Decoding astronomical spectra using machine learning

Spectroscopy is one of the cornerstones of modern astronomy. Using spectra, the light from far-away objects measured on Earth can be related back to the physical and chemical conditions of the astronomical matter from which it is emitted. This makes spectroscopy an essential tool for constraining the physical and chemical conditions of the matter in stars, gas, galaxies and all other types of astronomical objects. However, whilst spectra carry a wealth of astronomical information, their analysis is often complicated by difficulties such as degeneracies between input parameters and gaps in our theoretical knowledge. In this thesis, we look towards the rapidly growing field of machine learning as a means of better extracting the information content of astronomical spectra. Chapters 2 and 3 of the thesis are dedicated to the study of spectra originating from the interstellar medium. Chapter 2 of this thesis presents a machine learning emulator for the UCLCHEM astrochemical code which when combined with a Bayesian treatment of the radiative-transfer inverse problem enables a rigorous handling of the degeneracies affecting molecular lines (all within short enough computational timescales to be tractable). Chapter 3 extends upon the work of Chapter 2 on modelling molecular lines and investigates the appropriateness of Non-negative Matrix Factorization, a blind source separation algorithm, for the task of unmixing the gas phases which may exist within molecular line-intensity maps. Chapter 4 and 5 are concerned with the analysis of stellar spectra. In these chapters, we introduce machine learning approaches for extracting the chemical content from stellar spectra which do not rely on manual spectral modelling. This removes the burden of building faithful forward-models of stellar spectroscopy in order to precisely extract the chemistry of stars. The two approaches are also complimentary. Chapter 4 presents a deep-learning approach for distilling the information content within stellar spectra into a representation where undesirable factors of variation are excluded. Such a representation can then be used to directly find chemically identical stars or for differential abundance analysis. However, the approach requires measurements of the to-be-excluded undesirable factors of variation. The second approach which is presented in Chapter 5 addresses this shortcoming by learning which factors of variation should be excluded using spectra of open clusters. However, because of the low number of known open clusters, whilst the method constructed in Chapter 4 is non-linear and parametrized by a feedforward neural network, the approach presented in Chapter 5 was made linear

Massive data compression for parameter-dependent covariance matrices

We show how the massive data compression algorithm MOPED can be used to reduce, by orders of magnitude, the number of simulated data sets which are required to estimate the covariance matrix required for the analysis of Gaussian-distributed data. This is relevant when the covariance matrix cannot be calculated directly. The compression is especially valuable when the covariance matrix varies with the model parameters. In this case, it may be prohibitively expensive to run enough simulations to estimate the full covariance matrix throughout the parameter space. This compression may be particularly valuable for the next generation of weak lensing surveys, such as proposed for Euclid and Large Synoptic Survey Telescope, for which the number of summary data (such as band power or shear correlation estimates) is very large, ∼104, due to the large number of tomographic redshift bins which the data will be divided into. In the pessimistic case where the covariance matrix is estimated separately for all points in an Monte Carlo Markov Chain analysis, this may require an unfeasible 109 simulations. We show here that MOPED can reduce this number by a factor of 1000, or a factor of ∼106 if some regularity in the covariance matrix is assumed, reducing the number of simulations required to a manageable 103, making an otherwise intractable analysis feasible

Disentangled Representation Learning for Astronomical Chemical Tagging

Modern astronomical surveys are observing spectral data for millions of stars. These spectra contain chemical information that can be used to trace the Galaxy's formation and chemical enrichment history. However, extracting the information from spectra and making precise and accurate chemical abundance measurements is challenging. Here we present a data-driven method for isolating the chemical factors of variation in stellar spectra from those of other parameters (i.e., Teff, log g, [Fe/H]). This enables us to build a spectral projection for each star with these parameters removed. We do this with no ab initio knowledge of elemental abundances themselves and hence bypass the uncertainties and systematics associated with modeling that rely on synthetic stellar spectra. To remove known nonchemical factors of variation, we develop and implement a neural network architecture that learns a disentangled spectral representation. We simulate our recovery of chemically identical stars using the disentangled spectra in a synthetic APOGEE-like data set. We show that this recovery declines as a function of the signal-to-noise ratio but that our neural network architecture outperforms simpler modeling choices. Our work demonstrates the feasibility of data-driven abundance-free chemical tagging

De-noising of galaxy optical spectra with autoencoders

Optical spectra contain a wealth of information about the physical properties and formation histories of galaxies. Often though, spectra are too noisy for this information to be accurately retrieved. In this study, we explore how machine learning methods can be used to de-noise spectra and increase the amount of information we can gain without having to turn to sample averaging methods such as spectral stacking. Using machine learning methods trained on noise-added spectra - SDSS spectra with Gaussian noise added - we investigate methods of maximising the information we can gain from these spectra, in particular from emission lines, such that more detailed analysis can be performed. We produce a variational autoencoder (VAE) model, and apply it on a sample of noise-added spectra. Compared to the flux measured in the original SDSS spectra, the model values are accurate within 0.3-0.5 dex, depending on the specific spectral line and S/N. Overall, the VAE performs better than a principle component analysis (PCA) method, in terms of reconstruction loss and accuracy of the recovered line fluxes. To demonstrate the applicability and usefulness of the method in the context of large optical spectroscopy surveys, we simulate a population of spectra with noise similar to that in galaxies at $z = 0.1$ observed by the Dark Energy Spectroscopic Instrument (DESI). We show that we can recover the shape and scatter of the MZR in this "DESI-like" sample, in a way that is not possible without the VAE-assisted de-noising.Comment: 14 pages, 10 figures, 6 tables, accepted for publication in MNRA

