Unmasking the physical information inherent to interstellar spectral line profiles with machine learning

When low-mass stars form, the collapsing cloud of gas and dust goes through several stages which are usually characterized by the shape of their spectral energy distributions. Such classification is based on the cloud morphology only and does not address the dynamical state of the object. In this paper we investigate the initial cloud collapse and subsequent disk formation through the dynamical behavior as reflected in the sub-millimeter spectral emission line profiles. If a young stellar object is to be characterized by its dynamical structure it is important to know how accurately information about the velocity field can be extracted and which observables provide the best description of the kinematics. Of particular interest is the transition from infalling envelope to rotating disk, because this provides the initial conditions for the protoplanetary disk, such as mass and size. We use a hydrodynamical model, describing the collapse of a core and formation of a disk, to produce synthetic observables which we compare to calculated line profiles of a simple parameterized model. Because we know the velocity field from the hydrodynamical simulation we can determine in a quantitative way how well our best-fit parameterized velocity field reproduces the original. We use a molecular line excitation and radiation transfer code to produce spectra of both our hydro dynamical simulation as well as our parameterized model. We find that information about the velocity field can reasonably well be derived by fitting a simple model to either single-dish lines or interferometric data, but preferentially by using a combination of the two. Our result shows that it is possible to establish relative ages of a sample of young stellar objects using this method, independently of the details of the hydrodynamical model.

Spectral line profiles are used to process experimental spectra when solving the inverse problem of computing the collisional parameters of the profiles [1]. The difference in their shapes is due to different physical conditions (hard/soft collisions, high/low pressures, etc.). Numerous different profiles are used in the study of the spectral line parameters of carbon dioxide, methane, methyl halides, and other molecules. The diversity of the line profiles used in the systematization of spectral line parameters adds complexity to the structures of data available in information systems and to the structures of individuals involved in ontological descriptions of the spectral line properties, which characterize the line profiles. A brief classification of spectral line profiles and their parameters is given, and the results of the systematization of spectral data relating to different line profiles used in processing carbon dioxide spectra are presented. The line profiles available in the library are described, and a system is built for importing spectral line parameters derived from the solution of the direct and inverse problems. Computer software for an automatic description of the properties of the solutions imported has been developed. The basic properties of the spectral data compiled in the W@DIS information system provide a description of the outcome of the imported data quality assessment.

We have observed the millimeter-wave rotational spectral lines of CH3CHO, H2CCO, cyclopropenone, and H2CO toward the cyanoployyne peak of Taurus Molecular Cloud-1 (TMC-1 CP). The spectral line profile of CH3CHO is found to reveal a well-separated double peak. It is similar to the line profile of CH3OH, but is much different from those of carbon-chain molecules and C34S. The different line profiles mean different distributions along the line of sight. The similarity of the spectral line profiles between CH3CHO and CH3OH suggests that CH3CHO is mainly formed on dust grains as CH3OH or through gas-phase reactions starting from CH3OH. On the other hand, the spectral line profiles of H2CCO and cyclopropenone are rather similar to those of carbon-chain molecules and C34S, implying their gas-phase productions. H2CO shows a composite spectral line profile reflecting the contributions of both gas-phase and grain-surface productions. In addition, we have detected the spectral lines of CH3CHO and HCOOCH3 toward the methanol peak near TMC-1 CP. We have also tentatively detected one line of (CH3)2O. Considering the chemical youth of TMC-1, the present results indicate that fairly complex organic species have already been formed in the early evolutionary phase of starless cores. TMC-1 is thus recognized as a novel source where formation processes of complex organic molecules can be studied on the basis of the line profiles.

