arXiv subjects

Pablo Lemos

Publications and source records attributed to Pablo Lemos.

At least 37 records · Page 2Linked to original sources

arXiv2023

Bayesian Imaging for Radio Interferometry with Score-Based Priors

The inverse imaging task in radio interferometry is a key limiting factor to retrieving Bayesian uncertainties in radio astronomy in a computationally effective manner. We use a score-based prior derived from optical images of galaxies to recover images of protoplanetary disks from the DSHARP survey. We demonstrate that our method produces plausible posterior samples despite the misspecified galaxy prior. We show that our approach produces results which are competitive with existing radio interferometry imaging algorithms.

astro-ph.IM↗

arXiv2023

Learning an Effective Evolution Equation for Particle-Mesh Simulations Across Cosmologies

Particle-mesh simulations trade small-scale accuracy for speed compared to traditional, computationally expensive N-body codes in cosmological simulations. In this work, we show how a data-driven model could be used to learn an effective evolution equation for the particles, by correcting the errors of the particle-mesh potential incurred on small scales during simulations. We find that our learnt correction yields evolution equations that generalize well to new, unseen initial conditions and cosmologies. We further demonstrate that the resulting corrected maps can be used in a simulation-based inference framework to yield an unbiased inference of cosmological parameters. The model, a network implemented in Fourier space, is exclusively trained on the particle positions and velocities.

astro-ph.CO↗

arXiv2023

The search for the lost attractor

N-body systems characterized by inverse square attractive forces may display a self similar collapse known as the gravo-thermal catastrophe. In star clusters, collapse is halted by binary stars, and a large fraction of Milky Way clusters may have already reached this phase. It has been speculated -- with guidance from simulations -- that macroscopic variables such as central density and velocity dispersion are governed post-collapse by an effective, low-dimensional system of ODEs. It is still hard to distinguish chaotic, low dimensional motion, from high dimensional stochastic noise. Here we apply three machine learning tools to state-of-the-art dynamical simulations to constrain the post collapse dynamics: topological data analysis (TDA) on a lag embedding of the relevant time series, Sparse Identification of Nonlinear Dynamics (SINDY), and Tests of Accuracy with Random Points (TARP).

astro-ph.GA↗

arXiv2023

Causa prima: cosmology meets causal discovery for the first time

In astrophysics, experiments are impossible. We thus must rely exclusively on observational data. Other observational sciences increasingly leverage causal inference methods, but this is not yet the case in astrophysics. Here we attempt causal discovery for the first time to address an important open problem in astrophysics: the (co)evolution of supermassive black holes (SMBHs) and their host galaxies. We apply the Peter-Clark (PC) algorithm to a comprehensive catalog of galaxy properties to obtain a completed partially directed acyclic graph (CPDAG), representing a Markov equivalence class over directed acyclic graphs (DAGs). Central density and velocity dispersion are found to cause SMBH mass. We test the robustness of our analysis by random sub-sampling, recovering similar results. We also apply the Fast Causal Inference (FCI) algorithm to our dataset to relax the hypothesis of causal sufficiency, admitting unobserved confounds. Hierarchical SMBH assembly may provide a physical explanation for our findings.

astro-ph.GA↗

arXiv2023

Interpretable machine learning for finding intermediate-mass black holes

Definitive evidence that globular clusters (GCs) host intermediate-mass black holes (IMBHs) is elusive. Machine learning (ML) models trained on GC simulations can in principle predict IMBH host candidates based on observable features. This approach has two limitations: first, an accurate ML model is expected to be a black box due to complexity; second, despite our efforts to realistically simulate GCs, the simulation physics or initial conditions may fail to fully reflect reality. Therefore our training data may be biased, leading to a failure in generalization on observational data. Both the first issue -- explainability/interpretability -- and the second -- out of distribution generalization and fairness -- are active areas of research in ML. Here we employ techniques from these fields to address them: we use the anchors method to explain an XGBoost classifier; we also independently train a natively interpretable model using Certifiably Optimal RulE ListS (CORELS). The resulting model has a clear physical meaning, but loses some performance with respect to XGBoost. We evaluate potential candidates in real data based not only on classifier predictions but also on their similarity to the training data, measured by the likelihood of a kernel density estimation model. This measures the realism of our simulated data and mitigates the risk that our models may produce biased predictions by working in extrapolation. We apply our classifiers to real GCs, obtaining a predicted classification, a measure of the confidence of the prediction, an out-of-distribution flag, a local rule explaining the prediction of XGBoost and a global rule from CORELS.

