Cosmology with the Lyman-a Forest 3D Power Spectrum
Campus, Varian 355
Cosmology Seminars are held on Mondays at 11 am, on the 3rd floor Varian conference room. These are more focused and less didactic than the colloquium, and provide a stage for younger researchers to present their work in more detail.
Please contact Sandy Yuan, Delon Shen, or Risa Wechsler for more information.
Campus, Varian 355
Campus, Varian 355
Campus, Varian 355
Detecting strong lenses in a large dataset such as Euclid is very challenging due to the unbalanced nature of dataset. Existing CNN models are producing large amount of false positives, for example one strong lens candidate will be accompanied by 100's of false positives in the final sample. To over come this challenge, we have developed a novel ML pipeline called DenseLens, which consists of three components namely Classification ensemble, Regression ensemble and Segmentation.
Campus, Varian 355
For several decades, inflation has been considered the main paradigm for describing the early Universe. The theory describing inflation can be tested by measuring properties such as primordial non-Gaussianity (PNG). In particular, the single-field slow-roll inflation predict an almost Gaussian distribution of primordial fluctuations, i.e. a minimal amount of PNG.
Campus, Varian 355
First, I will present a novel approach to the flat-sky angular power spectrum approximation, highlighting its comparison to the full-sky formalism and presenting fast and efficient computational methods paramount for the data analysis of upcoming galaxy surveys. I will then construct the 3D galaxy observables like the power spectrum, systematically exploring the projection effects and relations of these observables to the theoretically computed quantities.
Campus, Varian 355
Recent advancements in cosmological observations have provided an unprecedented opportunity to investigate the distribution of baryons relative to the underlying matter. In this work, we pioneer the use of photometric redshifts from the imaging survey of the Dark Energy Spectroscopic Instrument (DESI) to probe the stacked kinematic Sunyaev-Zel'dovich (kSZ) effect with the Atacama Cosmology Telescope (ACT) around DESI luminous red galaxies (LRGs) and infer the distribution of baryons.
Campus, Varian 355
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.
Campus, Varian 355
Campus, Varian 355
High-redshift quasars and their host galaxies are among the most extreme systems at cosmic dawn. The stellar emission of high-redshift quasar host galaxies encodes critical information about the coevolution of SMBHs and their hosts in the early universe, which is nevertheless very challenging to detect. The unprecedented spatial resolution and infrared coverage of JWST have enabled the detection of high-redshift quasar host galaxies in the rest-frame optical for the first time.
Campus, Varian 355
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.