Long-term OVRO monitoring of LSI+61303: confirmation of the two close periodicities

Context: The gamma-ray binary LSI+61303 shows multiple periodicities. The timing analysis of 6.7 yr of GBI radio data and of 6 yr of Fermi-LAT GeV gamma-ray data both have found two close periodicities P1(GBI) = 26.49 \pm 0.07 d, P2(GBI)=26.92 \pm 0.07 d and P1(gamma)=26.48 \pm 0.08 d, P2(gamma) = 26.99 \pm 0.08 d. Aims: The system LSI+61303 is the object of several continuous monitoring programs at low and high energies. The frequency difference between f1 and f2 of only 0.0006 d(-1) requires long-term monitoring because the frequency resolution in timing analysis is related to the inverse of the overall time interval. The Owens Valley Radio Observatory (OVRO) 40 m telescope has been monitoring the source at 15 GHz for five years and overlaps with Fermi-LAT monitoring. The aim of this work is to establish whether the two frequencies are also resolved in the OVRO monitoring. Methods: We analysed OVRO data with the Lomb-Scargle method. We also updated the timing analysis of Fermi-LAT observations. Results: The periodograms of OVRO data confirm the two periodicities P1(OVRO) = 26.5 \pm 0.1 d and P2(OVRO) = 26.9 \pm 0.1 d. Conclusions: The three indipendent measurements of P1 and P2 with GBI, OVRO, and Fermi-LAT observations confirm that the periodicities are permanent features of the system LSI+61303. The similar behaviours of the emission at high (GeV) and low (radio) energy when the compact object in LSI+61303 is toward apastron suggest that the emission is caused by the same periodically (P1) ejected population of electrons in a precessing (P2) jet.Comment: 4 pages, 7 figures, A&A Letters in pres

Long-term OVRO monitoring of LS I +61º 303: confirmation of the two close periodicities

Context. The gamma-ray binary LS I +61° 303 shows multiple periodicities. The timing analysis of 6.7 yr of GBI radio data and of 6 yr of Fermi-LAT GeV gamma-ray data both have found two close periodicities P_(1,GBI) = 26.49 ± 0.07 d, P_(2,GBI) = 26.92 ± 0.07 d and P_(1,γ) = 26.48 ± 0.08 d, P_(2,γ) = 26.99 ± 0.08 d. Aims. The system LS I +61°303 is the object of several continuous monitoring programs at low and high energies. The frequency difference between ν_1 and ν_2 of only 0.0006 d^(-1) requires long-term monitoring because the frequency resolution in timing analysis is related to the inverse of the overall time interval. The Owens Valley Radio Observatory (OVRO) 40 m telescope has been monitoring the source at 15 GHz for five years and overlaps with Fermi-LAT monitoring. The aim of this work is to establish whether the two frequencies are also resolved in the OVRO monitoring. Methods. We analysed OVRO data with the Lomb-Scargle method. We also updated the timing analysis of Fermi-LAT observations. Results. The periodograms of OVRO data confirm the two periodicities and . Conclusions. The three independent measurements of P_1 and P_2 with GBI, OVRO, and Fermi-LAT observations confirm that the periodicities are permanent features of the system LS I +61°303. The similar behaviours of the emission at high (GeV) and low (radio) energy when the compact object in LS I +61°303 is toward apastron suggest that the emission is caused by the same periodically (P_1) ejected population of electrons in a precessing (P_2) jet

Ab initio quantum Monte Carlo calculations of spin superexchange in cuprates: the benchmarking case of Ca2_2CuO3_3

In view of the continuous theoretical efforts aimed at an accurate microscopic description of the strongly correlated transition metal oxides and related materials, we show that with continuum quantum Monte Carlo (QMC) calculations it is possible to obtain the value of the spin superexchange coupling constant of a copper oxide in a quantitatively excellent agreement with experiment. The variational nature of the QMC total energy allows us to identify the best trial wave function out of the available pool of wave functions, which makes the approach essentially free from adjustable parameters and thus truly ab initio. The present results on magnetic interactions suggest that QMC is capable of accurately describing ground state properties of strongly correlated materials.Comment: Published in Physical Review

Machine Learning Diffusion Monte Carlo Energies

We present two machine learning methodologies that are capable of predicting diffusion Monte Carlo (DMC) energies with small datasets (~60 DMC calculations in total). The first uses voxel deep neural networks (VDNNs) to predict DMC energy densities using Kohn-Sham density functional theory (DFT) electron densities as input. The second uses kernel ridge regression (KRR) to predict atomic contributions to the DMC total energy using atomic environment vectors as input (we used atom centred symmetry functions, atomic environment vectors from the ANI models, and smooth overlap of atomic positions). We first compare the methodologies on pristine graphene lattices, where we find the KRR methodology performs best in comparison to gradient boosted decision trees, random forest, gaussian process regression, and multilayer perceptrons. In addition, KRR outperforms VDNNs by an order of magnitude. Afterwards, we study the generalizability of KRR to predict the energy barrier associated with a Stone-Wales defect. Lastly, we move from 2D to 3D materials and use KRR to predict total energies of liquid water. In all cases, we find that the KRR models are more accurate than Kohn-Sham DFT and all mean absolute errors are less than chemical accuracy