Gaussian Process Reconstruction of Reionization History

Abstract We reconstruct the history of reionization using Gaussian process regression. Using the UV luminosity data compilation from Hubble Frontiers Fields we reconstruct the redshift evolution of UV luminosity density and thereby the evolution of the source term in the ionization equation. This model-independent reconstruction rules out single power-law evolution of the luminosity density but supports the logarithmic double power-law parameterization. We obtain reionization history by integrating ionization equations with the reconstructed source term. Using the optical depth constraint from Planck cosmic microwave background observation, measurement of UV luminosity function integrated until truncation magnitude of −17 and −15, and derived ionization fraction from high redshift quasar, galaxies, and gamma-ray burst observations, we constrain the history of reionization. In the conservative case we find the constraint on the optical depth as τ = 0.052 ± 0.001 ± 0.002 at 68% and 95% confidence intervals. We find the redshift duration between 10% and 90% ionization to be 2.05 − 0.21 − 0.30 + 0.11 + 0.37 . Longer duration of reionization is supported if UV luminosity density data with truncation magnitude of −15 is used in the joint analysis. Our results point out that even in a conservative reconstruction, a combination of cosmological and astrophysical observations can provide stringent constraints on the epoch of reionization.

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Observations of GRBs at high redshift

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2006.1992 ◽

2007 ◽

Vol 365 (1854) ◽

pp. 1377-1384 ◽

Cited By ~ 21

Author(s):

Nial R Tanvir ◽

Páll Jakobsson

Keyword(s):

Gamma Ray ◽

High Redshift ◽

Cosmological Parameters ◽

Gamma Ray Bursts ◽

Star Formation History ◽

Redshift Distribution ◽

Formation History ◽

History Of ◽

The Universe

The extreme luminosity of gamma-ray bursts and their afterglows means they are detectable, in principle, to very high redshifts. Although the redshift distribution of gamma-ray bursts (GRBs) is difficult to determine, due to incompleteness of present samples, we argue that for Swift-detected bursts, the median redshift is between 2.5 and 3, with a few per cent probably at z >6. Thus, GRBs are potentially powerful probes of the era of reionization and the sources responsible for it. Moreover, it seems probable that they can provide constraints on the star-formation history of the Universe and may also help in the determination of the cosmological parameters.

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Kinematic constraints beyond z ≃ 0 using calibrated GRB correlations

Astronomy and Astrophysics ◽

10.1051/0004-6361/202038264 ◽

2020 ◽

Vol 641 ◽

pp. A174

Author(s):

Orlando Luongo ◽

Marco Muccino

Keyword(s):

Gamma Ray ◽

High Redshift ◽

Systematic Errors ◽

Gamma Ray Bursts ◽

Auxiliary Variables ◽

Confidence Levels ◽

History Of ◽

Standard Cosmological Model ◽

Model Independent ◽

The Universe

Context. The dynamics of the Universe are revised using high-redshift data from gamma-ray bursts to constrain cosmographic parameters by means of model-independent techniques. Aims. Considering samples from four gamma-ray burst correlations and two hierarchies up to j0 and s0, respectively, we derived limits over the expansion history of the Universe. Since cosmic data span outside z ≃ 0, we investigated additional cosmographic methods such as auxiliary variables and Padé approximations Methods. Beziér polynomials were employed to calibrate our correlations and heal the circularity problem. Several Markov chain Monte Carlo simulations were performed on the model-independently calibrated Amati, Ghirlanda, Yonetoku, and combo correlations to obtain 1 − σ and 2 − σ confidence levels and to test the standard cosmological model. Results. Reasonable results are found up to j0 and s0 hierarchies, respectively, only partially alleviating the tension on local H0 measurements as j0 hierarchy is considered. Discussions on systematic errors have been extensively reported here. Conclusions. Our findings show that the ΛCDM model is not fully confirmed using gamma-ray bursts. Indications against a genuine cosmological constant are summarized and commented on in detail.

