dataesr/scientific-paragraphs-categorization huggingface dataset:
The LICORICE code computes the full radiative transfer in the Lyman-α line without any substantial approximation. We find that the difference between homogeneous and inhomogeneous gas density and temperature is very small for the computed flux. On the contrary, neglecting the effect of gas velocities produces a significant change in the computed flux. We identify the causes (mainly Doppler shifts due to velocity gradients) and quantify the magnitude of the effect in both an idealised setup and a realistic cosmological situation. We find that the amplitude of the effect, up to a factor of ∼2 on the 21 cm signal power spectrum on some scales (depending on both other model parameters and the redshift), can be easily discriminated with an SKA-like survey and can already be approached, particularly for exotic signals, by the ongoing NenuFAR Cosmic Dawn Key Science Program.
A recent improvement  (Reis et al. 2021 ) modifies the shape of the kernel to include the effect of scatterings in wings of the Lyman-α line, although still assuming a homogeneous medium. The SPINTER code presented in this work follows the same approach. The Lyman-α coupling can also be computed through Monte Carlo radiative transfer simulations (see e. g. Semelin et al. 2007; Baek et al. 2009; Vonlanthen et al. 2011, using the LICORICE code). In this case P α , the number of scatterings per atom per second is directly evaluated. However, computing an average of 10 6 scatterings per photon and a sufficient number of photons (to control the sampling noise) reaching the core of the line in each resolution element at each desired output redshift is not computationally feasible yet. As a consequence photons are propagated (through an inhomogeneous medium) until they redshift into the core of the line and then a prescribed number of scatterings is tallied locally, considering that the spatial diffusion in the core of the line is negligible.
Validating the 1 H+div(u)/3 scaling ansatz As no complete analytical approach exists to describe the radiative transfer in the Lyman-α line in a non-homogeneously expanding medium, we turn to a Monte Carlo simulation to validate our ansatz. We use a simplified version of LICORICE, where the spatial dependence of physical fields is prescribed with analytical formulas, thus removing the need for grids and the difficulty of defining a spatial resolution. The Monte Carlo code computes the optical depth along the path of the photons taking both the Hubble expansion and the Doppler shifts from the gas velocity field into account. Thermal motion of atoms are included, scatterings are assumed isotropic in the rest frame of the atom, and atomic recoil is computed (but not the back-reaction from spin exchange).
The full radiative transfer with LICORICE The LICORICE code performs the full Monte Carlo 3D radiative transfer in the Lyman-α line. It is described in detail in  Semelin et al. (2007) ,  Baek et al. (2009)  and  Vonlanthen et al. (2011) , and has been used in a number of subsequent papers to compute the 21-cm signal during the CD (e. g  Semelin et al. 2017). Here, we give a few relevant features of the code and refer the reader to the above references for a complete description. The Lyman-α part of the code performs Monte Carlo ray-tracing on a uniform grid. Directions, frequencies, and target optical depths of photon packets are sampled from the relevant distributions. Source luminosities are implemented in such a way that they are not affected by the Monte Carlo sampling noise (i. e. photons are assigned to sources in a deterministic way, in proportion to their luminosity).
2009 ). Indeed, actually propagating all photons until they redshift out of the line would typically be a thousand times more expensive (in proportion to the number of computed scatterings), reaching the 10 7 CPU hour range for a grid with moderate resolution. The back-reaction from the gas on the local Lyman-α spectrum, due to the atomic recoil and spin exchange, is computed using the method described in  Hirata (2006). As the LICORICE code results serve in this work as a proxy for the ground truth, it is important to mention the limitations of the code. The most obvious one is the Monte Carlo sampling noise. This is even more the case than in situations where the photon packets can deposit a fraction of their content in each cell along their path, such as for radiative transfer of ionising photons or X-rays.