Context. Many novel methods have been proposed to mitigate stellar activity for exoplanet detection as the presence of stellar activity in radial velocity (RV) measurements is the current major limitation. Unlike traditional methods that model stellar activity in the RV domain, more methods are moving in the direction of disentangling stellar activity at the spectral level. As deep neural networks have already been proven to be one of the most effective tools in data mining, in this work, we explore their potential in the context of Earth-like planet detection in RV measurements. Aims. The goal of this paper is to present a novel convolutional neural network-based algorithm that efficiently models stellar activity signals at the spectral level, enhancing the detection of Earth-like planets. Methods. Based on the idea that the presence of planets can only produce a Doppler shift at the spectral level while the presence of stellar activity can introduce a variation in the profile of spectral lines (asymmetry and depth change), we trained a convolutional neural network to build the correlation between the change in the spectral line profile and the corresponding RV, full width at half maximum (FWHM) and bisector span (BIS) values derived from the classical cross-correlation function. Results. This algorithm has been tested on three intensively observed stars: Alpha Centauri B (HD 128621), Tau ceti (HD 10700), and the Sun. By injecting simulated planetary signals at the spectral level, we demonstrate that our machine learning algorithm can achieve, for HD 128621 and HD 10700, a detection threshold of 0.5 m s−1 in semi-amplitude for planets with periods ranging from 10 to 300 days. This threshold would correspond to the detection of a ~4 M⊕ in the habitable zone of those stars. On the HARPS-N solar dataset, thanks to significantly more data, our algorithm is even more efficient at mitigating stellar activity signals and can reach a threshold of 0.2 m s−1, which would correspond to a 2.2 M⊕ planet on the orbit of the Earth. Conclusions. To the best of our knowledge, it is the first time that such low detection thresholds are reported for the Sun, but also for other stars, and therefore this highlights the efficiency of our convolutional neural network-based algorithm at mitigating stellar activity in RV measurements.

The cognitive impairment known as dementia affects millions of individuals throughout the globe. The use of machine learning (ML) and deep learning (DL) algorithms has shown great promise as a means of early identification and treatment of dementia. Dementias such as Alzheimer’s Dementia, frontotemporal dementia, Lewy body dementia, and vascular dementia are all discussed in this article, along with a literature review on using ML algorithms in their diagnosis. Different ML algorithms, such as support vector machines, artificial neural networks, decision trees, and random forests, are compared and contrasted, along with their benefits and drawbacks. As discussed in this article, accurate ML models may be achieved by carefully considering feature selection and data preparation. We also discuss how ML algorithms can predict disease progression and patient responses to therapy. However, overreliance on ML and DL technologies should be avoided without further proof. It’s important to note that these technologies are meant to assist in diagnosis but should not be used as the sole criteria for a final diagnosis. The research implies that ML algorithms may help increase the precision with which dementia is diagnosed, especially in its early stages. The efficacy of ML and DL algorithms in clinical contexts must be verified, and ethical issues around the use of personal data must be addressed, but this requires more study.

The profile of a stellar spectral line is formed by the transfer of radiation through the atmosphere by atomic processes in different chemical elements distributed usually unequally over the surface of a magnetic star.The theory of model atmospheres accounts for all possible physical conditions. Usually one assumes chemical homogeneity with a plane parallel atmosphere. The resulting line profile, however, is strongly deformed by the geometrical influence of the topographic element distribution and the magnetic surface field structure as well as the projection onto the line of sight of the outgoing radiation from all surface points and its integration over the visible disk.Line formation by the geometry of projection and element distribution is used for the inverse procedure of Doppler Imaging by V.L. Khokhlova and her followers. We consider here only the influence of the magnetic field on the line profile including the Stokes parameters -components. The large scatter of measuring points is partly due to the asymmetry of the line profiles!To search for other articles by the author(s) go to: http://adsabs.harvard.edu/abstract_service.html

view Abstract Citations (56) References (25) Co-Reads Similar Papers Volume Content Graphics Metrics Export Citation NASA/ADS Spectral Line Profiles and Luminosities of Astrophysical Water Masers Nedoluha, Gerald E. ; Watson, William D. Abstract The spectral line narrowing and rebroadening that occurs for astrophysical masers as a function of the emergent radiative flux is calculated for the prominent, 22 GHz masing transition of water. The increased line breadths due to hyperfine structure lead to reliable, essentially model-independent upper limits to the emergent flux that tend to be lower than other estimates for these masers. For many 22 GHz masers, including the outbursts in W49 and Orion, the observed line breadths are less than 0.9 km/s. For these, the upper limit to the emergent maser flux is 10 to the 10th K-sr when expressed in terms of the brightness temperature and the solid angle for beaming. It is concluded that the extreme brightness of the interstellar water masers is due to a high degree of beaming and not to more effective pumping. Publication: The Astrophysical Journal Pub Date: February 1991 DOI: 10.1086/185932 Bibcode: 1991ApJ...367L..63N Keywords: Interstellar Masers; Luminosity; Spectral Line Width; Water Masers; Brightness Temperature; Circular Polarization; Computational Astrophysics; Interstellar Magnetic Fields; Astrophysics; LINE PROFILES; MASERS full text sources ADS |

