Hiranya Peiris

The evolution of linear initial conditions present in the early Universe into extended halos of dark matter at late times can be computed using cosmological simulations. However, a theoretical understanding of this complex process remains elusive; in particular, the role of anisotropic information in the initial conditions in establishing the final mass of dark matter halos remains a long-standing puzzle. Here, we build a deep learning framework to investigate this question. We train a three-dimensional convolutional neural network to predict the mass of dark matter halos from the initial conditions, and quantify in full generality the amounts of information in the isotropic and anisotropic aspects of the initial density field about final halo masses. We find that anisotropies add a small, albeit statistically significant amount of information over that contained within spherical averages of the density field about final halo mass …

We present pop-cosmos: a comprehensive model characterizing the galaxy population, calibrated to galaxies from the Cosmic Evolution Survey (COSMOS) with photometry in bands from the ultra-violet to the infra-red. We construct a detailed forward model for the COSMOS data, comprising: a population model describing the joint distribution of galaxy characteristics and its evolution (parameterized by a flexible score-based diffusion model); a state-of-the-art stellar population synthesis (SPS) model connecting galaxies' instrinsic properties to their photometry; and a data-model for the observation, calibration and selection processes. By minimizing the optimal transport distance between synthetic and real data we are able to jointly fit the population- and data-models, leading to robustly calibrated population-level inferences that account for parameter degeneracies, photometric noise and calibration, and selection effects. We present a number of key predictions from our model of interest for cosmology and galaxy evolution, including the mass function and redshift distribution; the mass-metallicity-redshift and fundamental metallicity relations; the star-forming sequence; the relation between dust attenuation and stellar mass, star formation rate and attenuation-law index; and the relation between gas-ionization and star formation. Our model encodes a comprehensive picture of galaxy evolution that faithfully predicts galaxy colors across a broad redshift () and wavelength range.

We present a simple method for assessing the predictive performance of high-dimensional models directly in data space when only samples are available. Our approach is to compare the quantiles of observables predicted by a model to those of the observables themselves. In cases where the dimensionality of the observables is large (e.g. multiband galaxy photometry), we advocate that the comparison is made after projection onto a set of principal axes to reduce the dimensionality. We demonstrate our method on a series of two-dimensional examples. We then apply it to results from a state-of-the-art generative model for galaxy photometry (pop-cosmos) that generates predictions of colors and magnitudes by forward simulating from a 16-dimensional distribution of physical parameters represented by a score-based diffusion model. We validate the predictive performance of this model directly in a space of nine broadband colors. Although motivated by this specific example, the techniques we present will be broadly useful for evaluating the performance of flexible, non-parametric population models of this kind, and can be readily applied to any setting where two sets of samples are to be compared.

We use explainable neural networks to connect the evolutionary history of dark matter halos with their density profiles. The network captures independent factors of variation in the density profiles within a low-dimensional representation, which we physically interpret using mutual information. Without any prior knowledge of the halos’ evolution, the network recovers the known relation between the early time assembly and the inner profile and discovers that the profile beyond the virial radius is described by a single parameter capturing the most recent mass accretion rate. The results illustrate the potential for machine-assisted scientific discovery in complicated astrophysical datasets.

Ultracold atomic gases can undergo phase transitions that mimic relativistic vacuum decay, allowing us to empirically test early Universe physics in tabletop experiments. We investigate the physics of these analog systems, going beyond previous analyses of the classical equations of motion to study quantum fluctuations in the cold-atom false vacuum. We show that the fluctuation spectrum of this vacuum state agrees with the usual relativistic result in the regime where the classical analogy holds, providing further evidence for the suitability of these systems for studying vacuum decay. Using a suite of semiclassical lattice simulations, we simulate bubble nucleation from this analog vacuum state in a 1D homonuclear potassium-41 mixture, finding qualitative agreement with instanton predictions. We identify realistic parameters for this system that will allow us to study vacuum decay with current experimental …

We investigate the accuracy requirements for field-level inference of cluster and void masses using data from galaxy surveys. We introduce a two-step framework that takes advantage of the fact that cluster masses are determined by flows on larger scales than the clusters themselves. First, we determine the integration accuracy required to perform field-level inference of cosmic initial conditions on these large scales by fitting to late-time galaxy counts using the Bayesian Origin Reconstruction from Galaxies (BORG) algorithm. A 20-step COLA integrator is able to accurately describe the density field surrounding the most massive clusters in the local super-volume (), but does not by itself lead to converged virial mass estimates. Therefore, we carry out ‘posterior resimulations’, using full N-body dynamics while sampling from the inferred initial conditions, and thereby obtain estimates of masses for …

