Cosmology

Abstract:

<jats:p><jats:italic>Context.N</jats:italic>-body simulations are computationally expensive and machine learning (ML) based emulation techniques have thus emerged as a way to increase their speed. Surrogate models are indeed fast, however, they are limited in terms of their trustworthiness due to potentially substantial emulation errors that current approaches are not equipped to correct.</jats:p> <jats:p><jats:italic>Aims.</jats:italic> To alleviate this problem, we have introduced COmoving Computer Acceleration (COCA), a hybrid framework interfacing ML algorithm with an <jats:italic>N</jats:italic>-body simulator. The correct physical equations of motion are solved in an emulated frame of reference, so that any emulation error is corrected by design. Thus, we are able to find a solution for the perturbation of particle trajectories around the ML solution. This approach is computationally cheaper than obtaining the full solution and it is guaranteed to converge to the truth as the number of force evaluations is increased.</jats:p> <jats:p><jats:italic>Methods.</jats:italic> Even though it is applicable to any ML algorithm and <jats:italic>N</jats:italic>-body simulator, we assessed this approach in the particular case of particle-mesh (PM) cosmological simulations in a frame of reference predicted by a convolutional neural network. In such cases, the time dependence is encoded as an additional input parameter to the network.</jats:p> <jats:p><jats:italic>Results.</jats:italic> We find that COCA efficiently reduces emulation errors in particle trajectories, requiring far fewer force evaluations than running the corresponding simulation without ML. As a consequence, we were able to obtain accurate final density and velocity fields for a reduced computational budget. We demonstrate that this method exhibits robustness when applied to examples outside the range of the training data. When compared to the direct emulation of the Lagrangian displacement field using the same training resources, COCA’s ability to correct emulation errors results in more accurate predictions.</jats:p> <jats:p><jats:italic>Conclusions.</jats:italic> Therefore, COCA makes <jats:italic>N</jats:italic>-body simulations cheaper by skipping unnecessary force evaluations, while still solving the correct equations of motion and correcting for emulation errors made by ML.</jats:p>

Abstract:

We present the third data release of the JWST Advanced Deep Extragalactic Survey (JADES), providing both imaging and spectroscopy in the two GOODS fields. Spectroscopy consists of medium-depth and deep NIRSpec/microshutter assembly spectra of 4000 targets, covering the spectral range 0.6–5.3 μm and observed with both the low-dispersion prism (R = 30–300) and all three medium-resolution gratings (R = 500–1500). We describe the observations, data reduction, sample selection, and target allocation. We measured 2375 redshifts (2053 from multiple emission lines); our targets span the range from z = 0.5 up to z = 13, including 404 at z > 5. The data release includes 2D and 1D fully reduced spectra, with slit-loss corrections and background subtraction optimized for point sources. We also provide redshifts and signal-to-noise ratio > 5 emission-line flux catalogs for the prism and grating spectra, and concise guidelines on how to use these data products. Alongside spectroscopy, we are also publishing fully calibrated NIRCam imaging, which enables studying the JADES sample with the combined power of imaging and spectroscopy. Together, these data provide the largest statistical sample to date to characterize the properties of galaxy populations in the first billion years after the Big Bang.