Astrophysics

Future surveys will map the cosmic microwave background (CMB) with unprecedented precision. The high fidelity of the data will present new opportunities to extract deep insights about the history, contents, and evolution of our universe.  However, new tools and techniques will be required to maximize the potential of the forthcoming data.  I will describe the techniques necessary to address the emerging challenges and to harness the exciting opportunities provided by future CMB observations.

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Zoom link https://pitp.zoom.us/j/92999899446?pwd=YTNEUkMrV2RXOWE0Mk11b2tDQ2Q0Zz09

Abstract TBA

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Zoom link https://pitp.zoom.us/j/95952209469?pwd=TERqVlZsMVhvbzJqU1hsalhiUVYxdz09

Deep learning (DL) methods have demonstrated great potential for extracting rich non-linear information from cosmological fields, a challenge that traditional summary statistics struggle to address. Most of these DL methods are discriminative models, i.e., they directly learn the posterior constraints of cosmological parameters. In this talk, I will make the argument that learning the field-level likelihood function using generative modeling approaches such as Normalizing Flows usually leads to more effective extraction of cosmological information. This approach also enables anomaly detection to improve the robustness of the analysis. To scale the modeling to high dimensional data and improve its generalization capabilities, we further incorporate physical inductive biases, such as symmetries and multiscale structure, into the architecture of the normalizing flow models. On mock weak lensing maps, I will show that the model leads to significant improvement in constraining power compared to power spectrum and alternative DL models. I will also show that it is able to detect domain shifts between training simulations and test data, such as noise miscalibration and baryonic effect, which, if left unaddressed, could introduce systematic biases in parameter constraints. Finally, I will talk about our ongoing work on applying this model to the field-level cosmic shear analysis for HSC.

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Zoom link https://pitp.zoom.us/j/97478701784?pwd=UVg4TVp1WFArcXNETG5ITGd0S0NuZz09

Abstract TBA

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Zoom link https://pitp.zoom.us/j/96982969023?pwd=MjgzaU5mZUVuV3pNVG11dHM2WUd4UT09

Major improvements in the theoretical aspects of Cosmology have been possible in recent years due to QFT-inspired methods, such as the effective field theory of large-scale structure (EFTofLSS). In this talk, I will explore further connections between high-energy physics and cosmology. I will present a systematic approach to renormalizing the galaxy bias parameters using path integrals and a finite cutoff scale Λ. I will derive the differential equations of the Wilson-Polchinski renormalization group that describe the evolution of the finite-scale bias parameters with Λ, analogous to the β-function running in QFT. I will then discuss how the RG-flow of EFTofLSS can lead to improvements in the extraction of cosmological parameters and also serve as a tool for sanity checks.

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Zoom link https://pitp.zoom.us/j/93248279658?pwd=Z1M1UnI3eVh3NTZ3NCt4NUtGaW5idz09

The current ΛCDM concordance model has been widely successful in describing our Universe. However, crucial questions, such as the H0 tension, remain unanswered and are becoming increasingly critical with the continuous release of high-precision cosmological data. This has led to the exploration of modified ΛCDM models, one of them being the coupled quintessence, or Coupled Dark Energy (CDE) model.  Here, we perform for the first time a tomographic analysis of coupled dark energy, where the coupling strength is parametrised and constrained in different redshift bins. We employ cosmic microwave background data from Planck, ACT and SPT, showing the impact of different choices that can be made in combining these datasets. Then, we use a range of low redshift probes to test CDE cosmologies, both for a constant and a tomographic coupling. In particular, we use for the first time data from weak lensing, galaxy clustering, and 3x2pt galaxy-galaxy lensing cross-correlation data. For CMB and background datasets, a tomographic coupling allows for β values up to one order of magnitude larger than in previous works, in particular at z < 1. The use of 3x2pt analysis then becomes important to constrain β at low redshifts, even when coupling is allowed to vary: for 3x2pt we find, at 0.5 < z < 1, β = 0.018+0.007 −0.011, comparable to what CMB and background datasets would give for a constant coupling. This makes upcoming galaxy surveys potentially powerful probes to test CDE models at low redshifts. 

