arXiv subjects

Lucas A. Souza

Publications and source records attributed to Lucas A. Souza.

8 recordsLinked to original sources

arXiv2026

Bayesian constraints on quadratic $f(Q)$ gravity from the ADM mass of white dwarfs

We calculate static equilibrium sequences for white dwarfs (WDs) in quadratic symmetric teleparallel gravity, f(Q)=Q+alpha Q^2, and perform a Bayesian comparison between the negative-coupling sector, general relativity (GR), and the positive-coupling sector using mass-radius measurements. Regular interior solutions are obtained by imposing the affine field equation and selecting the analytic branch C=r exp(B/2), and are matched to asymptotically flat exterior spacetimes to determine the ADM mass. The matter sector is described by a cold carbon-12 equation of state including relativistic electron degeneracy, nuclear rest energy, and Coulomb lattice corrections. Central density is marginalized at fixed alpha with a log-uniform prior. The sequences span 10^14 <= |alpha|/cm^2 <= 10^21. Positive couplings shift the compact branch toward larger radii and lower masses, whereas negative couplings produce high-mass extensions, including an admissible equilibrium configuration with M_ADM=14.34 solar masses. Using Sirius B, QS Vir, V471 Tau B, ZTF J1901+1458, and LHS 4033, we obtain conditional posterior medians alpha_-= -1.12x10^16 cm^2 and alpha_+=1.85x10^17 cm^2. With equal prior weights, the posterior probabilities for the negative, GR, and positive sectors are 0.228, 0.378, and 0.395, respectively, with ln(B_+/GR)=0.043. Current data therefore provide conditional constraints on the coupling but no direct evidence for modified gravity, emphasizing that the ADM mass prescription and admissible stellar domain must be consistently defined when using WD observations to constrain the theory.

gr-qc↗

arXiv2026

Variational neural-network solution of the two-body $^{17}\mathrm{F}$ proton-halo problem with a Coulomb--Whittaker tail

We present a variational artificial neural-network (VANN) solution of $^{17}$F in a two-body $^{16}$O$+p$ potential model. The calculation uses a standard interaction from the literature as a controlled benchmark for testing whether a neural variational ansatz can reproduce not only bound-state energies and interior wave functions, but also the Coulomb--Whittaker tails that control halo and peripheral-capture observables. The reduced radial wave function is obtained by minimizing the Rayleigh quotient of the radial Schrödinger Hamiltonian with the constraints required by each partial wave. Because the variational energy can converge before the asymptotic normalization is correct, the ansatz combines a neural interior with the charged-particle Coulomb--Whittaker form. In the $s_{1/2}$ channel, the Pauli-forbidden $0s_{1/2}$ component is computed and the physical one-node branch is checked independently for forbidden-state contamination. The Coulomb--Whittaker-constrained VANN reproduces independent Numerov benchmarks for the compact $d_{5/2}$ ground state and the extended $s_{1/2}$ halo state in energy, nodes, rms radius, and overlap. The compact-state ANC agrees to within one percent, while the halo ANC differs by about $4.2\%$, within the larger numerical sensitivity of the asymptotic extraction. The continuum scattering states are obtained by standard Numerov integration with Coulomb matching; only the bound states are represented by the neural ansatz. Combined with these $p$-wave scattering states, the VANN bound states yield astrophysical $S$ factors consistent with published benchmarks and data within the accuracy of the adopted two-body model. The results demonstrate the usefulness of physically constrained neural wave functions for tail-sensitive nuclear calculations and identify the asymptotic region as the most sensitive part of the calculation.

nucl-th↗

arXiv2026

Neural-network solution of subtracted three-body Faddeev integral equations near the Efimov limit

We apply a deep-neural-network (DNN) ansatz to the symmetrized spectator vector of the subtracted three-body Faddeev integral equation for identical bosons near the Efimov limit. The network is trained by minimizing the residual of the discretized integral equation, while the positive binding scale associated with the three-body energy is treated as a trainable parameter. Deterministic diagonalization of the same discretized kernel is used only as an a posteriori numerical benchmark. As preliminary validation, the neural-solver strategy is tested on the analytically solvable hydrogen radial problem. At unitarity, the DNN reproduces the Efimov ground-state binding scale with a DNN--deterministic deviation of $0.022\%$, while the first excited state is recovered to $0.002\%$. The deterministic solver recovers the universal Efimov scaling ratio $e^{2π/s_0}\simeq 515.03$, and the neural method traces the bound-state branches as a function of the inverse scattering length $1/a$ by continuation from the unitary solution. These results indicate that DNN-based residual minimization can provide a compact and differentiable representation of a renormalized few-body integral-equation solution in a regime governed by discrete scale invariance.

nucl-th↗

arXiv2026

Performance-Driven Causal Signal Engineering for Financial Markets under Non-Stationarity