astro-ph.GA↗

arXiv2023

${\rm S{\scriptsize IM}BIG}$: The First Cosmological Constraints from the Non-Linear Galaxy Bispectrum

We present the first cosmological constraints from analyzing higher-order galaxy clustering on non-linear scales. We use ${\rm S{\scriptsize IM}BIG}$, a forward modeling framework for galaxy clustering analyses that employs simulation-based inference to perform highly efficient cosmological inference using normalizing flows. It leverages the predictive power of high-fidelity simulations and robustly extracts cosmological information from regimes inaccessible with current standard analyses. In this work, we apply ${\rm S{\scriptsize IM}BIG}$ to a subset of the BOSS galaxy sample and analyze the redshift-space bispectrum monopole, $B_0(k_1, k_2, k_3)$, to $k_{\rm max}=0.5\,h/{\rm Mpc}$. We achieve 1$σ$ constraints of $Ω_m=0.293^{+0.027}_{-0.027}$ and $σ_8= 0.783^{+0.040}_{-0.038}$, which are more than 1.2 and 2.4$\times$ tighter than constraints from standard power spectrum analyses of the same dataset. We also derive 1.4, 1.4, 1.7$\times$ tighter constraints on $Ω_b$, $h$, $n_s$. This improvement comes from additional cosmological information in higher-order clustering on non-linear scales and, for $σ_8$, is equivalent to the gain expected from a standard analysis on a $\sim$4$\times$ larger galaxy sample. Even with our BOSS subsample, which only spans 10% of the full BOSS volume, we derive competitive constraints on the growth of structure: $S_8 = 0.774^{+0.056}_{-0.053}$. Our constraint is consistent with results from both cosmic microwave background and weak lensing. Combined with a $ω_b$ prior from Big Bang Nucleosynthesis, we also derive a constraint on $H_0=67.6^{+2.2}_{-1.8}\,{\rm km\,s^{-1}\,Mpc^{-1}}$ that is consistent with early universe constraints.

astro-ph.CO↗

arXiv2023

${\rm S{\scriptsize IM}BIG}$: The First Cosmological Constraints from Non-Gaussian and Non-Linear Galaxy Clustering

The 3D distribution of galaxies encodes detailed cosmological information on the expansion and growth history of the Universe. We present the first cosmological constraints that exploit non-Gaussian cosmological information on non-linear scales from galaxy clustering, inaccessible with current standard analyses. We analyze a subset of the BOSS galaxy survey using ${\rm S{\scriptsize IM}BIG}$, a new framework for cosmological inference that leverages high-fidelity simulations and deep generative models. We use two clustering statistics beyond the standard power spectrum: the bispectrum and a convolutional neural network based summary of the galaxy field. We infer constraints on $Λ$CDM parameters, $Ω_b$, $h$, $n_s$, $Ω_m$, and $σ_8$, that are 1.6, 1.5, 1.7, 1.2, and 2.3$\times$ tighter than power spectrum analyses. With this increased precision, we derive constraints on the Hubble constant, $H_0$, and $S_8 = σ_8 \sqrt{Ω_m/0.3}$ that are competitive with other cosmological probes, even with a sample that only spans 10% of the full BOSS volume. Our $H_0$ constraints, imposing the Big Bang Nucleosynthesis prior on the baryon density, are consistent with the early time constraints from the cosmic microwave background (CMB). Meanwhile, our $S_8$ constraints are consistent with weak lensing experiments and similarly lie below CMB constraints. Lastly, we present forecasts to show that future work extending ${\rm S{\scriptsize IM}BIG}$ to upcoming spectroscopic galaxy surveys (DESI, PFS, Euclid) will produce leading $H_0$ and $S_8$ constraints that bridge the gap between early and late time measurements and shed light on current cosmic tensions.

