Direct T$_e$ metallicity calibration of R23 in strong line emitters

The gas metallicity of galaxies is often estimated using strong emission lines such as the optical lines of [OIII] and [OII]. The most common measure is R23, defined as ([OII]$lambda$$lambda$3726, 3729 + [OIII]$lambda$$lambda$4959,5007)/H$beta$. Most calibrations for these strong-line metallicity indicators are for continuum selected galaxies. We report a new empirical calibration of R23 for extreme emission-line galaxies using a large sample of about 800 star-forming green pea galaxies with reliable T$_e$-based gas-phase metallicity measurements. This sample is assembled from Sloan Digital Sky Survey (SDSS) Data Release 13 with the equivalent width of the line [OIII]$lambda$5007 $>$ 300 AA or the equivalent width of the line H$beta$ $>$ 100 AA in the redshift range 0.011 $<$ z $<$ 0.411. For galaxies with strong emission lines and large ionization parameter (which manifests as log [OIII]$lambda$$lambda$4959,5007/[OII]$lambda$$lambda$3726,3729 $geq$ 0.6), R23 monotonically increases with log(O/H) and the double-value degeneracy is broken. Our calibration provides metallicity estimates that are accurate to within $sim$ 0.14 dex in this regime. Many previous R23 calibrations are found to have bias and large scatter for extreme emission-line galaxies. We give formulae and plots to directly convert R23 and [OIII]$lambda$$lambda$4959,5007/[OII]$lambda$$lambda$3726,3729 to log(O/H). Since green peas are best nearby analogs of high-redshift Lyman-$alpha$ emitting galaxies, the new calibration offers a good way to estimate the metallicities of both extreme emission-line galaxies and high-redshift Lyman-$alpha$ emitting galaxies. We also report on 15 galaxies with metallicities less than 1/12 solar, with the lowest metallicities being 12+log(O/H) = 7.25 and 7.26.

New fully empirical calibrations of strong-line metallicity indicators in star forming galaxies

We derive new empirical calibrations for strong-line diagnostics of gas phase metallicity in local star forming galaxies by uniformly applying the Te method over the full metallicity range probed by the Sloan Digital Sky Survey (SDSS). To measure electron temperatures at high metallicity, where the auroral lines needed are not detected in single galaxies, we stacked spectra of more than 110,000 galaxies from the SDSS in bins of log[O II]/H$beta$ and log[O III]/H$beta$. This stacking scheme does not assume any dependence of metallicity on mass or star formation rate, but only that galaxies with the same line ratios have the same oxygen abundance. We provide calibrations which span more than 1 dex in metallicity and are entirely defined on a consistent absolute Te metallicity scale for galaxies. We apply our calibrations to the SDSS sample and find that they provide consistent metallicity estimates to within 0.05 dex.

Machine learning quantum states in the NISQ era

We review the development of generative modeling techniques in machine learning for the purpose of reconstructing real, noisy, many-qubit quantum states. Motivated by its interpretability and utility, we discuss in detail the theory of the restricted Boltzmann machine. We demonstrate its practical use for state reconstruction, starting from a classical thermal distribution of Ising spins, then moving systematically through increasingly complex pure and mixed quantum states. Intended for use on experimental noisy intermediate-scale quantum (NISQ) devices, we review recent efforts in reconstruction of a cold atom wavefunction. Finally, we discuss the outlook for future experimental state reconstruction using machine learning, in the NISQ era and beyond.

The GIGANTES dataset: precision cosmology from voids in the machine learning era

We present GIGANTES, the most extensive and realistic void catalog suite ever released -- containing over 1 billion cosmic voids covering a volume larger than the observable Universe, more than 20 TB of data, and created by running the void finder VIDE on QUIJOTEs halo simulations. The expansive and detailed GIGANTES suite, spanning thousands of cosmological models, opens up the study of voids, answering compelling questions: Do voids carry unique cosmological information? How is this information correlated with galaxy information? Leveraging the large number of voids in the GIGANTES suite, our Fisher constraints demonstrate voids contain additional information, critically tightening constraints on cosmological parameters. We use traditional void summary statistics (void size function, void density profile) and the void auto-correlation function, which independently yields an error of $0.13,mathrm{eV}$ on $sum,m_{ u}$ for a 1 $h^{-3}mathrm{Gpc}^3$ simulation, without CMB priors. Combining halos and voids we forecast an error of $0.09,mathrm{eV}$ from the same volume. Extrapolating to next generation multi-Gpc$^3$ surveys such as DESI, Euclid, SPHEREx, and the Roman Space Telescope, we expect voids should yield an independent determination of neutrino mass. Crucially, GIGANTES is the first void catalog suite expressly built for intensive machine learning exploration. We illustrate this by training a neural network to perform likelihood-free inference on the void size function. Cosmology problems provide an impetus to develop novel deep learning techniques, leveraging the symmetries embedded throughout the universe from physical laws, interpreting models, and accurately predicting errors. With GIGANTES, machine learning gains an impressive dataset, offering unique problems that will stimulate new techniques.

Cataloging the radio-sky with unsupervised machine learning: a new approach for the SKA era

We develop a new analysis approach towards identifying related radio components and their corresponding infrared host galaxy based on unsupervised machine learning methods. By exploiting PINK, a self-organising map algorithm, we are able to associate radio and infrared sources without the a priori requirement of training labels. We present an example of this method using $894,415$ images from the FIRST and WISE surveys centred towards positions described by the FIRST catalogue. We produce a set of catalogues that complement FIRST and describe 802,646 objects, including their radio components and their corresponding AllWISE infrared host galaxy. Using these data products we (i) demonstrate the ability to identify objects with rare and unique radio morphologies (e.g. X-shaped galaxies, hybrid FR-I/FR-II morphologies), (ii) can identify the potentially resolved radio components that are associated with a single infrared host and (iii) introduce a curliness statistic to search for bent and disturbed radio morphologies, and (iv) extract a set of 17 giant radio galaxies between 700-1100 kpc. As we require no training labels, our method can be applied to any radio-continuum survey, provided a sufficiently representative SOM can be trained.