantity in this framework, the time-dependent Dyson map and metric and solutions to the time-dependent Schrödinger equation, in an algebraic fashion, using time-dependent Darboux transformations, utilising Lewis-Riesenfeld invariants, point transformations and some approximation methods. I comment on the ambiguities of this metric and demonstrate that this can even lead to infinite series of metric operators. I conclude with some applications to PT-symmetrically coupled oscillators, demonstrate the equivalence of the time-dependent double wells and unstable anharmonic oscillators and show how the unphysical PT-symmetrically broken regions in the parameter space for the time-independent theory becomes physical in the explicitly time-dependent systems. I discuss how this leads to a prolongation of the otherwise rapidly decaying von Neumann entropy. The so-called sudden death of the entropy is stopped at a finite value.1

Assessment of the role of the protected land in the preservation of the steppe in Southwest Algeria (Case of the Rogassa Region)

In the Algerian steppe (Rogassa) overgrazing led to the reduction of the surface of the pasture land and its biodiversity. The objective of this work was to highlight the importance of the protected land in maintaining biodiversity and preserving the degraded areas. A mixed sampling for the two different sites was adopted, where fifty floristic surveys were conducted using the Braun-Blanquet method. The analysis of the real biological spectrum of the unprotected land was marked by the strong dominance of the therophytes. However, in the protected land the chamaephytes dominated all of the biological types. The analysis of the biogeographic spectra revealed the reappearance of the endemic species (+3%). The Shannon Index was very important in this matter (2.43). The Student’s t-test of the biodiversity’s index showed a highly significant difference between the two sites. The ascending hierarchical classification revealed that the protected land is subdivided into four groups dominated respectively by: Macrochloa tenacissima, Lygeum spartum, Artemisia erba-alba and Hordeum murinum. The results of the Jaccard dissimilarity index indicated a strong difference of the order of 80% between the two areas.

Use of machine learning and principal component analysis to retrieve nitrogen dioxide (NO2) with hyperspectral imagers and reduce noise in spectral fitting

Abstract. Nitrogen dioxide (NO2) is an important trace-gas pollutant and climate agent whose presence also leads to spectral interference in ocean color retrievals. NO2 column densities have been retrieved with satellite UV–Vis spectrometers such as the Ozone Monitoring Instrument (OMI) and the Tropospheric Monitoring Instrument (TROPOMI) that typically have spectral resolutions of the order of 0.5 nm or better and spatial footprints as small as 3.6 km × 5.6 km. These NO2 observations are used to estimate emissions, monitor pollution trends, and study effects on human health. Here, we investigate whether it is possible to retrieve NO2 amounts with lower-spectral-resolution hyperspectral imagers such as the Ocean Color Instrument (OCI) that will fly on the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite set for launch in early 2024. OCI will have a spectral resolution of 5 nm and a spatial resolution of ∼ 1 km with global coverage in 1–2 d. At this spectral resolution, small-scale spectral structure from NO2 absorption is still present. We use real spectra from the OMI to simulate OCI spectra that are in turn used to estimate NO2 slant column densities (SCDs) with an artificial neural network (NN) trained on target OMI retrievals. While we obtain good results with no noise added to the OCI simulated spectra, we find that the expected instrumental noise substantially degrades the OCI NO2 retrievals. Nevertheless, the NO2 information from OCI may be of value for ocean color retrievals. OCI retrievals can also be temporally averaged over timescales of the order of months to reduce noise and provide higher-spatial-resolution maps that may be useful for downscaling lower-spatial-resolution data provided by instruments such as OMI and TROPOMI; this downscaling could potentially enable higher-resolution emissions estimates and be useful for other applications. In addition, we show that NNs that use coefficients of leading modes of a principal component analysis of radiance spectra as inputs appear to enable noise reduction in NO2 retrievals. Once trained, NNs can also substantially speed up NO2 spectral fitting algorithms as applied to OMI, TROPOMI, and similar instruments that are flying or will soon fly in geostationary orbit.

Non-stationary quantum mechanics in hybrid non-Hermitian interaction representation

A new formulation of unitary quantum mechanics is proposed. Conceptually our proposal is inspired not only by the conventional interaction picture (where the wave functions and the observables do both vary with time) and by the newer, non-Hermitian version of Schrödinger picture (where the Hamiltonians with real spectra are still stationary) but also by their universal non-Hermitian interaction-picture “overlap” (NIP) in which the physical inner-product metric in Hilbert space is also time-dependent. The main idea of the innovation (called “hybrid interaction picture”, HIP) lies in a partial simplification of the NIP Hilbert-space metric achieved via a partial preconditioning of the operators (in this sense we call their necessary Hermitization “hybrid”). Via an illustrative example it is shown that the HIP approach might open new ways towards the technically optimal quantum-model-building strategies.

