Zobrazeno 1 - 10
of 136
pro vyhledávání: '"Alsing, Justin"'
Autor:
Thorp, Stephen, Alsing, Justin, Peiris, Hiranya V., Deger, Sinan, Mortlock, Daniel J., Leistedt, Boris, Leja, Joel, Loureiro, Arthur
We present an efficient Bayesian method for estimating individual photometric redshifts and galaxy properties under a pre-trained population model (pop-cosmos) that was calibrated using purely photometric data. This model specifies a prior distributi
Externí odkaz:
http://arxiv.org/abs/2406.19437
Scaling laws for large language models (LLMs) have provided useful guidance on how to train ever larger models for predictable performance gains. Time series forecasting shares a similar sequential structure to language, and is amenable to large-scal
Externí odkaz:
http://arxiv.org/abs/2405.13867
Autor:
Alsing, Justin, Thorp, Stephen, Deger, Sinan, Peiris, Hiranya, Leistedt, Boris, Mortlock, Daniel, Leja, Joel
Publikováno v:
ApJS 274, 12 (2024)
We present pop-cosmos: a comprehensive model characterizing the galaxy population, calibrated to $140,938$ ($r<25$ selected) galaxies from the Cosmic Evolution Survey (COSMOS) with photometry in $26$ bands from the ultra-violet to the infra-red. We c
Externí odkaz:
http://arxiv.org/abs/2402.00935
Autor:
Thorp, Stephen, Peiris, Hiranya V., Mortlock, Daniel J., Alsing, Justin, Leistedt, Boris, Deger, Sinan
We present a simple method for assessing the predictive performance of high-dimensional models directly in data space when only samples are available. Our approach is to compare the quantiles of observables predicted by a model to those of the observ
Externí odkaz:
http://arxiv.org/abs/2402.00930
Autor:
Sarin, Nikhil, Peiris, Hiranya V., Mortlock, Daniel J., Alsing, Justin, Nissanke, Samaya M., Feeney, Stephen M.
Gravitational-wave (GW) observations of neutron star-black hole (NSBH) mergers are sensitive to the nuclear equation of state (EOS). We present a new methodology for EOS inference with non-parametric Gaussian process (GP) priors, enabling direct cons
Externí odkaz:
http://arxiv.org/abs/2311.05689
We present a framework for the efficient computation of optimal Bayesian decisions under intractable likelihoods, by learning a surrogate model for the expected utility (or its distribution) as a function of the action and data spaces. We leverage re
Externí odkaz:
http://arxiv.org/abs/2311.05742
Set-based learning is an essential component of modern deep learning and network science. Graph Neural Networks (GNNs) and their edge-free counterparts Deepsets have proven remarkably useful on ragged and topologically challenging datasets. The key t
Externí odkaz:
http://arxiv.org/abs/2310.03812
Publikováno v:
ApJS 265 23 (2023)
The Probabilistic Value-Added Bright Galaxy Survey (PROVABGS) catalog will provide the posterior distributions of physical properties of $>10$ million DESI Bright Galaxy Survey (BGS) galaxies. Each posterior distribution will be inferred from joint B
Externí odkaz:
http://arxiv.org/abs/2209.14323
We present a Bayesian hierarchical framework to analyze photometric galaxy survey data with stellar population synthesis (SPS) models. Our method couples robust modeling of spectral energy distributions with a population model and a noise model to ch
Externí odkaz:
http://arxiv.org/abs/2207.07673
Autor:
Hu, Teng, Khaire, Vikram, Hennawi, Joseph F., Walther, Michael, Hiss, Hector, Alsing, Justin, Oñorbe, Jose, Lukic, Zarija, Davies, Frederick
We present a new approach to measure the power-law temperature density relationship $T=T_0 (\rho / \bar{\rho})^{\gamma -1}$ and the UV background photoionization rate $\Gamma_{\rm HI}$ of the IGM based on the Voigt profile decomposition of the Ly$\al
Externí odkaz:
http://arxiv.org/abs/2207.07151