astro-ph.CO↗

arXiv2023

SimBIG: Field-level Simulation-Based Inference of Galaxy Clustering

We present the first simulation-based inference (SBI) of cosmological parameters from field-level analysis of galaxy clustering. Standard galaxy clustering analyses rely on analyzing summary statistics, such as the power spectrum, $P_\ell$, with analytic models based on perturbation theory. Consequently, they do not fully exploit the non-linear and non-Gaussian features of the galaxy distribution. To address these limitations, we use the {\sc SimBIG} forward modelling framework to perform SBI using normalizing flows. We apply SimBIG to a subset of the BOSS CMASS galaxy sample using a convolutional neural network with stochastic weight averaging to perform massive data compression of the galaxy field. We infer constraints on $Ω_m = 0.267^{+0.033}_{-0.029}$ and $σ_8=0.762^{+0.036}_{-0.035}$. While our constraints on $Ω_m$ are in-line with standard $P_\ell$ analyses, those on $σ_8$ are $2.65\times$ tighter. Our analysis also provides constraints on the Hubble constant $H_0=64.5 \pm 3.8 \ {\rm km / s / Mpc}$ from galaxy clustering alone. This higher constraining power comes from additional non-Gaussian cosmological information, inaccessible with $P_\ell$. We demonstrate the robustness of our analysis by showcasing our ability to infer unbiased cosmological constraints from a series of test simulations that are constructed using different forward models than the one used in our training dataset. This work not only presents competitive cosmological constraints but also introduces novel methods for leveraging additional cosmological information in upcoming galaxy surveys like DESI, PFS, and Euclid.

astro-ph.CO↗

arXiv2023

Towards equilibrium molecular conformation generation with GFlowNets

Sampling diverse, thermodynamically feasible molecular conformations plays a crucial role in predicting properties of a molecule. In this paper we propose to use GFlowNet for sampling conformations of small molecules from the Boltzmann distribution, as determined by the molecule's energy. The proposed approach can be used in combination with energy estimation methods of different fidelity and discovers a diverse set of low-energy conformations for highly flexible drug-like molecules. We demonstrate that GFlowNet can reproduce molecular potential energy surfaces by sampling proportionally to the Boltzmann distribution.

cs.LG↗

arXiv2023

The Cosmic Microwave Background and $H_0$

The cosmic microwave background (CMB) offers a unique window into the early universe, providing insights into cosmological parameters like the Hubble constant. Recent precise measurements of the CMB by experiments like Planck seem to point to a lower value for the Hubble constant compared to some other measurements like those from Type Ia supernovae. This discrepancy, known as the Hubble tension, currently lacks a definitive explanation. In this chapter, we provide an overview of how the Hubble constant is determined from detailed measurements of the CMB power spectrum. We explain the physics underlying key features of the CMB spectrum and their connection to cosmological parameters. We then examine the consistency of Planck's Hubble constant determination, both internally within the data itself and externally with other astrophysical probes. While largely consistent, some anomalies like the lensing amplitude parameter $A_L$ remain unresolved. We also explore various theoretical extensions to the standard $Λ$CDM cosmological model and assess their potential to resolve the Hubble tension. No clear resolution emerges, indicating significant tensions remain between early and late universe probes within simple extensions to $Λ$CDM. Upcoming CMB experiments promise improved precision and should provide further insights into this cosmic conundrum. A coherent picture bridging measurements across cosmic time remains an open challenge at the forefront of modern cosmology.