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Using Gaussian Process Regression to Integrate the Transition Structure Factor Curve for the Many-Body Correlation Energy

10.26226/morressier.5fa409874d4e91fe5c54b97a ◽

2020 ◽

Author(s):

Laura Weiler

Keyword(s):

Gaussian Process ◽

Structure Factor ◽

Correlation Energy ◽

Gaussian Process Regression ◽

Many Body ◽

Transition Structure ◽

The Many ◽

Structure Factor Curve ◽

Body Correlation

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Exchange Spin Coupling from Gaussian Process Regression

10.26434/chemrxiv.12589541.v3 ◽

2020 ◽

Author(s):

Marc Philipp Bahlke ◽

Natnael Mogos ◽

Jonny Proppe ◽

Carmen Herrmann

Keyword(s):

Machine Learning ◽

Gaussian Process ◽

Gaussian Process Regression ◽

Molecular Magnets ◽

Molecular Structures ◽

Spin Coupling ◽

Structure Property ◽

Data Set ◽

Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

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SAMPL6 Challenge Results from pKa Predictions Based on a General Gaussian Process Model

10.26434/chemrxiv.6406505.v2 ◽

2018 ◽

Author(s):

Caitlin C. Bannan ◽

David Mobley ◽

A. Geoff Skillman

Keyword(s):

Gaussian Process ◽

Process Model ◽

Molecular Graph ◽

Gaussian Process Regression ◽

Ionization State ◽

Training Set ◽

Physiochemical Properties ◽

Quantile Plots ◽

Physical And Chemical ◽

Good Agreement

<div>A variety of fields would benefit from accurate pK<sub>a</sub> predictions, especially drug design due to the affect a change in ionization state can have on a molecules physiochemical properties.</div><div>Participants in the recent SAMPL6 blind challenge were asked to submit predictions for microscopic and macroscopic pK<sub>a</sub>s of 24 drug like small molecules.</div><div>We recently built a general model for predicting pK<sub>a</sub>s using a Gaussian process regression trained using physical and chemical features of each ionizable group.</div><div>Our pipeline takes a molecular graph and uses the OpenEye Toolkits to calculate features describing the removal of a proton.</div><div>These features are fed into a Scikit-learn Gaussian process to predict microscopic pK<sub>a</sub>s which are then used to analytically determine macroscopic pK<sub>a</sub>s.</div><div>Our Gaussian process is trained on a set of 2,700 macroscopic pK<sub>a</sub>s from monoprotic and select diprotic molecules.</div><div>Here, we share our results for microscopic and macroscopic predictions in the SAMPL6 challenge.</div><div>Overall, we ranked in the middle of the pack compared to other participants, but our fairly good agreement with experiment is still promising considering the challenge molecules are chemically diverse and often polyprotic while our training set is predominately monoprotic.</div><div>Of particular importance to us when building this model was to include an uncertainty estimate based on the chemistry of the molecule that would reflect the likely accuracy of our prediction. </div><div>Our model reports large uncertainties for the molecules that appear to have chemistry outside our domain of applicability, along with good agreement in quantile-quantile plots, indicating it can predict its own accuracy.</div><div>The challenge highlighted a variety of means to improve our model, including adding more polyprotic molecules to our training set and more carefully considering what functional groups we do or do not identify as ionizable. </div>

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Introduction: Seventy Years of Calorimetry

10.1093/oso/9780198786351.003.0001 ◽

2018 ◽

Author(s):

Richard Wigmans

Keyword(s):

Gamma Ray ◽

Experimental Techniques ◽

Gamma Ray Detectors ◽

History Of

The history of calorimetry is described, starting with the small scintillating crystals that were used as nuclear gamma ray detectors, to the multi-ton instruments that form the heart of modern experiments at colliding-beam accelerators. The different experimental techniques that are used for generating signals are reviewed.

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