Calculation of Critical Nucleation Rates by the Persistent Embryo Method: Application to Quasi Hard Sphere Models

We study crystal nucleation of the Weeks-Chandler-Andersen (WCA) model, using the recently introduced Persistent Embryo Method (PEM). The method provides detailed characterization of pre-critical, critical and post-critical nuclei, as well as nucleation rates that compare favorably with those obtained using other methods (umbrella sampling, forward flux sampling or seeding). We further map our results to a hard sphere model allowing to compare with other existing predictions. Implications for experiments are also discussed.Comment: 27 pages, 11 figure

Cosmological Constraints from the eBOSS Lyman-α\alpha Forest using the PRIYA Simulations

We present new cosmological parameter constraints from the eBOSS Lyman-$\alpha$ forest survey. We use a new theoretical model and likelihood based on the PRIYA simulation suite. PRIYA is the first suite to resolve the Lyman-$\alpha$ forest in a ($120$ Mpc/h)$^3$ volume, using a multi-fidelity emulation technique. We use PRIYA to predict Lyman-$\alpha$ forest observables with $\lesssim 1\%$ interpolation error over an $11$ dimensional ($9$ simulated, $2$ in post-processing) parameter space. We identify an internal tension within the flux power spectrum data. Once the discrepant data is removed, we find the scalar spectral index at $k = 0.78$ h/Mpc to be $n_P = 0.97 - 0.995$ at $68\%$ confidence from the Lyman-$\alpha$ forest flux power spectrum alone, in good agreement with Planck. The amplitude of matter fluctuations is $\sigma_8 = 0.733 \pm 0.026$ at $68\%$ confidence, in agreement with Dark Energy Survey weak lensing measurements and other small-scale structure probes and in tension with CMB measurements from Planck and ACT. The effective optical depth to Lyman-$\alpha$ photons from our pipeline is in good agreement with earlier measurements. We add measurements of the mean temperature of the intergalactic gas from $z=3.8 - 2.2$ and use them to constrain the duration and heating rate of helium reionization, finding a preference for an early, hot, helium reionization event, as suggested by measurements from the helium Lyman-$\alpha$ forest. Adding the mean IGM temperature data also increases the significance of the $\sigma_8$ tension. In the near future we will use our pipeline to infer cosmological parameters from the DESI Lyman-$\alpha$ data.Comment: 38 pages, 13 figures, submitted to JCA

Structural and Chemical Orders in Ni64.5Zr35.5 Metallic Glass by Molecular Dynamics Simulation

The atomic structure of Ni64.5Zr35.5 metallic glass has been investigated by molecular dynamics (MD) simulations. The calculated structure factors from the MD glassy sample at room temperature agree well with the X-ray diffraction (XRD) and neutron diffraction (ND) experimental data. Using the pairwise cluster alignment and clique analysis methods, we show that there are three types dominant short-range order (SRO) motifs around Ni atoms in the glass sample of Ni64.5Zr35.5, i.e., Mixed-Icosahedron(ICO)-Cube, Twined-Cube and icosahedron-like clusters. Furthermore, chemical order and medium-range order (MRO) analysis show that the Mixed-ICO-Cube and Twined-Cube clusters exhibit the characteristics of the crystalline B2 phase. Our simulation results suggest that the weak glass-forming ability (GFA) of Ni64.5Zr35.5 can be attributed to the competition between the glass forming ICO SRO and the crystalline Mixed-ICO-Cube and Twined-Cube motifs

Gaussian process machine learning-based surface extrapolation method for improvement of the edge effect in surface filtering

Filtering for signal and data is an important technology to reduce and/or remove noise signal for further extraction of desired information. However, it is well known that significant distortions may occur in the boundary areas of the filtered data because there is no sufficient data to be processed. This drawback largely affects the accuracy of topographic measurements and characterizations of precision freeform surfaces, such as freeform optics. To address this issue, a Gaussian process machine learning-based method is presented for extrapolation of the measured surface to an extended measurement area with high accuracy prior to filtering the surface. With the extrapolated data, the edge distortion can be effectively reduced. The effectiveness of this method was evaluated using both simulated and experimental data. Successful implementation of the proposed method not only addresses the issue in surface filtering but also provides a promising solution for numerous applications involving filtering processes

Machine Learning Uncovers the Universe's Hidden Gems: A Comprehensive Catalogue of CIV Absorption Lines in SDSS DR12

We assemble the largest CIV absorption line catalogue to date, leveraging machine learning, specifically Gaussian processes, to remove the need for visual inspection for detecting CIV absorbers. The catalogue contains probabilities classifying the reliability of the absorption system within a quasar spectrum. Our training set was a sub-sample of DR7 spectra that had no detectable CIV absorption in a large visually inspected catalogue. We used Bayesian model selection to decide between our continuum model and our absorption-line models. Using a random hold-out sample of 1301 spectra from all of the 26,030 investigated spectra in DR7 CIV catalogue, we validated our pipeline and obtained an 87% classification performance score. We found good purity and completeness values, both ~80%, when a probability of ~95% is used as the threshold. Our pipeline obtained similar CIV redshifts and rest equivalent widths to our training set. Applying our algorithm to 185,425 selected quasar spectra from SDSS DR12, we produce a catalogue of 113,775 CIV doublets with at least 95% confidence. Our catalogue provides maximum a posteriori values and credible intervals for CIV redshift, column density, and Doppler velocity dispersion. We detect CIV absorption systems with a redshift range of 1.37 $\!-\!$ 5.1, including 33 systems with a redshift larger than 5 and 549 absorbers systems with a rest equivalent width greater than 2 A at more than 95% confidence. Our catalogue can be used to investigate the physical properties of the circumgalactic and intergalactic media.Comment: 18 pages, 25 figures, 3 table

Effect of Samarium doping on the nucleation of fcc-Aluminum in undercooled liquids

The effect of Sm doping on the fcc-Al nucleation was investigated in Al-Sm liquids with low Sm concentrations (xSm) with molecular dynamics simulations. The nucleation in the moderately undercooled liquid is achieved by the recently developed persistent-embryo method. Systematically computing the nucleation rate with different xSm (xSm=0%, 1%, 2%, 3%, 5%) at 700 K, we found Sm dopant reduces the nucleation rate by up to 25 orders of magnitudes with only 5% doping concentration. This effect is mostly associated with the increase in the free energy barrier with a minor contribution from suppression of the attachment to the nucleus caused by Sm doping.Comment: 4 figure