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Register a new userIdentifying MgII Narrow Absorption Lines with Deep Learning
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Metal absorption line systems in distant quasar spectra probe of the history of gas content in the universe. The MgII $lambda lambda$ 2796, 2803 doublet is one of the most important absorption lines since it is a proxy of the star formation rate and a tracer of the cold gas associated with high redshift galaxies. Machine learning algorithms have been used to detect absorption lines systems in large sky surveys, such as Principle Component Analysis (PCA), Gaussian Process (GP) and decision trees. A very powerful algorithm in the field of machine learning called deep neural networks, or deep learning is a new structure of neural network that automatically extracts semantic features from raw data and represents them at a high level. In this paper, we apply a deep convolutional neural network for absorption line detection. We use the previously published DR7 MgII catalog (Zhu et al. 2013) as the training and validation sample and the DR12 MgII catalog as the test set. Our deep learning algorithm is capable of detecting MgII absorption lines with an accuracy of $sim$94% . It takes only $sim 9$ seconds to analyze $sim$ 50000 quasar spectra with our deep neural network, which is ten thousand times faster than traditional methods, while preserving high accuracy with little human interference. Our study shows that Mg II absorption line detection accuracy of a deep neutral network model strongly depends on the filter size in the filter layer of the neural network, and the best results are obtained when the filter size closely matches the absorption feature size.
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Aims. We have searched for temporal variations of narrow absorption lines in high resolution quasar spectra. A sample of 5 distant sources have been assembled, for which 2 spectra - VLT/UVES or Keck/HIRES - taken several years apart are available. Methods. We first investigate under which conditions variations in absorption line profiles can be detected reliably from high resolution spectra, and discuss the implications of changes in terms of small-scale structure within the intervening gas or intrinsic origin. The targets selected allow us to investigate the time behavior of a broad variety of absorption line systems, sampling diverse environments: the vicinity of active nuclei, galaxy halos, molecular-rich galaxy disks associated with damped Lya systems, as well as neutral gas within our own Galaxy. Results. Absorption lines from MgII, FeII or proxy species with lines of lower opacity tracing the same kind of gas appear to be remarkably stable (1 sigma upper limits as low as 10 % for some components on scales in the range 10 - 100 au), even for systems at z_abs ~ z_e. Marginal variations are observed for MgII lines toward PKS 1229-021 at z_abs = 0.83032; however, we detect no systems displaying changes as large as those reported in low resolution SDSS spectra. In neutral or diffuse molecular media, clear changes are seen for Galactic NaI lines toward PKS 1229-02 (decrease of N by a factor of four for one of the five components over 9.7 yr), corresponding to structure at a scale of about 35 au, in good agreement with known properties of the Galactic interstellar medium. Tentative variations are detected for H2 J=3 lines toward FBQS J2340-0053 at z_abs =2.05454 (~35% change in column density), suggesting the existence of structure at the 10 au-scale for this warm gas. A marginal change is also seen in CI from another velocity component (~70% variation in N(CI)).
We exploit the widely-separated images of the lensed quasar SDSS J1029+2623 ($z_{em}$=2.197, $theta =22^{primeprime}!!.5$) to observe its outflowing wind through two different sightlines. We present an analysis of three observations, including two with the Subaru telescope in 2010 February (Misawa et al. 2013) and 2014 April (Misawa et al. 2014), separated by 4 years, and one with the Very Large Telescope, separated from the second Subaru observation by $sim$2 months. We detect 66 narrow absorption lines (NALs), of which 24 are classified as intrinsic NALs that are physically associated with the quasar based on partial coverage analysis. The velocities of intrinsic NALs appear to cluster around values of $v_{ej}$ $sim$ 59,000, 43,000, and 29,000 km/s, which is reminiscent of filamentary structures obtained by numerical simulations. There are no common intrinsic NALs at the same redshift along the two sightlines, implying that the transverse size of the NAL absorbers should be smaller than the sightline distance between two lensed images. In addition to the NALs with large ejection velocities of $v_{ej}$ > 1,000 km/s, we also detect broader proximity absorption lines (PALs) at $z_{abs}$ $sim$ $z_{em}$. The PALs are likely to arise in outflowing gas at a distance of r $leq$ 620 pc from the central black hole with an electron density of $n_e$ $geq$ 8.7$times$10$^{3}$ cm$^{-3}$. These limits are based on the assumption that the variability of the lines is due to recombination. We discuss the implications of these results on the three-dimensional structure of the outflow.