Recently, analysis of solar spectra obtained with the EUV Imaging Spectrograph (EIS) onboard the Hinode satellite has revealed the ubiquitous presence of asymmetries in transition region (TR) and coronal spectral line profiles. These asymmetries have been observed especially at the footpoints of coronal loops and have been associated with strong upflows that may play a significant role in providing the corona with hot plasma. Here, we perform a detailed study of the various processes that can lead to spectral line asymmetries, using both simple forward models and state-of-the-art three-dimensional radiative MHD simulations of the solar atmosphere using the Bifrost code. We describe a novel technique to determine the presence and properties of faint secondary components in the wings of spectral line profiles. This method is based on least-squares fitting of observed so-called R(ed)B(lue) asymmetry profiles with pre-calculated RB asymmetry profiles for a wide variety of secondary component properties. We illustrate how this method could be used to perform reliable double Gaussian fits that are not over- or under-constrained. We also find that spectral line asymmetries appear in TR and coronal lines that are synthesized from our three-dimensional MHD simulations. Our models show that the spectral asymmetries are a sensitive measure of the velocity gradient with height in the TR of coronal loops. The modeled TR shows a large gradient of velocity that increases with height: this occurs as a consequence of ubiquitous, episodic heating at low heights in the model atmosphere. We show that the contribution function of spectral lines as a function of temperature is critical for sensitivity to velocity gradients and thus line asymmetries: lines that are formed over a temperature range that includes most of the TR are the most sensitive. As a result, lines from lithium-like ions (e.g., O VI) are found to be the most sensitive to line asymmetries. We compare the simulated line profiles directly with line profiles observed in the quiet Sun with SOHO/SUMER and Hinode/EIS and find that the shape of the profiles is very similar. In addition, the simulated profiles with the strongest blueward asymmetry occur in footpoint regions of coronal loops, which is similar to what we observe with SUMER and EIS. There is however a significant discrepancy between the simulations and observations: the simulated RB asymmetries are an order of magnitude smaller than the observations. We discuss the possible reasons for this discrepancy. In summary, our analysis shows that observations of spectral line asymmetries can provide a powerful new diagnostic to help constrain coronal heating models.

Many individual studies in the literature observed the superiority of tree-based machine learning (ML) algorithms. However, the current body of literature lacks statistical validation of this superiority. This study addresses this gap by employing five ML algorithms on 200 open-access datasets from a wide range of research contexts to statistically confirm the superiority of tree-based ML algorithms over their counterparts. Specifically, it examines two tree-based ML (Decision tree and Random forest) and three non-tree-based ML (Support vector machine, Logistic regression and k-nearest neighbour) algorithms. Results from paired-sample t-tests show that both tree-based ML algorithms reveal better performance than each non-tree-based ML algorithm for the four ML performance measures (accuracy, precision, recall and F1 score) considered in this study, each at p<0.001 significance level. This performance superiority is consistent across both the model development and test phases. This study also used paired-sample t-tests for the subsets of the research datasets from disease prediction (66) and university-ranking (50) research contexts for further validation. The observed superiority of the tree-based ML algorithms remains valid for these subsets. Tree-based ML algorithms significantly outperformed non-tree-based algorithms for these two research contexts for all four performance measures. We discuss the research implications of these findings in detail in this article.