The entropy of galaxy spectra: how much information is encoded?

Abstract The inverse problem of extracting the stellar population content of galaxy spectra is analysed here from a basic standpoint based on information theory. By interpreting spectra as probability distribution functions, we find that galaxy spectra have high entropy, thus leading to a rather low effective information content. The highest variation in entropy is unsurprisingly found in regions that have been well studied for decades with the conventional approach. We target a set of six spectral regions that show the highest variation in entropy – the 4000 Å break being the most informative one. As a test case with real data, we measure the entropy of a set of high-quality spectra from the Sloan Digital Sky Survey, and contrast entropy-based results with the traditional method based on line strengths. The data are classified into star-forming (SF), quiescent (Q), and active galactic nucleus (AGN) galaxies, and show – independently of any physical model – that AGN spectra can be interpreted as a transition between SF and Q galaxies, with SF galaxies featuring a more diverse variation in entropy. The high level of entanglement complicates the determination of population parameters in a robust, unbiased way, and affects traditional methods that compare models with observations, as well as machine learning (especially deep learning) algorithms that rely on the statistical properties of the data to assess the variations among spectra. Entropy provides a new avenue to improve population synthesis models so that they give a more faithful representation of real galaxy spectra.

Automatic classification of signal regions in 1H Nuclear Magnetic Resonance spectra.

The identification and characterization of signal regions in Nuclear Magnetic Resonance (NMR) spectra is a challenging but crucial phase in the analysis and determination of complex chemical compounds. Here, we present a novel supervised deep learning approach to perform automatic detection and classification of multiplets in 1H NMR spectra. Our deep neural network was trained on a large number of synthetic spectra, with complete control over the features represented in the samples. We show that our model can detect signal regions effectively and minimize classification errors between different types of resonance patterns. We demonstrate that the network generalizes remarkably well on real experimental 1H NMR spectra.

QUIJOTE scientific results – IV. A northern sky survey in intensity and polarization at 10–20 GHz with the multifrequency instrument

ABSTRACT We present QUIJOTE intensity and polarization maps in four frequency bands centred around 11, 13, 17, and 19 GHz, and covering approximately 29 000 deg2, including most of the northern sky region. These maps result from 9000 h of observations taken between May 2013 and June 2018 with the first QUIJOTE multifrequency instrument (MFI), and have angular resolutions of around 1°, and sensitivities in polarization within the range 35–40 µK per 1° beam, being a factor ∼2–4 worse in intensity. We discuss the data processing pipeline employed, and the basic characteristics of the maps in terms of real space statistics and angular power spectra. A number of validation tests have been applied to characterize the accuracy of the calibration and the residual level of systematic effects, finding a conservative overall calibration uncertainty of 5 per cent. We also discuss flux densities for four bright celestial sources (Tau A, Cas A, Cyg A, and 3C274), which are often used as calibrators at microwave frequencies. The polarization signal in our maps is dominated by synchrotron emission. The distribution of spectral index values between the 11 GHz and WMAP 23 GHz map peaks at β = −3.09 with a standard deviation of 0.14. The measured BB/EE ratio at scales of ℓ = 80 is 0.26 ± 0.07 for a Galactic cut |b| &amp;gt; 10°. We find a positive TE correlation for 11 GHz at large angular scales (ℓ ≲ 50), while the EB and TB signals are consistent with zero in the multipole range 30 ≲ ℓ ≲ 150. The maps discussed in this paper are publicly available.

Classification of pathogenic bacteria by Raman spectroscopy combined with variational auto-encoder and deep learning.

Rapid and early identification of pathogens is critical to guide antibiotic therapy. Raman spectroscopy as a noninvasive diagnostic technique provides rapid and accurate detection of pathogens. Raman spectrum of single cells serves as the "fingerprint" of the cell, revealing its metabolic characteristics. Rapid identification of pathogens can be achieved by combining Raman spectroscopy and deep learning. Traditional classification techniques frequently require lots of data for training, which is time costing to collect Raman spectra. For trace samples and strains that are difficult to culture, it is difficult to provide an accurate classification model. In order to reduce the number of samples collected and improve the accuracy of the classification model, a new pathogen detection method integrating Raman spectroscopy, variational auto-encoder (VAE), and long short-term memory network (LSTM) is proposed in this paper. We collect the Raman signals of pathogens and input them to VAE for training. VAE will generate a large number of Raman spectral data that cannot be distinguished from the real spectrum, and the signal-to-noise ratio is higher than that of the real spectrum. These spectra are input into the LSTM together with the real spectrum for training, and a good classification model is obtained. The results of the experiments reveal that this method not only improves the average accuracy of pathogen classification to 96.9% but also reduces the number of Raman spectra collected from 1000 to 200. With this technology, the number of Raman spectra collected can be greatly reduced, so that strains that are difficult to culture or trace can be rapidly identified.