The semi-numerical approach with the SPINTER code The analytical formula for estimating the average Lymanα intensity in a homogeneous and isotropic universe and neglecting wing scatterings is described in  Furlanetto (2006). Santos et al. (2010)  and  Mesinger et al. (2011)  implemented a generalised version that accounts for a non-homogeneous emissivity field. The method to obtain the intensity field at a target redshift comes down to using a series of FFTs to convolve the emissivity field at higher redshifts with a Dirac-like spherical kernel whose radius depends on the emission and target redshifts. Reis et al. (2021)  improved on that by using a spherically symmetric kernel that accounts for wing scatterings in a homogeneous medium. SPINTER implements a similar approach. In the following sections, we describe a formal framework for using a kernel that includes wing scattering that, to our knowledge, has not been formulated analytically before and we provide some details on the SPINTER implementation.
Acknowledgements. Some of the numerical simulations in this work were performed using HPC resources from  GENCI-CINES  (grant  2018-A0050410557 ). LVEK acknowledges the financial support from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (Grant agreement No. 884760 , ' CoDEX '). RB acknowledges the  Israel Science Foundation  (grant No. 2359/20 ), the  Vera Rubin Presidential Chair in Astronomy  and the  Packard Foundation.
Acknowledgements The authors would like to thank the  MAMI accelerator group  for the excellent beam quality which made this experiment possible. This work is supported by the  Federal State of Rhineland-Palatinate , by the  Deutsche Forschungsgemeinschaft  with the  Collaborative Research Center  1044, by the  Slovenian Research Agency  under Grants  P1-0102  and  J1-4383  and by  Croatian Science Foundation  under the project  IP-2018-01-8570. The authors are grateful to the  Mainz Institute for   Theoretical Physics (MITP)  of the Cluster of Excellence PRISMA+ (Project  ID 39 , 083 ,149), for its hospitality and its partial support during the completion of this work.
2019; Graham et al. 2019; Dekany et al. 2020) Bright Transient Survey (BTS; Fremling et al. 2020; Perley et al. 2020 ) and analyse their optical light curves, spectra, hosts, and rates. Throughout this paper, we have compared the results derived from our sample to the ones in S13. This paper is organized as follows; we first discuss the sample selection criteria, the photometric and spectroscopic data collection in Section 2, then an analysis of the light curves and color curves and the bolometric luminosities is done in Section 3. 1. An analysis of early-and late-time spectra and emission-line identification is presented in Section 3. 2, and an analysis of the host galaxies is provided in Section 3. 3. The rates are estimated from the BTS survey in Section 3. 4. We end with a discussion about the nature of SN Ia-CSM progenitors and a summary in Sections 4 and 5, respectively.
We corrected all photometry for Milky Way extinction with the Python package extinction (Barbary 2016) using the dust extinction function from  Fitzpatrick (1999) , the  Schlafly & Finkbeiner (2011)  dust map, and an R V of 3. 1. Then we converted all measurements into flux units for analysis and considered anything less than a 3σ detection an upper limit. There is moderate to good coverage in the g, r, c, and o bands for all SNe in our sample. Figure  1  shows a multipaneled figure of the light curves of the objects in our sample.
Optical Spectroscopy The main instruments used for taking spectra and the software used to reduce the data are summarized in Table  2. Additionally, the spectrum Reguitti (2020) obtained using the Asiago Faint Object Spectrograph and Camera (AFOSC) on the 1. 8 m telescope at Cima Ekar, and the spectrum  Stein et al. (2018)  obtained using the ESO Faint Object Spectrograph and Camera version 2 (EFOSC2) on ESO's New Technology Telescope (NTT) were taken from TNS. The details for all optical spectra (61 for the sample in total) presented in this paper are provided in Table  3. Furthermore, all spectra were corrected for Milky Way extinction using extinction and the same procedure as for the photometry. The SN redshifts were derived using narrow host lines for objects which did not already have a host redshift available in the NASA/IPAC Extragalactic Database 24  (NED).
Hα, Hβ, and He I Emission Lines To analyse the Hα line emission, we first fit the continuum level using the fit_continuum function of the specutils Python package, where the continuum is estimated by a cubic function fitted to regions on each side of the line. We remove this continuum level and then fit the Hα line with a broad and a narrow-component Gaussian function using the fit_lines function of specutils, which returns the bestfit Gaussian model and the 1σ uncertainties of the model parameters. We generate 1000 sample models within the 1σ uncertainties of the parameters centered around the best-fit values and calculate the intensity, flux, and velocity (FWHM) of the broad and narrow components for each model. Then we take the median and standard deviation of the intensity, flux, and velocity FWHM distributions to get their final best values and 1σ uncertainties.