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Zoom link https://pitp.zoom.us/j/94442666279?pwd=OTgrMTZ5dTRzZmc2WFhuMkF3ekJzdz09

Dark matter interactions with Standard Model particles can inject energy at early times, altering the standard evolution of the early universe. In particular, this energy injection can perturb the spectrum of the cosmic microwave background (CMB) away from that of a perfect blackbody, alter the CMB anisotropy spectrum, and affect processes by which the first stars form. For this study, I will discuss recent work to upgrade the DarkHistory code package to more carefully track interactions among low energy electrons, hydrogen atoms, and radiation, in order to accurately compute the evolution of the CMB spectral distortion in the presence of Dark Matter energy injection. I will show results for the contribution to the spectral distortions from redshifts z < 3000 for arbitrary energy injection scenarios, new CMB anisotropy constraints on light dark matter, as well as the effect of exotic energy injection on early star formation.

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Zoom link https://pitp.zoom.us/j/99559611185?pwd=bDFVdmpyVE5CbXVXVHdEL29Md0FXUT09

Inflation remains one of the enigmas in fundamental physics. While it is difficult to distinguish different inflation models, information contained in primordial non-Gaussianity (PNG) offers a route to break the degeneracy. In galaxy surveys, the local type PNG is usually probed by measuring the scale-dependent bias in the galaxy power spectrum on large scales, where cosmic variance and systematics are also large. Other types of PNG need bispectrum, which is computationally challenging and is contaminated by gravity. I will introduce a new approach to measuring PNG by using the reconstructed density field, a density field reversed to the initial conditions from late time. With the reconstructed density field, we can fit a new template at the field level, or compute a near optimal bispectrum estimator, to constrain PNG. By reconstructing the initial conditions, we remove the nonlinearity induced by gravity, which is a source of confusion when measuring PNG. Near optimal bispectrum estimator mitigates computational challenges. This new approach shows strong constraining power, offers an alternative way to the existing method with different systematics, and also follows organically the procedure of baryon acoustic oscillation (BAO) analysis in large galaxy surveys. I will present a reconstruction method using convolutional neural networks that significantly improves the performance of traditional reconstruction algorithms in the matter density field, which is crucial for more effectively probing PNG. This pipeline can enable new observational constraints on PNG from the ongoing Dark Energy Spectroscopic Instrument (DESI) and Euclid surveys, as well as from upcoming surveys, such as that of the Nancy Grace Roman Space Telescope.

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Zoom link https://pitp.zoom.us/j/92361466496?pwd=ZlljUGlKaTVlSFZIV21NUHNGY2RRUT09

The deep learning architecture associated with ChatGPT and related generative AI products is known as transformers. Initially applied to Natural Language Processing, transformers and the self-attention mechanism they exploit have gained widespread interest across the natural sciences. We will present the mathematics underlying the attention mechanism and describe the basic transformer architecture. We will then describe applications to time series and imaging data in astronomy and discuss possible foundation models.

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Zoom link https://pitp.zoom.us/j/91226066758?pwd=TWZ5RVliMjVKYXdLcHdya09lNWZhQT09

Black hole thermodynamics plays a central role in the theoretical landscape, acting as both synthesizer and sieve for concepts in field theory, quantum gravity, information theory, and more. While its formulation and applications are well understood in asymptotically flat and anti-de Sitter spaces, significant difficulties arise when generalizing to cosmological settings. In this talk, I will discuss how the thermodynamic properties of black holes can be understood in such scenarios through the Euclidean path integral. I will also discuss black holes in a more generalized, semi-classical setting, and suggest a consistent framework for describing a wide variety of dynamical black hole models and ultracompact objects, with consequences for horizon formation, back-reaction, and the growth of astrophysical black holes.

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Zoom link https://pitp.zoom.us/j/97647270696?pwd=bXhFYWYyYUlrTEUyVXhtOUNDakhhQT09