Many individual studies in the literature observed the superiority of tree-based machine learning (ML) algorithms. However, the current body of literature lacks statistical validation of this superiority. This study addresses this gap by employing five ML algorithms on 200 open-access datasets from a wide range of research contexts to statistically confirm the superiority of tree-based ML algorithms over their counterparts. Specifically, it examines two tree-based ML (Decision tree and Random forest) and three non-tree-based ML (Support vector machine, Logistic regression and k-nearest neighbour) algorithms. Results from paired-sample t-tests show that both tree-based ML algorithms reveal better performance than each non-tree-based ML algorithm for the four ML performance measures (accuracy, precision, recall and F1 score) considered in this study, each at p<0.001 significance level. This performance superiority is consistent across both the model development and test phases. This study also used paired-sample t-tests for the subsets of the research datasets from disease prediction (66) and university-ranking (50) research contexts for further validation. The observed superiority of the tree-based ML algorithms remains valid for these subsets. Tree-based ML algorithms significantly outperformed non-tree-based algorithms for these two research contexts for all four performance measures. We discuss the research implications of these findings in detail in this article.

The aging and multimorbid population and health personnel shortages pose a substantial burden on primary health care. While predictive machine learning (ML) algorithms have the potential to address these challenges, concerns include transparency and insufficient reporting of model validation and effectiveness of the implementation in the clinical workflow. To systematically identify predictive ML algorithms implemented in primary care from peer-reviewed literature and US Food and Drug Administration (FDA) and Conformité Européene (CE) registration databases and to ascertain the public availability of evidence, including peer-reviewed literature, gray literature, and technical reports across the artificial intelligence (AI) life cycle. PubMed, Embase, Web of Science, Cochrane Library, Emcare, Academic Search Premier, IEEE Xplore, ACM Digital Library, MathSciNet, AAAI.org (Association for the Advancement of Artificial Intelligence), arXiv, Epistemonikos, PsycINFO, and Google Scholar were searched for studies published between January 2000 and July 2023, with search terms that were related to AI, primary care, and implementation. The search extended to CE-marked or FDA-approved predictive ML algorithms obtained from relevant registration databases. Three reviewers gathered subsequent evidence involving strategies such as product searches, exploration of references, manufacturer website visits, and direct inquiries to authors and product owners. The extent to which the evidence for each predictive ML algorithm aligned with the Dutch AI predictive algorithm (AIPA) guideline requirements was assessed per AI life cycle phase, producing evidence availability scores. The systematic search identified 43 predictive ML algorithms, of which 25 were commercially available and CE-marked or FDA-approved. The predictive ML algorithms spanned multiple clinical domains, but most (27 [63%]) focused on cardiovascular diseases and diabetes. Most (35 [81%]) were published within the past 5 years. The availability of evidence varied across different phases of the predictive ML algorithm life cycle, with evidence being reported the least for phase 1 (preparation) and phase 5 (impact assessment) (19% and 30%, respectively). Twelve (28%) predictive ML algorithms achieved approximately half of their maximum individual evidence availability score. Overall, predictive ML algorithms from peer-reviewed literature showed higher evidence availability compared with those from FDA-approved or CE-marked databases (45% vs 29%). The findings indicate an urgent need to improve the availability of evidence regarding the predictive ML algorithms' quality criteria. Adopting the Dutch AIPA guideline could facilitate transparent and consistent reporting of the quality criteria that could foster trust among end users and facilitating large-scale implementation.