We introduce a performance-driven framework for constructing strictly causal forward-oriented observables in strongly non-stationary time series. The method combines a robustly normalized composite of heterogeneous indicators with a causally computed derivative component, yielding a local phase-leading effect that is amplified near regime transitions while remaining fully causal. A hysteresis-based decision functional maps the observable into discrete system states, with execution delayed by one step to preserve strict temporal ordering. Adaptation is achieved through a walk-forward scheme, in which model parameters are selected using rolling train--validation windows and subsequently applied out-of-sample. In this setting, the validation segment acts as an internal performance screen rather than as a statistical validation set, and no claims of generalization are inferred from it alone. The framework is evaluated on high-frequency financial time series as an experimentally accessible realization of a non-stationary complex system. Under a controlled zero-cost setting, the resulting dynamics exhibit a pronounced risk-reshaping effect, characterized by smoother trajectories and reduced drawdowns relative to direct exposure, and should be interpreted as an upper bound on achievable performance. These results illustrate how causal signal engineering can generate anticipatory structure in non-stationary systems without relying on non-causal information, explicit horizon labeling, or high-capacity predictive models.

q-fin.CP↗

arXiv2025

Forward-Oriented Causal Observables for Non-Stationary Financial Markets

We study short-horizon forecasting in financial time series under strict causal constraints, treating the market as a non-stationary stochastic system in which any predictive observable must be computable online from information available up to the decision time. Rather than proposing a machine-learning predictor or a direct price-forecast model, we focus on \emph{constructing} an interpretable causal signal from heterogeneous micro-features that encode complementary aspects of the dynamics (momentum, volume pressure, trend acceleration, and volatility-normalized price location). The construction combines (i) causal centering, (ii) linear aggregation into a composite observable, (iii) causal stabilization via a one-dimensional Kalman filter, and (iv) an adaptive ``forward-like'' operator that mixes the composite signal with a smoothed causal derivative term. The resulting observable is mapped into a transparent decision functional and evaluated through realized cumulative returns and turnover. An application to high-frequency EURUSDT (1-minute) illustrates that causally constructed observables can exhibit substantial economic relevance in specific regimes, while degrading under subsequent regime shifts, highlighting both the potential and the limitations of causal signal design in non-stationary markets.

q-fin.CP↗

arXiv2021

Short-range correlations effects on the deformability of neutron stars

In the present work, we investigate the effects of short-range correlations (SRC) on the dimensionless deformability of the binary neutron system related to the GW170817 event. We implemented phenomenological SRC in a relativistic mean-field model in which the bulk parameters, namely, incompressibility ($K_0$), effective nucleon mass ratio ($m^*$), symmetry energy ($J$) and its slope ($J_0$), are independently controlled. Our results point out that the SRC favor the model to pass through the constraints, established by the LIGO/Virgo Collaboration, on the values of $Λ_{1.4}$ and on the $Λ_1\timesΛ_2$ region. We also found a clear linear correlation between $Λ_{1.4}$ with $K_0$ and $L_0$ (increasing dependence), and with $m^*$ and $J$ (decreasing dependence). Finally, we also obtained compatible numbers for $R_{1.4}$ (model with and without SRC) in comparison with recent data from the neutron star interior composition explorer mission.

nucl-th↗

arXiv2020

Short-range correlation effects on the neutron star cooling

Short range correlations (SRC) have been known to be an important aspect of nuclear theory for some time. Recent works have re-ignited interest on this topic, particularly due to the fact that it has recently been demonstrated that SRC may be responsible for breaking pairing gaps in nuclear matter. In this work we revisit the concept of SRC for beta equilibrated matter in neutron stars. We construct two equivalent models, with and without SRC and proceed to investigate the thermal evolution of stars described by such models. We show that SRC play a major role in the thermal evolution of neutron stars. It will be shown that while the SRC largely leaves the macroscopic properties of the star unaltered, it significantly alters the proton fraction, thus leading to an early onset of the direct Urca (DU) process, which in turns leads to stars exhibiting much faster cooling.

nucl-th↗

arXiv2015

Core momentum distribution in two-neutron halo nuclei

The core momentum distribution of a weakly-bound neutron-neutron-core exotic nucleus is computed within a renormalized zero-range three-body model, with interactions in the s-wave channel. The halo wave-function in momentum space is obtained by using as inputs the two-body scattering lengths and the two-neutron separation energy. The core momentum densities are computed for $^{11}$Li, $^{14}$Be $^{20}$C and $^{22}$C. The model describes the experimental data for $^{11}$Li, $^{14}$Be and to some extend $^{20}$C. The recoil momentum distribution of the $^{20}$C from the breakup of $^{22}$C nucleus is computed for different two-neutron separation energies, and from the comparison with recent experimental data the two-neutron separation energy is estimated in the range $100\lesssim S_{2n}\lesssim 400$ KeV. The recoil momentum distribution depends weakly on the neutron-$^{20}$C scattering length, while the matter radius is strongly sensitive to it. The expected universality of the momentum distribution width is verified by also considering excited states for the system.

nucl-th↗