Related papers: Ly$\alpha$NNA: A Deep Learning Field-level Inferen…
The Lyman-$\alpha$ absorption spectrum associated with photons traversing the intergalactic medium allows us to probe the linear matter power spectrum down to relatively small distance scales. Finding ways of accurately evaluating…
We introduce FLAME, a machine-learning algorithm designed to fit Voigt profiles to HI Lyman-alpha (Ly$\alpha$) absorption lines using deep convolutional neural networks. FLAME integrates two algorithms: the first determines the number of…
The back-shifted Fermi gas model is widely employed for calculating nuclear level density (NLD) as it can effectively reproduce experimental data by adjusting parameters. However, selecting parameters for nuclei lacking experimental data…
We study the distribution of low-redshift Ly$\alpha$ clouds in a CDM model using numerical simulations including photoionization and cooling of the baryonic component. The ionizing background is found to be efficient enough to keep most of…
One of the major science goals over the coming decade is to test fundamental physics with probes of the cosmic large-scale structure out to high redshift. Here we present a fully Bayesian approach to infer the three-dimensional cosmic…
Estimates of the metagalactic hydrogen ionization rate from the Lyman-alpha forest opacity in QSO absorption spectra depend on the complex density distribution of neutral hydrogen along the line-of-sight. We use a large suite of high…
We present the one-dimensional Lyman-$\alpha$ forest power spectrum measurement derived from the data release 1 (DR1) of the Dark Energy Spectroscopic Instrument (DESI). The measurement of the Lyman-$\alpha$ forest power spectrum along the…
In the past decade, deep neural networks (DNNs) came to the fore as the leading machine learning algorithms for a variety of tasks. Their raise was founded on market needs and engineering craftsmanship, the latter based more on trial and…
Since model bias and associated initialization shock are serious shortcomings that reduce prediction skills in state-of-the-art decadal climate prediction efforts, we pursue a complementary machine-learning-based approach to climate…
We develop, discuss, and compare several inference techniques to constrain theory parameters in collider experiments. By harnessing the latent-space structure of particle physics processes, we extract extra information from the simulator.…
We propose novel model transfer-learning methods that refine a decision forest model M learned within a "source" domain using a training set sampled from a "target" domain, assumed to be a variation of the source. We present two random…
We compare the low redshift (z ~ 0.1) Lyman-alpha forest from hydrodynamical simulations with data from the Cosmic Origin Spectrograph (COS). We find tension between the observed number of lines with b-parameters in the range 25-45 km/s and…
Recurrent neural networks (RNNs), especially long short-term memory (LSTM) RNNs, are effective network for sequential task like speech recognition. Deeper LSTM models perform well on large vocabulary continuous speech recognition, because…
The Aim of our study is to understand the variety of observed Lyman-alpha (Lya) line profiles and strengths in Lyman Break Galaxies (LBGs) and Lya emitters (LAEs), the physical parameters governing them, and hence deriving constraints on…
We present an analysis of Si III absorption and its effect on the 1D Ly$\alpha$ forest power spectrum using the Sherwood-Relics hydrodynamical simulation suite. In addition to oscillations from the Ly$\alpha$--Si III cross correlation that…
The significance of the Ly$\alpha$ emission line as a probe of the high-redshift Universe has long been established. Originating mainly in the vicinity of young, massive stars and in association with accretion of large bulks of matter, it…
Tree structured graphical models are powerful at expressing long range or hierarchical dependency among many variables, and have been widely applied in different areas of computer science and statistics. However, existing methods for…
The 1D Ly-$\alpha$ forest flux power spectrum $P_{\mathrm{1D}}$ is sensitive to scales smaller than a typical galaxy survey, and hence ties to the intergalactic medium's thermal state, suppression from neutrino masses and new dark matter…
We present a likelihood-free probabilistic inversion method based on normalizing flows for high-dimensional inverse problems. The proposed method is composed of two complementary networks: a summary network for data compression and an…
We introduce to astrophysics the threshold probability functions S_2, C_2, and D_2 first derived by \citet{torq+88}, which effectively samples the flux probability distribution (PDF) of the Lya forest at different spatial scales. These…