Abstract:As part of a larger study to elucidate the presence of hydrated minerals on asteroid surfaces, we are developing a robust taxonomic classification system using spectroscopic observations in the vicinity of 3 &amp;#956;m. We have constructed a Python algorithm to identify band centers and band depths near 3 &amp;#181;m for a set of normalized, thermally-corrected asteroid spectra for use to serve as inputs to Python&amp;#8217;s Scikit-Learn library of Machine Learning (ML) algorithms. We anticipate a thorough investigation of both Principal Component Analysis and ML (supervised, unsupervised, and Artificial Neural Network) techniques to assess which technique is likely to be better suited for classifying the 3-&amp;#181;m data. At this writing, we have run tests using Python&amp;#8217;s Agglomerative clustering ML algorithm to examine possible clustering scenarios. These initial steps have given us some familiarity with the mechanics of using ML on the 3-&amp;#181;m dataset as well as serving to identify some possible pitfalls or cul-de-sacs. Presented here are the preliminary results we have obtained.Introduction:Although various techniques have been used, asteroid classification has typically been done via Principal Component Analysis (PCA: [1,2]). PCA is a statistical technique that reduces the dimensionality of a dataset by identifying the most important parameters within a dataset based on their variance. Parameters that exhibit the greatest amount of variance are considered to be of greater importance while parameters with the least amount of variance are considered to be of lower importance. While the PCA technique produces better visualizations of the data by reducing the dimensionality of a dataset, the PCA technique comes with some drawbacks. Disadvantages such as its dependence on scale and information loss due to the orthogonal property of PCA can cause interpretation of PCA results to prove to be a more critical and time-consuming process. Therefore, exploring other means of classification may prove to be worthwhile.Machine Learning (ML) algorithms have had a significant impact on the way in which data is analyzed and interpreted, and have already proven to be a powerfully reliable resource in the field of planetary science. Accordingly, the application of ML to an asteroid taxonomy has the potential to be more efficient, objective, and easy-to-implement than PCA. ML algorithms can be supervised, in which the program &amp;#8220;learns&amp;#8221; from training data and is able to classify new inputs, or unsupervised, in which the program analyzes the dataset to determine patterns such as clusters. [3] used an Artificial Neural Network (ANN, a subset of ML) to classify asteroids, work followed up by [4]. Recent explorations of supervised ML for asteroid taxonomy are promising, and have applied training sets from existing databases to new visible and/or NIR photometric data (e.g. [5,6,7]).We seek to explore the benefits of ML algorithms, as well as compare and contrast to the PCA technique, in the production of an asteroid taxonomy. Our initial exploration has utilized a set of normalized, thermally-corrected asteroid spectra in the vicinity of 3 &amp;#181;m. We have identified band centers and band depths and served this parameter space as inputs to Python&amp;#8217;s Agglomerative clustering ML algorithm.Methodology:Thermal corrections of the asteroid spectra were performed via a forward model that uses a modified version of the Standard Thermal Model (STM: [8]). The forward model treats the beaming parameter as a free parameter adjusting its value for each iteration of the STM until it converges onto a value that yields expected long-wavelength continuum behavior. Spectra were then normalized to unity at a wavelength of 2.3 &amp;#181;m, followed by identification of band centers and band depths near 3 &amp;#181;m using both polynomial and Gaussian fits. In addition, band depths were measured at wavelengths of 2.9 &amp;#181;m and 3.2 &amp;#181;m to gather more information on asteroid band shapes. Lastly, the aforementioned calculated spectral features were input into Python&amp;#8217;s Agglomerative clustering algorithm to determine which asteroid spectra shared similar features.Summary:As part of a larger investigation to better understand hydrated mineralogies as they apply to asteroids, we have begun work towards developing a quantitative taxonomic framework derived from asteroid spectra in the wavelength range from 2.0-4.0 &amp;#181;m. Our exploration thus far of Python&amp;#8217;s Agglomerative clustering algorithm has proven to be fruitful. Minor changes to the parameterization of this algorithm can yield very different results, which naturally can lead to different interpretations. The Agglomerative clustering algorithm is one of many the powerful ML algorithms we will explore against the PCA technique, all of which we will be discussing in our presentation.

