Engineering topics

Willis, Kevin

Publications and source records attributed to Willis, Kevin.

3 records

DOE OSTI2024

Detecting rare neutral atomic-carbon absorbers with a deep neural network

ABSTRACT C i absorbers play an important role as indicators for exploring the presence of cold gas in the interstellar medium of galaxies. However, the current data base of C i absorbers is very limited due to their weak absorption feature and rarity. Here, we report results from a search of C i λλ1560, 1656 absorption lines using Mg ii absorbers as signposts with modified deep learning algorithms, which provides a very quick way to search for weak C i absorber candidates. A total of 107 C i absorbers were detected, which nearly doubles the size of previously known samples. In addition, we found 17 C i absorbers to be associated with 2175 Å dust absorbers (2DAs), i.e. about 16 per cent C i absorbers are associated with 2DAs. Comparing the average dust depletion patterns of C i absorbers with those of damped Lyman α absorbers (DLAs), Mg ii absorbers, Ca ii absorbers, and 2175 Å dust absorbers (2DAs) shows that C i absorbers generally have environments with more dust than DLAs, Mg ii, and Ca ii absorbers, but similar to dust in 2DAs. Similarity between the dust depletion pattern of C i absorbers to that of the warm disc in the Milky Way indicates that C i absorption clouds are possibly associated with disc components in distant galaxies. Therefore, C i absorbers are confirmed to be excellent probes to trace cold gas and dust in the Universe.

Ge, Jian↗

DOE OSTI2022

Newly discovered Ca ii absorbers in the early Universe: statistics, element abundances, and dust

ABSTRACT We report discoveries of 165 new quasar Ca ii absorbers from the Sloan Digital Sky Survey (SDSS) Data Releases 7 and 12. Our ca ii rest-frame equivalent width distribution supports the weak and strong subpopulations, split at ${W}^{\lambda 3934}_{0}=0.7$ Å. Comparison of both populations’ dust depletion shows clear consistency for weak absorber association with halo-type gas in the Milky Way (MW), while strong absorbers have environments consistent with halo and disc-type gas. We probed our high-redshift Ca ii absorbers for 2175 Å dust bumps, discovering 12 2175 Å dust absorbers (2DAs). This clearly shows that some Ca ii absorbers follow the Large Magellanic Cloud (LMC) extinction law rather than the Small Magellanic Cloud extinction law. About 33 per cent of our strong Ca ii absorbers exhibit the 2175 Å dust bump, while only 6 per cent of weak Ca ii absorbers show this bump. 2DA detection further supports the theory that strong Ca ii absorbers are associated with disc components and are dustier than the weak population. Comparing average Ca ii absorber dust depletion patterns to that of Damped Ly α absorbers (DLAs), Mg ii absorbers, and 2DAs shows that Ca ii absorbers generally have environments with more dust than DLAs and Mg ii absorbers, but less dust than 2DAs. Comparing 2175 Å dust bump strengths from different samples and also the MW and LMC, the bump strength appears to grow stronger as the redshift decreases, indicating dust growth and the global chemical enrichment of galaxies in the Universe over time.

Astronomy & Astrophysics↗

DOE OSTI2022

Discovering Ca II absorption lines with a neural network

Quasar absorption line analysis is critical for studying gas and dust components and their physical and chemical properties as well as the evolution and formation of galaxies in the early universe. Calcium II (Ca II ) absorbers, which are one of the dustiest absorbers and are located at lower redshifts than most other absorbers, are especially valuable when studying physical processes and conditions in recent galaxies. However, the number of known quasar Ca II absorbers is relatively low due to the difficulty of detecting them with traditional methods. In this work, we developed an accurate and quick approach to search for Ca II absorption lines using deep learning. In our deep learning model, a convolutional neural network, tuned using simulated data, is used for the classification task. The simulated training data are generated by inserting artificial Ca II absorption lines into original quasar spectra from the Sloan Digital Sky Survey (SDSS), while an existing Ca II catalogue is adopted as the test set. The resulting model achieves an accuracy of 96 per cent on the real data in the test set. Our solution runs thousands of times faster than traditional methods, taking a fraction of a second to analyse thousands of quasars, while traditional methods may take days to weeks. The trained neural network is applied to quasar spectra from SDSS’s DR7 and DR12 and discovered 399 new quasar Ca II absorbers. In addition, we confirmed 409 known quasar Ca II absorbers identified previously by other research groups through traditional methods.

79 ASTRONOMY AND ASTROPHYSICS↗