Host Galaxies We retrieved science-ready coadded images from the Galaxy Evolution Explorer (GALEX) general release 6/7  (Martin et al. 2005)   27 We used the software package Lambda Adaptive Multi-Band Deblending Algorithm in R (LAMBDAR;  Wright et al. 2016 ) and tools presented in  Schulze et al. (2021) , to measure the brightness of the host galaxy. The spectral energy distribution (SED) was modeled with the software package Prospector 28   (Johnson et al. 2021). We assumed a linear-exponential star formation history, the  Chabrier (2003)  initial mass function, the  Calzetti et al. (2000)  attenuation model, and the  Byler et al. (2017)  model for the ionized gas contribution. The priors were set as described in  Schulze et al. (2021). Figure  13  shows the log of star formation rate (SFR) as a function of stellar mass for the hosts of the BTS SNe Ia-CSM.
Based on observations obtained with the  Samuel Oschin Telescope  48 inch and the 60 inch Telescope at the  Palomar Observatory  as part of the ZTF project. ZTF is supported by the  National Science Foundation  under Grants No. AST-1440341  and  AST-2034437  and a collaboration including current partners  Caltech ,  IPAC , the  Weizmann Institute of Science , the  Oskar Klein Center at Stockholm University , the  University of Maryland ,  Deutsches Elektronen-Synchrotron and Humboldt University , the  TANGO Consortium of Taiwan , the  University of Wisconsin at Milwaukee ,  Trinity College Dublin ,  Lawrence Livermore National Laboratories, IN2P3 ,  University of Warwick ,  Ruhr University Bochum ,  Northwestern University  and former partners the  University of Washington ,  Los Alamos National Laboratories , and  Lawrence Berkeley National Laboratories.
Operations are conducted by  COO ,  IPAC , and  UW. The ZTF forced-photometry service was funded under the  Heising-Simons Foundation  grant # 12540303  (PI: Graham). This work was supported by the  GROWTH  project ( Kasliwal  et al. 2019) funded by the  National Science Foundation  under  PIRE  grant No. 1545949. The  Oskar Klein Centre  was funded by the  Swedish Research Council. Partially based on observations made with the  Nordic Optical Telescope , operated by the  Nordic Optical Telescope Scientific Association at the Observatorio del Roque de los Muchachos, La Palma, Spain , of the  Instituto de Astrofisica de Canarias. Some of the data presented here were obtained with ALFOSC. Some of the data presented herein were obtained at the  W. M. Keck Observatory , which is operated as a scientific partnership among the  California Institute of Technology , the  University of California , and  NASA ; the observatory was made possible by the generous financial support of the  W.
M. Keck Foundation. The SED Machine is based upon work supported by the  National Science Foundation  under grant No. 1106171. This work has made use of data from the  Asteroid Terrestrialimpact Last Alert System  (ATLAS) project. The  Asteroid Terrestrial-impact Last Alert System (ATLAS)  project is primarily funded to search for near earth asteroids through  NASA  grants  NN12AR55G ,  80NSSC18K0284 , and  80NSSC18K1575 ; byproducts of the NEO search include images and catalogs from the survey area. The ATLAS science products have been made possible through the contributions of the  University of Hawaii Institute for Astronomy , the  Queenʼs University Belfast , the  Space Telescope Science Institute , the  South African Astronomical Observatory , and  The Millennium Institute of Astrophysics (MAS), Chile.
