Zobrazeno 1 - 10
of 855
pro vyhledávání: '"Sharma, Siddharth"'
Autor:
Nguyen, Tri, Villaescusa-Navarro, Francisco, Mishra-Sharma, Siddharth, Cuesta-Lazaro, Carolina, Torrey, Paul, Farahi, Arya, Garcia, Alex M., Rose, Jonah C., O'Neil, Stephanie, Vogelsberger, Mark, Shen, Xuejian, Roche, Cian, Anglés-Alcázar, Daniel, Kallivayalil, Nitya, Muñoz, Julian B., Cyr-Racine, Francis-Yan, Roy, Sandip, Necib, Lina, Kollmann, Kassidy E.
The connection between galaxies and their host dark matter (DM) halos is critical to our understanding of cosmology, galaxy formation, and DM physics. To maximize the return of upcoming cosmological surveys, we need an accurate way to model this comp
Externí odkaz:
http://arxiv.org/abs/2409.02980
Autor:
Zhang, Gemma, Helfer, Thomas, Gagliano, Alexander T., Mishra-Sharma, Siddharth, Villar, V. Ashley
A common setting in astronomy is the availability of a small number of high-quality observations, and larger amounts of either lower-quality observations or synthetic data from simplified models. Time-domain astrophysics is a canonical example of thi
Externí odkaz:
http://arxiv.org/abs/2408.16829
Autor:
Delaunoy, Arnaud, Bonardeaux, Maxence de la Brassinne, Mishra-Sharma, Siddharth, Louppe, Gilles
Simulation-based inference methods have been shown to be inaccurate in the data-poor regime, when training simulations are limited or expensive. Under these circumstances, the inference network is particularly prone to overfitting, and using it witho
Externí odkaz:
http://arxiv.org/abs/2408.15136
Autor:
Giovanetti, Cara, Lisanti, Mariangela, Liu, Hongwan, Mishra-Sharma, Siddharth, Ruderman, Joshua T.
We introduce LINX (Light Isotope Nucleosynthesis with JAX), a new differentiable public Big Bang Nucleosynthesis (BBN) code designed for fast parameter estimation. By leveraging JAX, LINX achieves both speed and differentiability, enabling the use of
Externí odkaz:
http://arxiv.org/abs/2408.14538
Autor:
Giovanetti, Cara, Lisanti, Mariangela, Liu, Hongwan, Mishra-Sharma, Siddharth, Ruderman, Joshua T.
We present the first joint-likelihood analysis of Big Bang Nucleosynthesis (BBN) and Cosmic Microwave Background (CMB) data. Bayesian inference is performed on the baryon abundance and the effective number of neutrino species, $N_{\rm eff}$, using a
Externí odkaz:
http://arxiv.org/abs/2408.14531
Analyses of the cosmic 21-cm signal are hampered by astrophysical foregrounds that are far stronger than the signal itself. These foregrounds, typically confined to a wedge-shaped region in Fourier space, often necessitate the removal of a vast major
Externí odkaz:
http://arxiv.org/abs/2407.21097
We present PAPERCLIP (Proposal Abstracts Provide an Effective Representation for Contrastive Language-Image Pre-training), a method which associates astronomical observations imaged by telescopes with natural language using a neural network model. Th
Externí odkaz:
http://arxiv.org/abs/2403.08851
Autor:
Ivanov, Mikhail M., Cuesta-Lazaro, Carolina, Mishra-Sharma, Siddharth, Obuljen, Andrej, Toomey, Michael W.
Perturbative, or effective field theory (EFT)-based, full-shape analyses of galaxy clustering data involve ``nuisance parameters'' to capture various observational effects such as the galaxy-dark matter connection (galaxy bias). We present an efficie
Externí odkaz:
http://arxiv.org/abs/2402.13310
Publikováno v:
Mon. Not. Roy. Astron. Soc., 527 (2024), 7459-7481
The influx of massive amounts of data from current and upcoming cosmological surveys necessitates compression schemes that can efficiently summarize the data with minimal loss of information. We introduce a method that leverages the paradigm of self-
Externí odkaz:
http://arxiv.org/abs/2308.09751
When analyzing real-world data it is common to work with event ensembles, which comprise sets of observations that collectively constrain the parameters of an underlying model of interest. Such models often have a hierarchical structure, where "local
Externí odkaz:
http://arxiv.org/abs/2306.12584