相关论文: FLAME: Fitting Ly$\alpha$ Absorption lines using M…

We present a physically-based absorption-line model for the spectroscopic study of the intergalactic medium (IGM). This model adopts results from Cloudy simulations and theoretical calculations by Gnat and Sternberg (2007) to examine the…

We have employed deep neural network, or deep learning to predict the flux and the shape of the broad Ly$\alpha$ emission lines in the spectra of quasars. We use 17870 high signal-to-noise ratio (SNR > 15) quasar spectra from the Sloan…

We study the evolution with redshift of the number density of Ly$\alpha$ clouds in a CDM model using numerical simulations including photo-ionization and cooling of the baryonic component. The photo-ionizing flux is consistently taken as…

While model architectures and training strategies have become more generic and flexible with respect to different data modalities over the past years, a persistent limitation lies in the assumption of fixed quantities and arrangements of…

We measure the amount of absorption in the Lyman-alpha forest at 0 < z < 1.6 in HST FOS spectra of 74 QSOs. At 0 < z < 1.6 we find that 79% of the absorption is from the low density intergalactic medium, 12% from metals and 9% from the…

Correlations measured in three dimensions in the Lyman-alpha forest are contaminated by the presence of the damping wings of high column density (HCD) absorbing systems of neutral hydrogen (HI; having column densities $N(\mathrm{HI}) >…

Evaluation of the Voigt function, a convolution of a Lorentzian and a Gaussian profile, is essential in various fields such as spectroscopy, atmospheric science, and astrophysics. Efficient computation of the function is crucial, especially…

Over the last decade there has been an increasing frequency and intensity of wildfires across the globe, posing significant threats to human and animal lives, ecosystems, and socio-economic stability. Therefore urgent action is required to…

Fluorescence Lifetime Imaging (FLI) is a critical molecular imaging modality that provides unique information about the tissue microenvironment, which is invaluable for biomedical applications. FLI operates by acquiring and analyzing photon…

We propose and study a method called FLOT that estimates scene flow on point clouds. We start the design of FLOT by noticing that scene flow estimation on point clouds reduces to estimating a permutation matrix in a perfect world. Inspired…

This work presents a deep-learning approach to estimate atmospheric density profiles for use in planetary entry guidance problems. A long short-term memory (LSTM) neural network is trained to learn the mapping between measurements available…

Observations of the QSO HE 0226-4110 (zem = 0.495) with the Cosmic Origins Spectrograph (COS) from 1134 to 1796 {\AA} with a resolution of ~17 km s-1 and signal-to- noise (S/N) per resolution element of 20 to 40 are used to study the…

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 use FUSE and STIS spectra to study intergalactic absorption towards the quasar PG1259+593 (z=0.478). We identify 135 intergalactic absorption lines with equivalent widths >10mA, tracing 78 absorption components in 72 Ly alpha/beta…

Bloom filters are space-efficient probabilistic data structures that are used to test whether an element is a member of a set, and may return false positives. Recently, variations referred to as learned Bloom filters were developed that can…

We examine the Ly-alpha absorber population at z<0.3 detected in spectra of the QSOs PG0953+415 and H1821+643 taken with the Space Telescope Imaging Spectrograph aboard the Hubble Space Telescope. We compare their statistical properties to…

Bloom filters are widely used data structures that compactly represent sets of elements. Querying a Bloom filter reveals if an element is not included in the underlying set or is included with a certain error rate. This membership testing…

Vision transformers (ViT) have been shown to allow for more flexible feature detection and can outperform convolutional neural network (CNN) when pre-trained on sufficient data. Due to their promising feature detection capabilities, we…

We present the methodology for, and the first results from, a new imaging program aimed at identifying and characterizing the host galaxies of damped Lyman-alpha absorbers (DLAs) at z>2. We target quasar sightlines with multiple…

Non-intrusive load monitoring (NILM) aims at energy consumption and appliance state information retrieval from aggregated consumption measurements, with the help of signal processing and machine learning algorithms. Representation learning…