This research has made use of the  NASA/IPAC Infrared Science Archive , which is funded by the  National Aeronautics and Space Administration  and operated by the  California Institute of Technology. The Liverpool Telescope is operated on the island of La Palma by  Liverpool John Moores University  in the  Spanish Observatorio del Roque de los Muchachos  of the  Instituto de Astrofisica de Canarias  with financial support from the  UK Science and Technology Facilities Council. Y. S. thanks the  LSSTC Data Science Fellowship Program , which is funded by  LSSTC ,  NSF Cybertraining  grant # 1829740 , the  Brinson Foundation , and the  Moore Foundation ; her participation in the program has benefited this work.
h Kast Double Spectrograph (Miller & Stone 1987). i Low Resolution Imaging Spectrometer (Oke et al. 1995). j IDL-based automatic reduction pipeline (https://sites. astro. caltech. edu/ ~dperley/programs/lpipe. html) (Perley 2019). k Spectrograph for the Rapid Acquisition of Transients (Piascik et al. 2014). l Dual Imaging Spectrograph. m Astrophysics Research Consortium telescope at the Apache Point Observatory. n https://github. com/jkrogager/PyNOT.
The present work is an extension of the study performed by Mojtabi  [9]  with various mixtures, from binary to n-component mixtures. An application with a ternary mixture was presented. The mixture of tetralin, isobutyl benzene, n-dodecane with respectively 0. 8-0. 1-0. 1 mass fractions was studied experimentally in weightlessness. The geometry is a rectangular cavity. An analytical solution was obtained using the parallel flow approximation. The parameters leading to the optimal species separation for tetralin and n-dodecane were calculated. The analytical results were corroborated by direct numerical simulations (Comsol Multiphysics software).
A non-machine-learning approach based on the StreakDet software was previously tested, but the results were not optimal for short and/or faint streaks. We set out to improve the capability to detect asteroid streaks in Euclid images by using deep learning. We built, trained, and tested a three-step machine-learning pipeline with simulated Euclid images. First, a convolutional neural network (CNN) detected streaks and their coordinates in full images, aiming to maximize the completeness (recall) of detections. Then, a recurrent neural network (RNN) merged snippets of long streaks detected in several parts by the CNN. Lastly, gradient-boosted trees (XGBoost) linked detected streaks between different Euclid exposures to reduce the number of false positives and improve the purity (precision) of the sample.
The deep-learning pipeline surpasses the completeness and reaches a similar level of purity of a non-machine-learning pipeline based on the StreakDet software. Additionally, the deep-learning pipeline can detect asteroids 0. 25-0. 5 magnitudes fainter than StreakDet. The deep-learning pipeline could result in a 50% increase in the number of detected asteroids compared to the StreakDet software. There is still scope for further refinement, particularly in improving the accuracy of streak coordinates and enhancing the completeness of the final stage of the pipeline, which involves linking detections across multiple exposures.
Rubin Observatory Legacy Survey of Space and Time (LSST), currently planned for early 2025. LSST will discover SSOs visible from the Southern Hemisphere to a limiting magnitude similar to Euclid. Pöntinen et al. (2020)  tested the streak-detection software StreakDet, developed by  Virtanen et al. (2016) , to detect SSOs in simulated Euclid images. Overall, the results were good, but there was room for improvement in detecting short (shorter than approximately 13 pixels, corresponding to 9 arcsec h -1 ) and/or faint (fainter than magnitude 23-24, depending on streak length) streaks. Nucita et al. (in prep. )  developed a complementary pipeline for short streaks. On the other hand,  Lieu et al. (2019)  studied the use of convolutional neural networks (CNNs) to find asteroids in Euclid images.
Simulated Euclid VIS images and preprocessing To make it easy to directly compare the performance of the deep-learning pipeline to that of StreakDet, we used the same simulated Euclid VIS data as  Pöntinen et al. (2020). The VIS data used by  Nucita et al. (in prep. )   same software and similar image properties, but it is not identical. We use only the VIS data for asteroid detection because VIS images have longer exposure times and thus reach fainter objects than NISP. The NISP data are valuable for asteroid science, but by using the VIS SSO detections, it should be possible to calculate where the detected SSOs are located in the NISP images without separate asteroid detection processes necessary for the NISP data. We use raw VIS images because they will be quickly available during the Euclid mission, which is essential for potential follow-up observations.