Counterdiabatic driving for pseudo- and antipseudo- Hermitian systems

In this work, we study the counterdiabatic (CD) driving scheme and population transfer in pseudo- and antipseudo-Hermitian systems. By discussing the adiabatic condition for non-Hermitian system and the relation between the adiabatic evolution and the real energy spectrum, we derive the CD driving scheme for pseudo- and antipseudo-Hermitian systems. Based on these, we derive the condition that an eigenstate of a non-Hermitian Hamiltonian is self-normalized to guarantee the normalized populations of the bare states in non-Hermitian systems. Our results are illustrated by studying the CD driving for a non-Hermitian three-level system. A scheme to realize the perfect population transfer between the bare states is proposed in the antipseudo-Hermitian case.

Demodulation of optical fiber sensors by MEMS tunable filter

An optical fiber sensor (OFS) demodulation system based on Micro-Electro-Mechanical System (MEMS) tunable filter (MTF) has been proposed and demonstrated in this study. By scanning the MTF, the acquired OFS curves were considered as the convolution of the tunable filter function and the real OFS spectrum. However, the wavelength setting error of the MTF significantly limited the measurement accuracy of the proposed demodulation system. To solve the problem, dual etalons with complementary phase shifts were adopted to calibrate the MTF in real time. For demonstration, a fiber Bragg grating and an open cavity Fabry-Pérot sensor were successfully demodulated by the MTF. Experimental results indicated that the measurement error of the MTF demodulator was improved from ±31 pm to ±3 pm, with the sensing resolution of 0.1 °C and 0.01 MPa, better than traditional grating-based spectrometers..

Elementary proof for the existence of periodic points with real and simple spectrum for diffeomorphisms in any dimension

Using only elementary techniques we prove that for any $ C^r $ diffeomorphism, $ f $, of a compact manifold of dimension $ d&gt;2 $, $ 1\leq r\leq \infty $, admitting a transverse homoclinic intersection, we can find a $ C^1 $-open neighborhood of $ f $ containing a $ C^1 $-open and $ C^r $-dense set of $ C^r $ diffeomorphisms which have a periodic point with real and simple spectrum. We use this fact to prove that $ C^r $-generically among $ C^r $ diffeomorphisms with horseshoes, we have density of periodic points with real and simple spectrum inside the horseshoe. As a corollary, we obtain that generically in the $ C^1 $-topology the unique obstruction to the existence of periodic points with real and simple spectrum are the Morse-Smale diffeomorphisms with all the periodic points admitting non-real eigenvalues.

Critical evaluation of the use of artificial data for machine learning based de novo peptide identification

Proteins are essential components of all living cells and so the study of their in situ expression, proteomics, has wide reaching applications. Peptide identification in proteomics typically relies on matching high resolution tandem mass spectra to a protein database but can also be performed de novo. While artificial spectra have been successfully incorporated into database search pipelines to increase peptide identification rates, little work has been done to investigate the utility of artificial spectra in the context of de novo peptide identification. Here, we perform a critical analysis of the use of artificial data for the training and evaluation of de novo peptide identification algorithms. First, we classify the different fragment ion types present in real spectra and then estimate the number of spurious matches using random peptides. We then categorise the different types of noise present in real spectra. Finally, we transfer this knowledge to artificial data and test the performance of a state-of-the-art de novo peptide identification algorithm trained using artificial spectra with and without relevant noise addition. Noise supplementation increased artificial training data performance from 30% to 77% of real training data peptide recall. While real data performance was not fully replicated, this work provides the first steps towards an artificial spectrum framework for the training and evaluation of de novo peptide identification algorithms. Further enhanced artificial spectra may allow for more in depth analysis of de novo algorithms as well as alleviating the reliance on database searches for training data.