The 2017 detection of a kilonova coincident with gravitational-wave emission has identified neutron star mergers as the major source of the heaviest elements and dramatically constrained alternative theories of gravity. Observing a population of such sources has the potential to transform cosmology, nuclear physics, and astrophysics. However, with only one confident multi-messenger detection currently available, modelling the diversity of signals expected from such a population requires improved theoretical understanding. In particular, models that are quick to evaluate and are calibrated with more detailed multi-physics simulations are needed to design observational strategies for kilonovae detection and to obtain rapid-response interpretations of new observations. We use grey-opacity models to construct populations of kilonovae, spanning ejecta parameters predicted by numerical simulations. Our …

We construct an anti-halo void catalogue of voids with radii in the Local Super-Volume ( from the Milky Way), using posterior resimulation of initial conditions inferred by field-level inference with Bayesian Origin Reconstruction from Galaxies (\codefont{BORG}). We describe and make use of a new algorithm for creating a single, unified void catalogue by combining different samples from the posterior. The catalogue is complete out to , with void abundances matching theoretical predictions. Finally, we compute stacked density profiles of those voids which are reliably identified across posterior samples, and show that these are compatible with CDM expectations once environmental selection (e.g., the estimated under-density of the Local Super-Volume) is accounted for.

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will discover an unprecedented number of supernovae (SNe), making spectroscopic classification for all the events infeasible. LSST will thus rely on photometric classification, whose accuracy depends on the not-yet-finalized LSST observing strategy. In this work, we analyze the impact of cadence choices on classification performance using simulated multiband light curves. First, we simulate SNe with an LSST baseline cadence, a nonrolling cadence, and a presto-color cadence, which observes each sky location three times per night instead of twice. Each simulated data set includes a spectroscopically confirmed training set, which we augment to be representative of the test set as part of the classification pipeline. Then we use the photometric transient classification library snmachine to build classifiers. We find that the active region of the …

Gravitational-wave (GW) observations of neutron star-black hole (NSBH) mergers are sensitive to the nuclear equation of state (EOS). Using realistic simulations of NSBH mergers, incorporating both GW and electromagnetic (EM) selection to ensure sample purity, we find that a GW detector network operating at O5-sensitivities will constrain the radius of a NS and the maximum NS mass with and precision, respectively. The results demonstrate strong potential for insights into the nuclear EOS, provided NSBH systems are robustly identified.

We summarize the recent progress of the Axion Longitudinal Plasma Haloscope (ALPHA) Consortium, a new experimental collaboration to build a plasma haloscope to search for axions and dark photons. The plasma haloscope is a novel method for the detection of the resonant conversion of light dark matter to photons. ALPHA will be sensitive to QCD axions over almost a decade of parameter space, potentially discovering dark matter and resolving the strong C P problem. Unlike traditional cavity haloscopes, which are generally limited in volume by the Compton wavelength of the dark matter, plasma haloscopes use a wire metamaterial to create a tuneable artificial plasma frequency, decoupling the wavelength of light from the Compton wavelength and allowing for much stronger signals. We develop the theoretical foundations of plasma haloscopes and discuss recent experimental progress. Finally, we outline a …

Cold-atom analogue experiments are a promising new tool for studying relativistic vacuum decay, allowing us to empirically probe early-Universe theories in the laboratory. However, existing analogue proposals place stringent requirements on the atomic scattering lengths that are challenging to realize experimentally. Here we generalize these proposals and show that any stable mixture between two states of a bosonic isotope can be used as a relativistic analogue. This greatly expands the range of suitable experimental setups, and will thus expedite efforts to study vacuum decay with cold atoms.

We present a forward-modeling framework for estimating galaxy redshift distributions from photometric surveys. Our forward model is composed of: a detailed population model describing the intrinsic distribution of the physical characteristics of galaxies, encoding galaxy evolution physics; a stellar population synthesis model connecting the physical properties of galaxies to their photometry; a data model characterizing the observation and calibration processes for a given survey; and explicit treatment of selection cuts, both into the main analysis sample and for the subsequent sorting into tomographic redshift bins. This approach has the appeal that it does not rely on spectroscopic calibration data, provides explicit control over modeling assumptions and builds a direct bridge between photo-z inference and galaxy evolution physics. In addition to redshift distributions, forward modeling provides a framework for …

