Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Parker, Liam"'
Autor:
Golkar, Siavash, Bietti, Alberto, Pettee, Mariel, Eickenberg, Michael, Cranmer, Miles, Hirashima, Keiya, Krawezik, Geraud, Lourie, Nicholas, McCabe, Michael, Morel, Rudy, Ohana, Ruben, Parker, Liam Holden, Blancard, Bruno Régaldo-Saint, Cho, Kyunghyun, Ho, Shirley
Transformers have revolutionized machine learning across diverse domains, yet understanding their behavior remains crucial, particularly in high-stakes applications. This paper introduces the contextual counting task, a novel toy problem aimed at enh
Externí odkaz:
http://arxiv.org/abs/2406.02585
Autor:
Massara, Elena, Hahn, ChangHoon, Eickenberg, Michael, Ho, Shirley, Hou, Jiamin, Lemos, Pablo, Modi, Chirag, Dizgah, Azadeh Moradinezhad, Parker, Liam, Blancard, Bruno Régaldo-Saint
We present the first $\Lambda$CDM cosmological analysis performed on a galaxy survey using marked power spectra. The marked power spectrum is the two-point function of a marked field, where galaxies are weighted by a function that depends on their lo
Externí odkaz:
http://arxiv.org/abs/2404.04228
Autor:
Hou, Jiamin, Dizgah, Azadeh Moradinezhad, Hahn, ChangHoon, Eickenberg, Michael, Ho, Shirley, Lemos, Pablo, Massara, Elena, Modi, Chirag, Parker, Liam, Blancard, Bruno Régaldo-Saint
Extracting the non-Gaussian information of the cosmic large-scale structure (LSS) is vital in unlocking the full potential of the rich datasets from the upcoming stage-IV galaxy surveys. Galaxy skew spectra serve as efficient beyond-two-point statist
Externí odkaz:
http://arxiv.org/abs/2401.15074
Autor:
Blancard, Bruno Régaldo-Saint, Hahn, ChangHoon, Ho, Shirley, Hou, Jiamin, Lemos, Pablo, Massara, Elena, Modi, Chirag, Dizgah, Azadeh Moradinezhad, Parker, Liam, Yao, Yuling, Eickenberg, Michael
The non-Gaussisan spatial distribution of galaxies traces the large-scale structure of the Universe and therefore constitutes a prime observable to constrain cosmological parameters. We conduct Bayesian inference of the $\Lambda$CDM parameters $\Omeg
Externí odkaz:
http://arxiv.org/abs/2310.15250
Autor:
Lemos, Pablo, Parker, Liam, Hahn, ChangHoon, Ho, Shirley, Eickenberg, Michael, Hou, Jiamin, Massara, Elena, Modi, Chirag, Dizgah, Azadeh Moradinezhad, Blancard, Bruno Regaldo-Saint, Spergel, David
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 anal
Externí odkaz:
http://arxiv.org/abs/2310.15256
Autor:
Hahn, ChangHoon, Lemos, Pablo, Parker, Liam, Blancard, Bruno Régaldo-Saint, Eickenberg, Michael, Ho, Shirley, Hou, Jiamin, Massara, Elena, Modi, Chirag, Dizgah, Azadeh Moradinezhad, Spergel, David
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
Externí odkaz:
http://arxiv.org/abs/2310.15246
Autor:
Hahn, ChangHoon, Eickenberg, Michael, Ho, Shirley, Hou, Jiamin, Lemos, Pablo, Massara, Elena, Modi, Chirag, Dizgah, Azadeh Moradinezhad, Parker, Liam, Blancard, Bruno Régaldo-Saint
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
Externí odkaz:
http://arxiv.org/abs/2310.15243
Autor:
Parker, Liam, Lanusse, Francois, Golkar, Siavash, Sarra, Leopoldo, Cranmer, Miles, Bietti, Alberto, Eickenberg, Michael, Krawezik, Geraud, McCabe, Michael, Ohana, Ruben, Pettee, Mariel, Blancard, Bruno Regaldo-Saint, Tesileanu, Tiberiu, Cho, Kyunghyun, Ho, Shirley
We present AstroCLIP, a single, versatile model that can embed both galaxy images and spectra into a shared, physically meaningful latent space. These embeddings can then be used - without any model fine-tuning - for a variety of downstream tasks inc
Externí odkaz:
http://arxiv.org/abs/2310.03024
Autor:
McCabe, Michael, Blancard, Bruno Régaldo-Saint, Parker, Liam Holden, Ohana, Ruben, Cranmer, Miles, Bietti, Alberto, Eickenberg, Michael, Golkar, Siavash, Krawezik, Geraud, Lanusse, Francois, Pettee, Mariel, Tesileanu, Tiberiu, Cho, Kyunghyun, Ho, Shirley
We introduce multiple physics pretraining (MPP), an autoregressive task-agnostic pretraining approach for physical surrogate modeling. MPP involves training large surrogate models to predict the dynamics of multiple heterogeneous physical systems sim
Externí odkaz:
http://arxiv.org/abs/2310.02994
Autor:
Golkar, Siavash, Pettee, Mariel, Eickenberg, Michael, Bietti, Alberto, Cranmer, Miles, Krawezik, Geraud, Lanusse, Francois, McCabe, Michael, Ohana, Ruben, Parker, Liam, Blancard, Bruno Régaldo-Saint, Tesileanu, Tiberiu, Cho, Kyunghyun, Ho, Shirley
Large Language Models have not yet been broadly adapted for the analysis of scientific datasets due in part to the unique difficulties of tokenizing numbers. We propose xVal, a numerical encoding scheme that represents any real number using just a si
Externí odkaz:
http://arxiv.org/abs/2310.02989