Nonlocal soliton in non-parity-time-symmetric coupler

Parity-time (PT) symmetric is not a necessary condition for achieving a real spectrum and some studies about realizing real spectra in non-PT-symmetric systems with arbitrary gain–loss profiles have been presented recently. By tuning the free parameters in non-PT-symmetric potentials, phase transition could also be induced. Above phase transition point, discrete complex eigenvalues bifurcate out from continuous real eigenvalues in the interior of the continuous spectrum. In this work, we investgate the existence and stability of solitons in nonlocal nonlinear couplers with non-PT-symmetric complex potentials both below and above phase transition. There are several discrete eigenvalues in the linear spectra of the non-PT-symmetric system used here. With the square-operator iteration method, we find that different continuous families of solitions can bifurcate from different discrete linear eigenvalues. Moreover, linear-stability analysis collaborated with direct numerical propagation simulations demonstrates that the nonlocal solitions can be stable in a range of parameter values. we first address the cases below the phase transition. To be specific, when we fix the coupling coefficient and vary the degree of nonlocality, it’s found that fundamental solitons, dipole solitons, tripolar solitons, quadrupole solitons bifurcate from the largest,the second-largest, the third-largest and the fifth-largest discrete eigenvalue, respectively. These nonlocal solitons are all stable in the low power region. With an increase of the degree of nonlocality, the stability region shrinks for the fundamental solitons while it widens for the dipole and multiplole solitons. At the same time, the power of all the stable solitons increases with the increase of the degree of nonlocality. By varying the coupling coefficient, the arrangement of soliton families emerging in the discrete interval of the linear spectrum can be changed. For example, the dipole solitons bifurcate from the third-or fourth-largest discrete eigenvalue while the tripolar solitons bifurcate from the fifth largest discrete eigenvalue. Above phase transition,the fundamental solitons are unstable in the low and high power region but are stable in the moderate power region. The stability region shrinks with the increasing degree of nonlocality. We also find the family of dipole solitons bifurcates from the second-largest discrete eigenvalue, but all the dipole solitons are unstable. In addition, we find that the eigenvalues in linear-stability spectra of solitons emerge as conjugation pairs.

Simulating Solar Radio Bursts Using Generative Adversarial Networks

Solar flares are one of the most extreme drivers of space weather in our solar system. The impulsive solar radio emission associated with a solar flare is known as a solar radio burst (SRB). They are generally studied in dynamic spectra and are classified into five major spectral classes, ranging from Type I to Type V, based on their form and frequency, and time duration. Due to their intricate characterisation, generating a training set for object-detection and classification models of such phenomena is a difficulty in machine learning. Current algorithms implement parametric modelling where the quantity, grouping, intensity, drift rate, heterogeneity, start–end frequency and start–end time of Type-III and Type-II radio bursts are all random. However, this model does not factor in the true shape or general features seen in real dynamic spectra observations of the Sun, which can be crucial when training classification or object-detection algorithms. In this research, we introduce a methodology named a Generative Adversarial Network (GAN) for generating realistic SRB simulations. By using real examples of Type-III and Type-II SRB data, we can train GANs to generate images almost comparable to real observed data. Furthermore, we evaluate the results of the generated model using human perception, then we compare and contrast the results using a metric known as the Fréchet Inception Distance.

Multitude of exceptional points in van der Waals magnets

Several works have recently addressed the emergence of exceptional points (EPs), i.e., degeneracies of non-Hermitian Hamiltonians, in the long-wavelength dynamics of coupled magnetic systems. Here, by focusing on the driven magnetization dynamics of a van der Waals ferromagnetic bilayer, we show that exceptional points can appear over extended portions of the first Brillouin zone as well. Furthermore, we demonstrate that the effective non-Hermitian magnon Hamiltonian, whose eigenvalues are purely real or come in complex conjugate pairs, respects an unusual wavevector-dependent pseudo-Hermiticity. Finally, for both armchair and zigzag nanoribbon geometries, we discuss both the complex and purely real spectra of the topological edge states and their experimental implications.

Electron energy loss spectroscopy database synthesis and automation of core-loss edge recognition by deep-learning neural networks

The ionization edges encoded in the electron energy loss spectroscopy (EELS) spectra enable advanced material analysis including composition analyses and elemental quantifications. The development of the parallel EELS instrument and fast, sensitive detectors have greatly improved the acquisition speed of EELS spectra. However, the traditional way of core-loss edge recognition is experience based and human labor dependent, which limits the processing speed. So far, the low signal–noise ratio and the low jump ratio of the core-loss edges on the raw EELS spectra have been challenging for the automation of edge recognition. In this work, a convolutional-bidirectional long short-term memory neural network (CNN-BiLSTM) is proposed to automate the detection and elemental identification of core-loss edges from raw spectra. An EELS spectral database is synthesized by using our forward model to assist in the training and validation of the neural network. To make the synthesized spectra resemble the real spectra, we collected a large library of experimentally acquired EELS core edges. In synthesize the training library, the edges are modeled by fitting the multi-Gaussian model to the real edges from experiments, and the noise and instrumental imperfectness are simulated and added. The well-trained CNN-BiLSTM network is tested against both the simulated spectra and real spectra collected from experiments. The high accuracy of the network, 94.9%, proves that, without complicated preprocessing of the raw spectra, the proposed CNN-BiLSTM network achieves the automation of core-loss edge recognition for EELS spectra with high accuracy.