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Register a new userUrchin: A Reverse Ray Tracer for Astrophysical Applications
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We describe URCHIN, a reverse ray tracing radiative transfer scheme optimised to model self-shielding from the post-reionisation ultraviolet (UV) background in cosmological simulations. The reverse ray tracing strategy provides several benefits over forward ray tracing codes including: (1) the preservation of adaptive density field resolution (2) completely uniform sampling of gas elements by rays; (3) the preservation of galilean invariance; (4) the ability to sample the UV background spectrum with hundreds of frequency bins; and (5) exact preservation of the input UV background spectrum and amplitude in optically thin gas. The implementation described here focuses on Smoothed Particle Hydrodynamics (SPH). However, the method can be applied to any density field representation in which resolution elements admit ray intersection tests and can be associated with optical depths. We characterise the errors in our implementation in stages beginning with comparison to known analytic solutions and ending with a realistic model of the z = 3 cosmological UV background incident onto a suite of spherically symmetric models of gaseous galactic halos.
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Electron-ion Bremsstrahlung (free-free) emission and absorption occur in many astrophysical plasmas for a wide range of physical conditions. This classical problem has been studied multiple times, and many analytical and numerical approximations exist. However, accurate calculations of the transition from the non-relativistic to the relativistic regime remain sparse. Here we provide a comprehensive study of the free-free Gaunt factors for ions with low charge (Z<=10). We compute the Gaunt factor using the expressions for the differential cross section given by Elwert & Haug (EH) and compare to various limiting cases. We develop a new software package, BRpack, for direct numerical applications. This package uses a combination of pre-computed tables and analytical approximations to efficiently cover a wide range of electron and photon energies, providing a representation of the EH Gaunt factor to better than 0.03% precision for Z<=2. Our results are compared to those of previous studies highlighting the improvements achieved here. BRpack should be useful in computations of spectral distortions of the cosmic microwave background, radiative transfer problems during reionization or inside galaxy clusters, and the modeling of galactic free-free foregrounds. The developed computational methods can furthermore be extended to higher energies and ion charge.
A long-standing problem of astrophysical research is how to simultaneously obtain spectra of thousands of sources randomly positioned in the field of view of a telescope. Digital Micromirror Devices, used as optical switches, provide a most powerful solution allowing to design a new generation of instruments with unprecedented capabilities. We illustrate the key factors (opto-mechanical, cryo-thermal, cosmic radiation environment,...) that constrain the design of DMD-based multi-object spectrographs, with particular emphasis on the IR spectroscopic channel onboard the EUCLID mission, currently considered by the European Space Agency for a 2017 launch date.
One of the most computationally demanding aspects of the hydrodynamical modelling of Astrophysical phenomena is the transport of energy by radiation or relativistic particles. Physical processes involving energy transport are ubiquitous and of capital importance in many scenarios ranging from planet formation to cosmic structure evolution, including explosive events like core collapse supernova or gamma-ray bursts. Moreover, the ability to model and hence understand these processes has often been limited by the approximations and incompleteness in the treatment of radiation and relativistic particles. The DIAPHANE project has focused in developing a portable and scalable library that handles the transport of radiation and particles (in particular neutrinos) independently of the underlying hydrodynamic code. In this work, we present the computational framework and the functionalities of the first version of the DIAPHANE library, which has been successfully ported to three different smoothed-particle hydrodynamic codes, GADGET2, GASOLINE and SPHYNX. We also present validation of different modules solving the equations of radiation and neutrino transport using different numerical schemes.
Two of the most rapidly growing observables in cosmology and astrophysics are gravitational waves (GW) and the neutral hydrogen (HI) distribution. In this work, we investigate the cross-correlation between resolved gravitational wave detections and HI signal from intensity mapping (IM) experiments. By using a tomographic approach with angular power spectra, including all projection effects, we explore possible applications of the combination of the Einstein Telescope and the SKAO intensity mapping surveys. We focus on three main topics: textit{(i)} statistical inference of the observed redshift distribution of GWs; textit{(ii)} constraints on dynamical dark energy models as an example of cosmological studies; textit{(iii)} determination of the nature of the progenitors of merging binary black holes, distinguishing between primordial and astrophysical origin. Our results show that: textit{(i)} the GW redshift distribution can be calibrated with good accuracy at low redshifts, without any assumptions on cosmology or astrophysics, potentially providing a way to probe astrophysical and cosmological models; textit{(ii)} the constrains on the dynamical dark energy parameters are competitive with IM-only experiments, in a complementary way and potentially with less systematics; textit{(iii)} it will be possible to detect a relatively small abundance of primordial black holes within the gravitational waves from resolved mergers. Our results extend towards $mathrm{GW times IM}$ the promising field of multi-tracing cosmology and astrophysics, which has the major advantage of allowing scientific investigations in ways that would not be possible by looking at single observables separately.
In high stakes applications such as healthcare and finance analytics, the interpretability of predictive models is required and necessary for domain practitioners to trust the predictions. Traditional machine learning models, e.g., logistic regression (LR), are easy to interpret in nature. However, many of these models aggregate time-series data without considering the temporal correlations and variations. Therefore, their performance cannot match up to recurrent neural network (RNN) based models, which are nonetheless difficult to interpret. In this paper, we propose a general framework TRACER to facilitate accurate and interpretable predictions, with a novel model TITV devised for healthcare analytics and other high stakes applications such as financial investment and risk management. Different from LR and other existing RNN-based models, TITV is designed to capture both the time-invariant and the time-variant feature importance using a feature-wise transformation subnetwork and a self-attention subnetwork, for the feature influence shared over the entire time series and the time-related importance respectively. Healthcare analytics is adopted as a driving use case, and we note that the proposed TRACER is also applicable to other domains, e.g., fintech. We evaluate the accuracy of TRACER extensively in two real-world hospital datasets, and our doctors/clinicians further validate the interpretability of TRACER in both the patient level and the feature level. Besides, TRACER is also validated in a high stakes financial application and a critical temperature forecasting application. The experimental results confirm that TRACER facilitates both accurate and interpretable analytics for high stakes applications.
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