astro-ph.CO↗

arXiv2023

Sampling-Based Accuracy Testing of Posterior Estimators for General Inference

Parameter inference, i.e. inferring the posterior distribution of the parameters of a statistical model given some data, is a central problem to many scientific disciplines. Generative models can be used as an alternative to Markov Chain Monte Carlo methods for conducting posterior inference, both in likelihood-based and simulation-based problems. However, assessing the accuracy of posteriors encoded in generative models is not straightforward. In this paper, we introduce `Tests of Accuracy with Random Points' (TARP) coverage testing as a method to estimate coverage probabilities of generative posterior estimators. Our method differs from previously-existing coverage-based methods, which require posterior evaluations. We prove that our approach is necessary and sufficient to show that a posterior estimator is accurate. We demonstrate the method on a variety of synthetic examples, and show that TARP can be used to test the results of posterior inference analyses in high-dimensional spaces. We also show that our method can detect inaccurate inferences in cases where existing methods fail.

stat.ML↗

arXiv2023

A theory of continuous generative flow networks

Generative flow networks (GFlowNets) are amortized variational inference algorithms that are trained to sample from unnormalized target distributions over compositional objects. A key limitation of GFlowNets until this time has been that they are restricted to discrete spaces. We present a theory for generalized GFlowNets, which encompasses both existing discrete GFlowNets and ones with continuous or hybrid state spaces, and perform experiments with two goals in mind. First, we illustrate critical points of the theory and the importance of various assumptions. Second, we empirically demonstrate how observations about discrete GFlowNets transfer to the continuous case and show strong results compared to non-GFlowNet baselines on several previously studied tasks. This work greatly widens the perspectives for the application of GFlowNets in probabilistic inference and various modeling settings.

cs.LG↗

arXiv2023

CMB constraints on the early universe independent of late time cosmology

The CMB is a powerful probe of early-universe physics but is only observed after passing through large-scale structure, which changes the observed spectra in important model-dependent ways. This is of particular concern given recent claims of significant discrepancies with low redshift data sets when a standard $Λ$CDM model is assumed. By using empirical measurements of the CMB lensing reconstruction, combined with weak priors on the smoothness of the lensing spectrum, foregrounds, and shape of any additional integrated Sachs-Wolfe effect, we show how the early-universe parameters can be constrained from CMB observations almost independently of the late-time evolution. This provides a way to test new models for early-universe physics, and measure early-universe parameters, independently of late-time cosmology. Using the empirical measurement of lensing keeps the size of the effect of late-time modelling uncertainty under control, leading to only modest increases in error bars of most early-universe parameters compared to assuming a full evolution model. We provide robust constraints on early-$Λ$CDM model parameters using the latest Planck PR4 data and show that with future data marginalizing over a single lensing amplitude parameter is sufficient to remove sensitivity to late-time cosmological model only if the spectral shape matches predictions.

astro-ph.CO↗

arXiv2023

Posterior Sampling of the Initial Conditions of the Universe from Non-linear Large Scale Structures using Score-Based Generative Models

Reconstructing the initial conditions of the universe is a key problem in cosmology. Methods based on simulating the forward evolution of the universe have provided a way to infer initial conditions consistent with present-day observations. However, due to the high complexity of the inference problem, these methods either fail to sample a distribution of possible initial density fields or require significant approximations in the simulation model to be tractable, potentially leading to biased results. In this work, we propose the use of score-based generative models to sample realizations of the early universe given present-day observations. We infer the initial density field of full high-resolution dark matter N-body simulations from the present-day density field and verify the quality of produced samples compared to the ground truth based on summary statistics. The proposed method is capable of providing plausible realizations of the early universe density field from the initial conditions posterior distribution marginalized over cosmological parameters and can sample orders of magnitude faster than current state-of-the-art methods.

astro-ph.CO↗

arXiv2023

Robust Simulation-Based Inference in Cosmology with Bayesian Neural Networks

Simulation-based inference (SBI) is rapidly establishing itself as a standard machine learning technique for analyzing data in cosmological surveys. Despite continual improvements to the quality of density estimation by learned models, applications of such techniques to real data are entirely reliant on the generalization power of neural networks far outside the training distribution, which is mostly unconstrained. Due to the imperfections in scientist-created simulations, and the large computational expense of generating all possible parameter combinations, SBI methods in cosmology are vulnerable to such generalization issues. Here, we discuss the effects of both issues, and show how using a Bayesian neural network framework for training SBI can mitigate biases, and result in more reliable inference outside the training set. We introduce cosmoSWAG, the first application of Stochastic Weight Averaging to cosmology, and apply it to SBI trained for inference on the cosmic microwave background.

