Astrophysics

The Sunyaev-Zel’dovich Effect—the Doppler boost of low-energy Cosmic Microwave Background photons scattering off free electrons—is an excellent probe of ionized gas residing in distant galaxies. Its two main constituents are the kinematic SZ effect (kSZ), where electrons have a non-zero line-of-sight (LOS) velocity and which probes the electron line-of-sight momentum, and the thermal SZ effect (tSZ), where electrons have high energies due to their temperature, and which probes the electron integrated pressure. These two effects provide complementary information to constrain the thermodynamic profile of gas residing in distant galaxies, which can be further used to understand feedback processes, a necessary ingredient to describe the evolution of the large-scale structure in our Universe. Both tSZ and kSZ can be measured in cross-correlation with large-scale structure. In this talk, I will discuss my past and ongoing measurements of the SZ-galaxy cross-correlation with unWISE galaxies, where to measure the kSZ effect I use the projected-fields estimator. unWISE is a galaxy catalog containing over 500 million galaxies on the full sky and consists of three subsamples of mean redshifts z=0.5, 1.1, 1.5, whose halo occupation distribution I have already constrained. If time permits, I will also present my work on mitigating foregrounds in the SZ cross-correlations, particularly the Cosmic Infrared Background (CIB).

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The current progress in gravitational wave detection opened a new exciting window in cosmology. It is natural to ask ourselves how we can best use this new tool to explore physics beyond the standard model. With this idea in mind, my collaborators and I asked what we could learn from Cosmological Gravitational Wave Backgrounds if they were to be detected to a certain accuracy. By drawing comparison to the cosmic microwave background, we investigate the impact of elastic scattering on any cosmological background. Specifically, we focus on quantifying spectral distortions in the energy density spectrum of CGWB attributed to interaction with beyond-the-standard-model particles. We will also explore the effect that elastic scattering of graviton of Primordial Black holes would have on CGWB in the regime where PBHs account for all the dark matter.

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Zoom link https://pitp.zoom.us/j/98460268383?pwd=RytzWHd5dU1lenRhWG1NYXM3OVJpQT09

In this talk, I will discuss two lines of research revolving around the study of weak gravitational lensing in our Universe. First, I will present a pipeline that has been in development to model the matter power spectrum from large to small scales, focusing on stage-IV photometric galaxy surveys. I will discuss the fundamental ingredients of this pipeline, the consistency checks performed to validate it, and how this new tool fares when performing a full Bayesian parameter estimation analysis with an LSST-like survey. In the second part of this talk, I will present a new and exciting methodology to study weakly-lensed gravitational waves in the wave-optics regime.

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Zoom link https://pitp.zoom.us/j/98447653523?pwd=QjFDdk1LZ25LeDd6Nk5iRCtGbFNYQT09

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