arXiv subjects

Xan Morice-Atkinson

Publications and source records attributed to Xan Morice-Atkinson.

7 recordsLinked to original sources

arXiv2025

A field-level reaction for screened modified gravity

We present a field-level reaction framework to emulate the nonlinear effects of screened modified gravity on the cosmic web. This approach is designed to enable field-level inference with data from Stage IV cosmological surveys. Building on the reaction method, which models the nonlinear matter power spectrum in modified gravity as corrections to a "pseudo" $\Lambda$CDM cosmology, we extend the method to full field-level predictions by applying it to the output of $N$-body simulations, including both positions and velocities. We focus on modifications to gravity that are scale-independent at the linear level, allowing us to isolate and emulate nonlinear deviations, particularly screening effects. Our neural network predicts the field-level correction ("reaction") to a pseudo$\Lambda$CDM simulation whose linear clustering matches that of the target. The emulator achieves sub-percent accuracy across a broad range of summary statistics, including 0.4\% agreement in the matter power spectrum at scales $k < 1$ Mpc$/h$, and 2\% accuracy in redshift-space distortion multipoles at $k < 0.3$ Mpc$/h$. We also validate the emulator against $N$-body simulations with increased force resolution and time steps, confirming the robustness of its performance. These results demonstrate that our framework is a practical and reliable tool for incorporating screened modified gravity models into field-level cosmological inference, enabling stringent tests of extra fundamental forces at cosmological scales.

astro-ph.CO↗

arXiv2025

LearningMatch: Siamese Neural Network Learns the Match Manifold

The match, which is defined as the the similarity between two waveform templates, is a fundamental calculation in computationally expensive gravitational-wave data-analysis pipelines, such as template bank generation. In this paper we introduce LearningMatch, a Siamese neural network that has learned the mapping between the parameters, specifically $\lambda_{0}$ (which is proportional to the chirp mass), $\eta$ (symmetric mass ratio), and equal aligned spin ($\chi_{1}$ = $\chi_{2}$), of two gravitational-wave templates and the match. The trained Siamese neural network, called LearningMatch, can predict the match to within $3.3\%$ of the actual match value. For match values greater than 0.95, a trained LearningMatch model can predict the match to within $1\%$ of the actual match value. LearningMatch can predict the match in 20 $\mu$s (mean maximum value) with Graphical Processing Units (GPUs). LearningMatch is 3 orders of magnitudes faster at determining the match than current standard mathematical calculations that involve the template being generated.

gr-qc↗

arXiv2024

A field-level emulator for modified gravity

Stage IV surveys like LSST and Euclid present a unique opportunity to shed light on the nature of dark energy. However, their full constraining power cannot be unlocked unless accurate predictions are available at all observable scales. Currently, only the linear regime is well understood in models beyond $\Lambda$CDM: on the nonlinear scales, expensive numerical simulations become necessary, whose direct use is impractical in the analyses of large datasets. Recently, machine learning techniques have shown the potential to break this impasse: by training emulators, we can predict complex data fields in a fraction of the time it takes to produce them. In this work, we present a field-level emulator capable of turning a $\Lambda$CDM N-body simulation into one evolved under $f(R)$ gravity. To achieve this, we build on the map2map neural network, using the strength of modified gravity $|f_{R_0}|$ as style parameter. We find that our emulator correctly estimates the changes it needs to apply to the positions and velocities of the input N-body particles to produce the target simulation. We test the performance of our network against several summary statistics, finding $1\%$ agreement in the power spectrum up to $k \sim 1$ $h/$Mpc, and $1.5\%$ agreement against the independent boost emulator eMantis. Although the algorithm is trained on fixed cosmological parameters, we find it can extrapolate to models it was not trained on. Coupled with available field-level emulators and simulation suites for $\Lambda$CDM, our algorithm can be used to constrain modified gravity in the large-scale structure using full information available at the field level.

astro-ph.CO↗

arXiv2024

Low-latency gravitational wave alert products and their performance at the time of the fourth LIGO-Virgo-KAGRA observing run

Multi-messenger searches for BNS and NSBH mergers are currently one of the most exciting areas of astronomy. The search for joint electromagnetic and neutrino counterparts to GWs has resumed with O4. To support this effort, public semi-automated data products are sent in near real-time and include localization and source properties to guide complementary observations. In preparation for O4, we have conducted a study using a simulated population of compact binaries and a MDC in the form of a real-time replay to optimize and profile the software infrastructure and scientific deliverables. End-to-end performance was tested, including data ingestion, running online search pipelines, performing annotations, and issuing alerts to the astrophysics community. We present an overview of the low-latency infrastructure and the performance of the data products that are now being released during O4 based on the MDC. We report the expected median latency for the preliminary alert of full bandwidth searches (29.5s) and show consistency and accuracy of released data products using the MDC. For the first time, we report the expected median latency for triggers from early warning searches (-3.1s), which are new in O4 and target neutron star mergers during inspiral phase. This paper provides a performance overview for LVK low-latency alert infrastructure and data products using the MDC and serves as a useful reference for the interpretation of O4 detections.