Next-generation surveys like the Legacy Survey of Space and Time (LSST) on the Vera C. Rubin Observatory (Rubin) will generate orders of magnitude more discoveries of transients and variable stars than previous surveys. To prepare for this data deluge, we developed the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC), a competition that aimed to catalyze the development of robust classifiers under LSST-like conditions of a nonrepresentative training set for a large photometric test set of imbalanced classes. Over 1000 teams participated in PLAsTiCC, which was hosted in the Kaggle data science competition platform between 2018 September 28 and 2018 December 17, ultimately identifying three winners in 2019 February. Participants produced classifiers employing a diverse set of machine-learning techniques including hybrid combinations and ensemble averages of a …

We present a Bayesian hierarchical framework to analyze photometric galaxy survey data with stellar population synthesis (SPS) models. Our method couples robust modeling of spectral energy distributions with a population model and a noise model to characterize the statistical properties of the galaxy populations and real observations, respectively. By self-consistently inferring all model parameters, from high-level hyperparameters to SPS parameters of individual galaxies, one can separate sources of bias and uncertainty in the data. We demonstrate the strengths and flexibility of this approach by deriving accurate photometric redshifts for a sample of spectroscopically confirmed galaxies in the COSMOS field, all with 26-band photometry and spectroscopic redshifts. We achieve a performance competitive with publicly released photometric redshift catalogs based on the same data. Prior to this work, this …

kilopop produces binary neutron star kilonovae in the grey-body approximation. It can also create populations of these objects useful for forecasting detection and testing observing scenarios. Additionally, it uses an emulator for the grey-opacity of the material calibrated against a suite of numerical radiation transport simulations with the code SuperNu (ascl: 2103.019).

False vacuum decay, a quantum mechanical first-order phase transition in scalar field theories, is an important phenomenon in early Universe cosmology. Recently, real-time semiclassical techniques based on ensembles of lattice simulations were applied to the problem of false vacuum decay. In this context, or any other lattice simulation, the effective potential experienced by long-wavelength modes is not the same as the bare potential. To make quantitative predictions using the real-time semiclassical techniques, it is therefore necessary to understand the redefinition of model parameters and the corresponding deformation of the vacuum state, as well as stochastic contributions that require modeling of unresolved subgrid modes. In this work, we focus on the former corrections and compute the expected modification of the true and false vacuum effective mass, which manifests as a modified dispersion …

We develop the use of mutual information (MI), a well-established metric in information theory, to interpret the inner workings of deep learning (DL) models. To accurately estimate MI from a finite number of samples, we present GMM-MI (pronounced'Jimmie'), an algorithm based on Gaussian mixture models that can be applied to both discrete and continuous settings. GMM-MI is computationally efficient, robust to the choice of hyperparameters and provides the uncertainty on the MI estimate due to the finite sample size. We extensively validate GMM-MI on toy data for which the ground truth MI is known, comparing its performance against established MI estimators. We then demonstrate the use of our MI estimator in the context of representation learning, working with synthetic data and physical datasets describing highly non-linear processes. We train DL models to encode high-dimensional data within a meaningful …

Strongly-lensed supernovae are rare and valuable probes of cosmology and astrophysics. Upcoming wide-field time-domain surveys, such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), are expected to discover an order-of-magnitude more lensed supernovae than have previously been observed. In this work, we investigate the cosmological prospects of lensed type Ia supernovae (SNIa) in LSST by quantifying the expected annual number of detections, the impact of stellar microlensing, follow-up feasibility, and how to best separate lensed and unlensed SNIa. We simulate SNIa lensed by galaxies, using the current LSST baseline v3.0 cadence, and find an expected number of 44 lensed SNIa detections per year. Microlensing effects by stars in the lens galaxy are predicted to lower the lensed SNIa detections by . The lensed events can be separated from the unlensed ones by jointly considering their colours and peak magnitudes. We define a `gold sample' of lensed SNIa per year with time delay days, detections before light-curve peak, and sufficiently bright ( mag) for follow-up observations. In three years of LSST operations, such a sample is expected to yield a measurement of the Hubble constant.

The cosmological information extracted from photometric surveys is most robust when multiple probes of the large scale structure of the Universe are used. Two of the most sensitive probes are the clustering of galaxies and the tangential shear of background galaxy shapes produced by those foreground galaxies, so-called galaxy-galaxy lensing. Combining the measurements of these two two-point functions leads to cosmological constraints that are independent of the way galaxies trace matter (the galaxy bias factor). The optimal choice of foreground, or lens, galaxies is governed by the joint, but conflicting requirements to obtain accurate redshift information and large statistics. We present cosmological results from the full 5000 deg 2 of the Dark Energy Survey’s first three years of observations (Y3) combining those two-point functions, using for the first time a magnitude-limited lens sample (MagLim) of 11 million …