De-noising of galaxy optical spectra with autoencoders

Optical spectra contain a wealth of information about the physical properties and formation histories of galaxies. Often though, spectra are too noisy for this information to be accurately retrieved. In this study, we explore how machine learning methods can be used to de-noise spectra and increase the amount of information we can gain without having to turn to sample averaging methods such as spectral stacking. Using machine learning methods trained on noise-added spectra - SDSS spectra with Gaussian noise added - we investigate methods of maximising the information we can gain from these spectra, in particular from emission lines, such that more detailed analysis can be performed. We produce a variational autoencoder (VAE) model, and apply it on a sample of noise-added spectra. Compared to the flux measured in the original SDSS spectra, the model values are accurate within 0.3-0.5 dex, depending on the specific spectral line and S/N. Overall, the VAE performs better than a principle component analysis (PCA) method, in terms of reconstruction loss and accuracy of the recovered line fluxes. To demonstrate the applicability and usefulness of the method in the context of large optical spectroscopy surveys, we simulate a population of spectra with noise similar to that in galaxies at z = 0.1 observed by the Dark Energy Spectroscopic Instrument (DESI). We show that we can recover the shape and scatter of the MZR in this ‘DESI-like’ sample, in a way that is not possible without the VAE-assisted de-noising

Disentangled Representation Learning for Astronomical Chemical Tagging

Modern astronomical surveys are observing spectral data for millions of stars. These spectra contain chemical information that can be used to trace the Galaxy's formation and chemical enrichment history. However, extracting the information from spectra, and making precise and accurate chemical abundance measurements are challenging. Here, we present a data-driven method for isolating the chemical factors of variation in stellar spectra from those of other parameters (i.e. \teff, \logg, \feh). This enables us to build a spectral projection for each star with these parameters removed. We do this with no ab initio knowledge of elemental abundances themselves, and hence bypass the uncertainties and systematics associated with modeling that rely on synthetic stellar spectra. To remove known non-chemical factors of variation, we develop and implement a neural network architecture that learns a disentangled spectral representation. We simulate our recovery of chemically identical stars using the disentangled spectra in a synthetic APOGEE-like dataset. We show that this recovery declines as a function of the signal to noise ratio, but that our neural network architecture outperforms simpler modeling choices. Our work demonstrates the feasibility of data-driven abundance-free chemical tagging.Comment: Accepted by ApJ. code available at github.com/drd13/tagging-packag

Os ideais e a sublimação

Le but de cet article est celui darticuler le travail de sublimation avec la constitution des idéaux qui caractérisent léconomie narcissique du sujet freudien. En partant de la différenciation, soulignée para Freud, entre la sublimation et lidéalisation, la manière par laquelle le procès sublimatoire sapproche du travail de desidéalisation promu par lhumour est démontrée. Dans la sublimation, ainsi que dans lhumour, on assiste à une érection du moi dans le moi, en même temps que sélabore le deuil des objets idéalisés de lenfance. Le rôle de la pulsion de mort dans le surgissement du procès créatif est aussi analysé.En este artículo, se pretende relacionar el trabajo de la sublimación con la constitución de los ideales que caracterizan la economía narcisista del sujeto freudiano. Partiendo de la diferenciación, anotada por Freud, entre sublimación e idealización, se demuestra de qué forma el proceso sublimatorio se aproxima del trabajo de desidealización promovido por el humor. En la sublimación, así como en el humor asistimos a ¿una erección del yo en el yo?, al mismo tiempo que elaboramos el luto por los objetos idealizado de la infancia. El papel de la pulsión de la muerte para la emergencia de procesos creativos también es analizado.The intention of this article is to relate the work of the sublimation with that of the constitution of the ideas that characterise the narcissistic economy of the Freudian subject. Based on the differentiation, highlighted by Freud, between sublimation and idealization, the article aims to demonstrate in what manner the sublimate process approaches the work of breaking down the defence mechanism through humour. In sublimation, as with humour, are we witnessing the erecting of I in the Self? While we elaborate the mourning for the idealized objects of childhood. The role of the death drive in the emergence of the creative process is also analysed.Pretende-se, nesse artigo, relacionar o trabalho da sublimação com a constituição dos ideais que caracterizam a economia narcísica do sujeito freudiano. Partindo da diferenciação, sublinhada por Freud, entre sublimação e idealização, demonstra-se de que modo o processo sublimatório se aproxima do trabalho de desidealização promovido pelo humor. Na sublimação, assim como no humor, assiste-se a uma ereção do eu no eu, ao mesmo tempo em que se elabora o luto pelos objetos idealizado da infância. O papel da pulsão de morte para a emergência de processos criativos é também analisado