Some supernova (SN) explosions show evidence for an interaction with a pre-existing nonspherically symmetric circumstellar medium (CSM) in their light curves, spectral line profiles, and polarization signatures. The origin of this aspherical CSM is unknown, but binary interactions have often been implicated. To better understand the connection with binary stars and to aid in the interpretation of observations, we performed two-dimensional axisymmetric hydrodynamic simulations where an expanding spherical SN ejecta initialized with realistic density and velocity profiles collide with various aspherical CSM distributions. We consider CSM in the form of a circumstellar disk, colliding wind shells in binary stars with different orientations and distances from the SN progenitor, and bipolar lobes representing a scaled down version of the Homunculus nebula ofηCar. We study how our simulations map onto observables, including approximate light curves, indicative spectral line profiles at late times, and estimates of a polarization signature. We find that the SN–CSM collision layer is composed of normal and oblique shocks, reflected waves, and other hydrodynamical phenomena that lead to acceleration and shear instabilities. As a result, the total shock heating power fluctuates in time, although the emerging light curve might be smooth if the shock interaction region is deeply embedded in the SN envelope. SNe with circumstellar disks or bipolar lobes exhibit late-time spectral line profiles that are symmetric with respect to the rest velocity and relatively high polarization. In contrast, SNe with colliding wind shells naturally lead to line profiles with asymmetric and time-evolving blue and red wings and low polarization. Given the high frequency of binaries among massive stars, the interaction of SN ejecta with a pre-existing colliding wind shell must occur and the observed signatures could be used to characterize the binary companion.

The spectral line profiles of ionized emitters in plasmas play an important role in the calculation of opacity, for short-wavelength laser studies, and for the diagnostics of inertial confinement fusion plasmas. Sophisticated theoretical methods and modeling have been advanced and applied in recent years to calculate spectral line profiles in the limits where broadening by electron collisions or by ion microfield dominates. Here, the authors describe recent measurements of spectral line profiles of a z-pinch experiment employing precision plasma diagnostic techniques. In particular, the electron-collisional-broadened 2s--2p transitions in B{sub III} have been investigated because their line profiles provide an excellent test for electron-impact line shape theories and electron collision strength calculations. Although they find good agreement with semiclassical calculations, a factor of two discrepancy with the most elaborate quantum-mechanical five-state close coupling calculations is observed. They discuss the experimental error estimates of the various measured quantities and show that the observed discrepancy can not be explained by experimental shortcomings. They further discuss measurements of non-isolated spectral lines of some {Delta}n = 1 transitions in C{sub IV}--O{sub VI}. For these transitions ion broadening dominates. Excellent agreement for the whole line profile with line broadening calculations is obtained for all cases only when including ion dynamic effects. The latter are calculated using the frequency-fluctuation model and account for about 10--25% of the line width of the considered ions.

The influence of spatial inhomogeneity and temporal fluctuations of the parameters of equilibrium plasma on the intensity and shape of the lines in the spectrum of its radiation was investigated. A closed mathematical model based on the solution of the radiation transfer equation, which describes the formation of the emission spectrum of the plasma volume with given characteristics, has been constructed. A semianalytical approximation that permits adequate description of the line radiation spectra of optically transparent plasma has been developed. The laws of formation of the line radia- tion spectra of inhomogeneous fluctuating plasma volumes and the relations relating the charac- teristics of these spectra to the emissivity and local parameters of the plasma have been found. For the presentation of the results, we have chosen atmospheric-pressure argon plasma. Introduction. Analysis of the intensity and the profiles of spectral lines in the radiation spectrum of plasma is one of the most effective methods of its diagnostics with the use of optical emission spectroscopy (OES). Plasma diagnostics by OES methods is based on the relation between the emissivity of the plasma and its local parameters, which is usually nonlinear. The OES diagnostics involves the recording of the spec- tral line profile and the intensity summed over the line and the observation period. To determine the emissiv- ity of an inhomogeneous plasma, it is necessary to solve the inverse problem. This problem is formally considered as incorrect: a small error in measured data can cause marked errors in the determination of the desired quantities (1). In the general case, it is solved by tomography methods. In so doing, the number of rays of observation and their directions are determined by the character of inhomogeneity, and their number can be very large. In the simplest case of axisymmetric inhomogeneities, the problem reduces to the inverse Abelian transform (2−4). The amount of recorded data increases significantly when the spectral line profiles obtained by scanning the radiation spectrum are used for diagnostics. The instability of plasma imposes addi- tional requirements on the measurement system: the recording time of plasma radiation should be shorter than the characteristic time of fluctuations. Therefore, there is often no point in solving traditionally the inverse problems in plasma OES or such a solution is impossible because of the large errors, excessive complexity of the recording systems, and stringent requirements imposed on them. As a consequence, there are practi- cally no OES methods for measuring local parameters in space and time of the low-temperature plasma of