astro-ph.CO↗

arXiv2023

The impact of weak lensing on Type Ia supernovae luminosity distances

When Type Ia supernovae are used to infer cosmological parameters, their luminosities are compared to those from a homogeneous cosmology. In this note we propose a test to examine to what degree SN Ia have been observed on lines of sight where the average matter density is \textit{not} representative of the homogeneous background. We apply our test to the Pantheon SN Ia compilation, and find two redshift bins which indicate a moderate bias to over-density at $\sim 2σ$. We modify the Tripp estimator to explicitly de-lens SN Ia magnitudes, and show that this reduces scatter of Hubble diagram residuals. Using our revised Tripp estimator, the effect on cosmological parameters from Pantheon in $Λ$CDM is however small with a change in mean value from $Ω_{\rm m} = 0.317 \pm 0.027$ (baseline) to $Ω_{\rm m} = 0.312 \pm 0.025$ (de-lensed). For the Flat $w$CDM case it is $Ω_{\rm m} = 0.332 \pm 0.049$ and $w = -1.16 \pm 0.16$ (baseline) versus $Ω_{\rm m} = 0.316 \pm 0.048$ and $w = -1.12 \pm 0.15$ (de-lensed). We note that the effect of lensing on cosmological parameters may be larger for future high-z surveys.

astro-ph.CO↗

arXiv2022

The Cosmic Graph: Optimal Information Extraction from Large-Scale Structure using Catalogues

We present an implicit likelihood approach to quantifying cosmological information over discrete catalogue data, assembled as graphs. To do so, we explore cosmological parameter constraints using mock dark matter halo catalogues. We employ Information Maximising Neural Networks (IMNNs) to quantify Fisher information extraction as a function of graph representation. We a) demonstrate the high sensitivity of modular graph structure to the underlying cosmology in the noise-free limit, b) show that graph neural network summaries automatically combine mass and clustering information through comparisons to traditional statistics, c) demonstrate that networks can still extract information when catalogues are subject to noisy survey cuts, and d) illustrate how nonlinear IMNN summaries can be used as asymptotically optimal compressed statistics for Bayesian simulation-based inference. We reduce the area of joint $Ω_m, σ_8$ parameter constraints with small ($\sim$100 object) halo catalogues by a factor of 42 over the two-point correlation function, and demonstrate that the networks automatically combine mass and clustering information. This work utilises a new IMNN implementation over graph data in Jax, which can take advantage of either numerical or auto-differentiability. We also show that graph IMNNs successfully compress simulations away from the fiducial model at which the network is fitted, indicating a promising alternative to n-point statistics in catalogue simulation-based analyses.

astro-ph.CO↗

arXiv2022

Direct cosmological inference from three-dimensional correlations of the Lyman-$α$ forest

When performing cosmological inference, standard analyses of the Lyman-$α$ (Ly$α$) three-dimensional correlation functions only consider the information carried by the distinct peak produced by baryon acoustic oscillations (BAO). In this work, we address whether this compression is sufficient to capture all the relevant cosmological information carried by these functions. We do this by performing a direct fit to the full shape, including all physical scales without compression, of synthetic Ly$α$ auto-correlation functions and cross-correlations with quasars at effective redshift $z_{\rm{eff}}=2.3$, assuming a DESI-like survey, and providing a comparison to the classic method applied to the same dataset. Our approach leads to a $3.5\%$ constraint on the matter density $Ω_{\rm{M}}$, which is about three to four times better than what BAO alone can probe. The growth term $f σ_{8} (z_{\rm{eff}})$ is constrained to the $10\%$ level, and the spectral index $n_{\rm{s}}$ to $\sim 3-4\%$. We demonstrate that the extra information resulting from our `direct fit' approach, except for the $n_{\rm{s}}$ constraint, can be traced back to the Alcock-Paczyński effect and redshift space distortion information.

astro-ph.CO↗