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 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

Trends in Molecular Emission from Different Extragalactic Stellar Initial Mass Functions

Banerji et al. (2009) suggested that top-heavy stellar Initial Mass Functions (IMFs) in galaxies may arise when the interstellar physical conditions inhibit low-mass star formation, and they determined the physical conditions under which this suppression may or may not occur. In this work, we explore the sensitivity of the chemistry of interstellar gas under a wide range of conditions. We use these results to predict the relative velocity-integrated antenna temperatures of the CO rotational spectrum for several models of high redshift active galaxies which may produce both top-heavy and unbiased IMFs. We find that while active galaxies with solar metallicity (and top-heavy IMFs) produce higher antenna temperatures than those with sub-solar metallicity (and unbiased IMFs) the actual rotational distribution is similar. The high-J to peak CO ratio however may be used to roughly infer the metallicity of a galaxy provided we know whether it is active or quiescent. The metallicity strongly influences the shape of the IMF. High order CO transitions are also found to provide a good diagnostic for high far-UV intensity and low metallicity counterparts of Milky Way type systems both of which show some evidence for having top-heavy IMFs. We also compute the relative abundances of molecules known to be effective tracers of high density gas in these galaxy models. We find that the molecules CO and CS may be used to distinguish between solar and sub-solar metallicity in active galaxies at high redshift whereas HCN, HNC and CN are found to be relatively insensitive to the IMF shape at the large visual magnitudes typically associated with extragalactic sources.Comment: 26 Pages, 8 Figures, Accepted for publication in Ap

CoVault: A Secure Analytics Platform

In a secure analytics platform, data sources consent to the exclusive use oftheir data for a pre-defined set of analytics queries performed by a specificgroup of analysts, and for a limited period. If the platform is secure under asufficiently strong threat model, it can provide the missing link to enablingpowerful analytics of sensitive personal data, by alleviating data subjects'concerns about leakage and misuse of data. For instance, many types of powerfulanalytics that benefit public health, mobility, infrastructure, finance, orsustainable energy can be made differentially private, thus alleviatingconcerns about privacy. However, no platform currently exists that issufficiently secure to alleviate concerns about data leakage and misuse; as aresult, many types of analytics that would be in the interest of data subjectsand the public are not done. CoVault uses a new multi-party implementation offunctional encryption (FE) for secure analytics, which relies on a uniquecombination of secret sharing, multi-party secure computation (MPC), anddifferent trusted execution environments (TEEs). CoVault is secure under a verystrong threat model that tolerates compromise and side-channel attacks on anyone of a small set of parties and their TEEs. Despite the cost of MPC, we showthat CoVault scales to very large data sizes using map-reduce based queryparallelization. For example, we show that CoVault can perform queries relevantto epidemic analytics at scale.<br

Molecular Tracers of Filamentary CO Emission Regions Surrounding the Central Galaxies of Clusters

Optical emission is detected from filaments around the central galaxies of clusters of galaxies. These filaments have lengths of tens of kiloparsecs. The emission is possibly due to heating caused by the dissipation of mechanical energy and by cosmic ray induced ionisation. CO millimeter and submillimeter line emissions as well as H$_{2}$ infrared emission originating in such filaments surrounding NGC~1275, the central galaxy of the Perseus cluster, have been detected. Our aim is to identify those molecular species, other than CO, that may emit detectable millimeter and submillimeter line features arising in these filaments, and to determine which of those species will produce emissions that might serve as diagnostics of the dissipation and cosmic ray induced ionisation. The time-dependent UCL photon-dominated region modelling code was used in the construction of steady-state models of molecular filamentary emission regions at appropriate pressures, for a range of dissipation and cosmic ray induced ionisation rates and incident radiation fields.HCO$^+$ and C$_2$H emissions will potentially provide information about the cosmic ray induced ionisation rates in the filaments. HCN and, in particular, CN are species with millimeter and submillimeter lines that remain abundant in the warmest regions containing molecules. Detections of the galaxy cluster filaments in HCO$^{+}$, C$_{2}$H, and CN emissions and further detections of them in HCN emissions would provide significant constraints on the dissipation and cosmic ray induced ionisation rates.Comment: 11 pages, 3 figures, 3 tables, accepted in A&

Crossing probability and number of crossing clusters in off-critical percolation

We consider two-dimensional percolation in the scaling limit close to criticality and use integrable field theory to obtain universal predictions for the probability that at least one cluster crosses between opposite sides of a rectangle of sides much larger than the correlation length and for the mean number of such crossing clusters.Comment: 13 pages, 5 figures. Published version with references, appendix and comparison with numerics adde

Molecular line intensities as measures of cloud masses - II. Conversion factors for specific galaxy types

We present theoretically-established values of the CO-to-H2 and C-to-H2 conversion factors that may be used to estimate the gas masses of external galaxies. We consider four distinct galaxy types, represented by M51, NGC 6946, M82 and SMC N27. The physical parameters that best represent the conditions within the molecular clouds in each of the galaxy types are estimated using a chi^2 analysis of several observed atomic fine structure and CO rotational lines. This analysis is explored over a wide range of density, radiation field, extinction, and other relevant parameters. Using these estimated physical conditions in methods that we have previously established, CO-to-H2 conversion factors are then computed for CO transitions up to J=9-8. For the conventional CO(1-0) transition, the computed conversion factor varies significantly below and above the canonical value for the Milky Way in the four galaxy types considered. Since atomic carbon emission is now frequently used as a probe of external galaxies, we also present, for the first time, the C-to-H2 conversion factor for this emission in the four galaxy types considered.Comment: 14 pages, 11 figures, accepted for publication in MNRA