The following is a summary of the sections in  Pöntinen et al. (2020) and Nucita et al. (in prep. )  describing the generation and preprocessing of the simulated VIS image data. The Euclid Consortium has developed a Python software package named ELViS to generate realistic simulated Euclid VIS data  (Euclid Collaboration: Serrano et al. , in prep. ). The simulated images include stars, galaxies, Solar System objects, cosmic rays, and artifacts such as ghosts, diffraction spikes, charge transfer inefficiency (CTI), bleeding, Poisson noise, readout noise, and bias (Fig. 1 ). The simulated asteroid population was generated with uniformly distributed random apparent magnitudes between 20 and 26. The apparent velocities range from 1 to 80 arcsec h -1. These magnitude and velocity ranges cause many of the streaks to be practically invisible in the images.
Therefore, the essence of the dataset is not that all the streaks could be detected and completeness of 100% could be achieved, but rather the dataset is built for characterizing the detection envelope, that is, testing where the magnitude and velocity limits of detection are. Streak angles range from 0 • to 360 • (clockwise from east). The simulated streaks are symmetrical. Thus, within exposures, it is not possible to distinguish between their start and end points. However, the direction becomes apparent when analyzing several exposures. One CCD image contains approximately 25 asteroids on average. The simulated asteroid streaks do not follow realistic rate-of-motion distributions or position-angle distributions but are nevertheless reasonably well-suited for measuring the performance of the pipeline.
The simulated data consist of four dithered exposures, so the total number of CCD images per dataset is 144. The shorter VIS exposures taken alongside the NISP observations were not simulated. We worked with 11 datasets, ten of which focused on faster-moving SSOs (from 10 to 80 arcsec h -1 ) and one focused on slower ones (from 1 to 20 arcsec h -1 ). In total, our datasets have 1584 CCD images. The data are in the format of multiextension FITS files, with each quadrant in its own FITS extension. For easier data management and image analysis, we tiled the quadrants into single-extension FITS files before utilizing them in our pipeline. As a preprocessing step, we removed bright pixels caused by cosmic rays with Astro-SCRAPPY  (McCully et al. 2018; Van Dokkum 2001). We used the default parameters of A135, page 3 of 18 Astro-SCRAPPY, which worked well, and the program removed practically all cosmic rays.
Training and test sets for deep learning We developed software to generate different kinds of training and test datasets for the deep-learning models from the simulated Euclid images. The basic idea is to extract smaller sub-images or cutouts from the simulated CCDs (Fig. 2 ). The training labels for each training example are the classification label and, for positive examples, also coordinates for the endpoints of the streak visible in the cutout. For consistency during the training and inference stages of the deep-learning pipeline, the leftmost end of a streak is defined as the starting point and the rightmost end as the ending point. Negative examples are cutouts selected randomly from areas of the CCDs not containing asteroids. The size of the cutout images and the ratio between positive and negative training examples can be chosen by the user.
Ultimately, both loss functions achieved practically identical classification accuracies on our datasets. To optimize the hyperparameters of the CNN model, we ran hyperparameter sweeps using the online service Weights and Biases  (Biewald 2020). We tested ResNet models of different depths, as well as simpler CNN models. The sweeps were run on NVIDIA V100 and A100 graphics processing units (GPU) on the high-performance computing platform of the University of Helsinki. In addition, some smaller models were run on a local NVIDIA GeForce RTX 3060 GPU. After testing, the CNN models worked best for smaller images rather than larger ones. The classification accuracy was fairly uniform for images of varying sizes, but the streak coordinate accuracy was better for small images. Furthermore, using images with four channels (four exposures) did not offer an advantage over images with just one channel (one exposure), but were slower to train.
After the model has merged the streaks, the coordinates of the resulting streak are denormalized and moved back to the original coordinates of the streaklet cluster. The same coordinate shift is also done in the testing phase. Including the CNN output classification labels (i. e. , probabilities) in the RNN input data improves the RNN coordinate prediction accuracy by approximately 10%, compared to inputting only the CNN output coordinates. This is probably due to CNN labels helping RNN to recognize outliers, such as false-positive streaklets whose prediction label differs from those in the rest of the streaklet cluster. During the testing stage, streaklet clusters are created from the CNN output catalogs of the test set CCDs. Nearby streaklets located approximately along the same line are grouped, and the groups are analyzed with graph theory so that each streaklet belongs to only one group, similar to the training stage, except omitting the ground-truth coordinates.