Traditional classification for subclass of the Seyfert galaxies is visual inspection or using a quantity defined as a flux ratio between the Balmer line and forbidden line. One algorithm of deep learning is Convolution Neural Network (CNN) and has shown successful classification results. We building a 1-dimension CNN model to distinguish Seyfert 1.9 spectra from Seyfert 2 galaxies. We find our model can recognize Seyfert 1.9 and Seyfert 2 spectra with an accuracy over 80% and pick out an additional Seyfert 1.9 sample which was missed by visual inspection. We use the new Seyfert 1.9 sample to improve performance of our model and obtain a 91% precision of Seyfert 1.9. These results indicate our model can pick out Seyfert 1.9 spectra among Seyfert 2 spectra. We decompose H{alpha} emission line of our Seyfert 1.9 galaxies by fitting 2 Gaussian components and derive line width and flux. We find velocity distribution of broad H{alpha} component of the new Seyfert 1.9 sample has an extending tail toward the higher end and luminosity of the new Seyfert 1.9 sample is slightly weaker than the original Seyfert 1.9 sample. This result indicates that our model can pick out the sources that have relatively weak broad H{alpha} component. Besides, we check distributions of the host galaxy morphology of our Seyfert 1.9 samples and find the distribution of the host galaxy morphology is dominant by large bulge galaxy. In the end, we present an online catalog of 1297 Seyfert 1.9 galaxies with measurement of H{alpha} emission line.
Exoplanet detection with precise radial velocity (RV) observations is currently limited by spurious RV signals introduced by stellar activity. We show that machine learning techniques such as linear regression and neural networks can effectively remove the activity signals (due to starspots/faculae) from RV observations. Previous efforts focused on carefully filtering out activity signals in time using modeling techniques like Gaussian Process regression (e.g. Haywood et al. 2014). Instead, we systematically remove activity signals using only changes to the average shape of spectral lines, and no information about when the observations were collected. We trained our machine learning models on both simulated data (generated with the SOAP 2.0 software; Dumusque et al. 2014) and observations of the Sun from the HARPS-N Solar Telescope (Dumusque et al. 2015; Phillips et al. 2016; Collier Cameron et al. 2019). We find that these techniques can predict and remove stellar activity from both simulated data (improving RV scatter from 82 cm/s to 3 cm/s) and from more than 600 real observations taken nearly daily over three years with the HARPS-N Solar Telescope (improving the RV scatter from 1.47 m/s to 0.78 m/s, a factor of ~ 1.9 improvement). In the future, these or similar techniques could remove activity signals from observations of stars outside our solar system and eventually help detect habitable-zone Earth-mass exoplanets around Sun-like stars.
Observations at intermediate redshifts reveal the presence of numerous, compact, weak MgII absorbers with near to super-solar metallicities, often surrounded by more extended regions that produce CIV and/or OVI absorption in the circumgalactic medium at large impact parameters from luminous galaxies. Their origin and nature remains unclear. We hypothesize that undetected, satellite dwarf galaxies are responsible for producing some of these weak MgII absorbers. We test our hypothesis using gas dynamical simulations of galactic outflows from a dwarf satellite galaxy with a halo mass of $5times10^{9}$ M$_{odot}$, which could form in a larger $L^{*}$ halo at z=2, to study the gas interaction in the halo. We find that thin, filamentary, weak MgII absorbers are produced in two stages: 1) when shocked core collapse supernova (SNII) enriched gas descending in a galactic fountain gets shock compressed by upward flows driven by subsequent SNIIs and cools (phase 1), and later, 2) during an outflow driven by Type Ia supernovae that shocks and sweeps up pervasive SNII enriched gas, which then cools (phase 2). The width of the filaments and fragments are $lesssim~100$ pc, and the smallest ones cannot be resolved at 12.8 pc resolution. The MgII absorbers in our simulations are continuously generated for >150 Myr by shocks and cooling, though each cloud survives for only ~60 Myr. Their metallicity is 10-20% solar metallicity and column density is $<10^{12}$ cm$^{-2}$. They are also surrounded by larger (0.5-1 kpc) CIV absorbers that seem to survive longer. In addition, larger-scale (>1 kpc) CIV and OVI clouds are produced in both expanding and shocked SNII enriched gas which is photoionized by the UV metagalactic radiation at intermediate redshift. Our simulation highlights the possibility of dwarf galactic outflows producing highly enriched multiphase gas.
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