astro-ph.HE↗

arXiv2018

Candidate Massive Galaxies at $z \sim 4$ in the Dark Energy Survey

Using stellar population models, we predicted that the Dark Energy Survey (DES) - due to its special combination of area (5000 deg. sq.) and depth ($i = 24.3$) - would be in the position to detect massive ($\gtrsim 10^{11}$ M$_{\odot}$) galaxies at $z \sim 4$. We confront those theoretical calculations with the first $\sim 150$ deg. sq. of DES data reaching nominal depth. From a catalogue containing $\sim 5$ million sources, $\sim26000$ were found to have observed-frame $g-r$ vs $r-i$ colours within the locus predicted for $z \sim 4$ massive galaxies. We further removed contamination by stars and artefacts, obtaining 606 galaxies lining up by the model selection box. We obtained their photometric redshifts and physical properties by fitting model templates spanning a wide range of star formation histories, reddening and redshift. Key to constrain the models is the addition, to the optical DES bands $g$, $r$, $i$, $z$, and $Y$, of near-IR $J$, $H$, $K_{s}$ data from the Vista Hemisphere Survey. We further applied several quality cuts to the fitting results, including goodness of fit and a unimodal redshift probability distribution. We finally select 233 candidates whose photometric redshift probability distribution function peaks around $z\sim4$, have high stellar masses ($\log($M$^{*}$/M$_{\odot})\sim 11.7$ for a Salpeter IMF) and ages around 0.1 Gyr, i.e. formation redshift around 5. These properties match those of the progenitors of the most massive galaxies in the local universe. This is an ideal sample for spectroscopic follow-up to select the fraction of galaxies which is truly at high redshift. These initial results and those at the survey completion, which we shall push to higher redshifts, will set unprecedented constraints on galaxy formation, evolution, and the re-ionisation epoch.

astro-ph.GA↗

arXiv2017

Learning from the machine: interpreting machine learning algorithms for point- and extended- source classification

We investigate star-galaxy classification for astronomical surveys in the context of four methods enabling the interpretation of black-box machine learning systems. The first is outputting and exploring the decision boundaries as given by decision tree based methods, which enables the visualization of the classification categories. Secondly, we investigate how the Mutual Information based Transductive Feature Selection (MINT) algorithm can be used to perform feature pre-selection. If one would like to provide only a small number of input features to a machine learning classification algorithm, feature pre-selection provides a method to determine which of the many possible input properties should be selected. Third is the use of the tree-interpreter package to enable popular decision tree based ensemble methods to be opened, visualized, and understood. This is done by additional analysis of the tree based model, determining not only which features are important to the model, but how important a feature is for a particular classification given its value. Lastly, we use decision boundaries from the model to revise an already existing method of classification, essentially asking the tree based method where decision boundaries are best placed and defining a new classification method. We showcase these techniques by applying them to the problem of star-galaxy separation using data from the Sloan Digital Sky Survey (hereafter SDSS). We use the output of MINT and the ensemble methods to demonstrate how more complex decision boundaries improve star-galaxy classification accuracy over the standard SDSS frames approach (reducing misclassifications by up to $\approx33\%$). We then show how tree-interpreter can be used to explore how relevant each photometric feature is when making a classification on an object by object basis.

astro-ph.IM↗

arXiv2017

Core or cusps: The central dark matter profile of a redshift one strong lensing cluster with a bright central image

We report on SPT-CLJ2011-5228, a giant system of arcs created by a cluster at $z=1.06$. The arc system is notable for the presence of a bright central image. The source is a Lyman Break galaxy at $z_s=2.39$ and the mass enclosed within the 14 arc second radius Einstein ring is $10^{14.2}$ solar masses. We perform a full light profile reconstruction of the lensed images to precisely infer the parameters of the mass distribution. The brightness of the central image demands that the central total density profile of the lens be shallow. By fitting the dark matter as a generalized Navarro-Frenk-White profile---with a free parameter for the inner density slope---we find that the break radius is $270^{+48}_{-76}$ kpc, and that the inner density falls with radius to the power $-0.38\pm0.04$ at 68 percent confidence. Such a shallow profile is in strong tension with our understanding of relaxed cold dark matter halos; dark matter only simulations predict the inner density should fall as $r^{-1}$. The tension can be alleviated if this cluster is in fact a merger; a two halo model can also reconstruct the data, with both clumps (density going as $r^{-0.8}$ and $r^{-1.0}$) much more consistent with predictions from dark matter only simulations. At the resolution of our Dark Energy Survey imaging, we are unable to choose between these two models, but we make predictions for forthcoming Hubble Space Telescope imaging that will decisively distinguish between them.

astro-ph.CO↗