1,721,167 research outputs found
Radio signal of axion-photon conversion in neutron stars: A ray tracing analysis
No full textAxion dark matter can resonantly convert into photons in the magnetospheres of neutron stars (NSs). It has recently been shown that radio observations of nearby NSs can therefore provide a highly sensitive probe of the axion parameter space. Here we extend existing calculations by performing the first three-dimensional computation of the photon flux, taking into account the isotropic phase-space distribution of axions and the structure of the NS magnetosphere. In particular, we study the overall magnitude of the flux and its possible time variation. We find that overall signal strength is robust to our more realistic analysis. In addition, we find that the variance of the signal with respect to the NS rotation is washed out by the additional trajectories in our treatment. Nevertheless, we show that SKA observations toward J0806.4-4123 are sensitive to gaγγ∼3×10-13 GeV-1 at ma∼7×10-6 eV, even when accounting for Doppler broadening. Finally, we provide the necessary code to calculate the photon flux for any given NS system https://github.com/mikaelLEROY/AxionNS_RayTracing
CMB bounds on dark matter annihilation: Nucleon energy losses after recombination
No full textWe consider the propagation and energy losses of protons and antiprotons produced by dark matter annihilation at redshifts 10
Differentiable strong lensing: Uniting gravity and neural nets through differentiable probabilistic programming
No full textSince upcoming telescopes will observe thousands of strong lensing systems, creating fully automated analysis pipelines for these images becomes increasingly important. In this work, we make a step towards that direction by developing the first end-to-end differentiable strong lensing pipeline. Our approach leverages and combines three important computer science developments: (i) convolutional neural networks (CNNs), (ii) efficient gradient-based sampling techniques, and (iii) deep probabilistic programming languages. The latter automatize parameter inference and enable the combination of generative deep neural networks and physics components in a single model. In the current work, we demonstrate that it is possible to combine a CNN trained on galaxy images as a source model with a fully differentiable and exact implementation of gravitational lensing physics in a single probabilistic model. This does away with hyperparameter tuning for the source model, enables the simultaneous optimization of nearly 100 source and lens parameters with gradient-based methods, and allows the use of efficient gradient-based posterior sampling techniques. These features make this automated inference pipeline potentially suitable for processing a large amount of data. By analysing mock lensing systems with different signal-to-noise ratios, we show that lensing parameters are reconstructed with per cent-level accuracy. More generally, we consider this work as one of the first steps in establishing differentiable probabilistic programming techniques in the particle astrophysics community, which have the potential to significantly accelerate and improve many complex data analysis tasks
Supersymmetric leptogenesis with a light hidden sector
No full textSupersymmetric scenarios incorporating thermal leptogenesis as the origin of the observed matter-antimatter asymmetry generically predict abundances of the primordial elements which are in conflict with observations. In this paper we propose a simple way to circumvent this tension and accommodate naturally thermal leptogenesis and primordial nucleosynthesis. We postulate the existence of a light hidden sector, coupled very weakly to the Minimal Supersymmetric Standard Model, which opens up new decay channels for the next-to-lightest supersymmetric particle, thus diluting its abundance during nucleosynthesis. We present a general model-independent analysis of this mechanism as well as two concrete realizations, and describe the relevant cosmological and astrophysical bounds and implications for this dark matter scenario. Possible experimental signatures at colliders and in cosmic-ray observations are also discussed
SICRET: Supernova Ia Cosmology with truncated marginal neural Ratio EsTimation
Full text linkType Ia supernovae (SNae Ia), standardisable candles that allow tracing the
expansion history of the Universe, are instrumental in constraining
cosmological parameters, particularly dark energy. State-of-the-art
likelihood-based analyses scale poorly to future large datasets, are limited to
simplified probabilistic descriptions, and must explicitly sample a
high-dimensional latent posterior to infer the few parameters of interest,
which makes them inefficient.
Marginal likelihood-free inference, on the other hand, is based on forward
simulations of data, and thus can fully account for complicated redshift
uncertainties, contamination from non-SN Ia sources, selection effects, and a
realistic instrumental model. All latent parameters, including instrumental and
survey-related ones, per-object and population-level properties, are implicitly
marginalised, while the cosmological parameters of interest are inferred
directly.
As a proof of concept, we apply truncated marginal neural ratio estimation
(TMNRE), a form of marginal likelihood-free inference, to BAHAMAS, a Bayesian
hierarchical model for SALT parameters. We verify that TMNRE produces unbiased
and precise posteriors for cosmological parameters from up to 100 000 SNae Ia.
With minimal additional effort, we train a network to infer simultaneously the
O(100 000) latent parameters of the supernovae (e.g. absolute brightnesses). In
addition, we describe and apply a procedure that utilises local amortisation of
the inference to convert the approximate Bayesian posteriors into frequentist
confidence regions with exact coverage. Finally, we discuss the planned
improvements to the model that are enabled by using a likelihood-free inference
framework, like selection effects and non-Ia contamination.Comment: 17 pages, 12 figures; This article has been accepted for publication
in MNRAS. (c): 2022 The Authors. Published by Oxford University Press on
behalf of the Royal Astronomical Society. All rights reserve
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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