arXiv subjects

Bryan Zaldivar

Publications and source records attributed to Bryan Zaldivar.

At least 19 recordsLinked to original sources

arXiv2026

Cosmic string gravitational wave backgrounds at LISA: II. Reconstruction of conventional signals over astrophysical foregrounds

We study the reconstruction of conventional cosmic-string signals with LISA in the presence of all major known astrophysical foregrounds expected in the LISA band. These include stellar-origin black-hole binaries (SOBHBs), galactic (WDs) and extragalactic (ExWDs) white dwarfs, extreme-mass-ratio-inspirals (EMRIs), and massive black-hole binaries (MBHBs). Using the Simulation-based Inference package GWBackFinder, we perform a joint inference on the LISA noise, foregrounds, and signal, across a range of injected string tensions $G\mu$. We find that reconstructing tensions with an error $\lesssim 10\%$ requires values as large as $G\mu \gtrsim 10^{-11}$, i.e. a factor $\sim10^5$ larger than previous estimates with no foregrounds, and $\sim 10^2$ larger compared to estimates accounting only for SOBHB and WD foregrounds. This work is the second in a series initiated in Ref. arXiv:2508.05395, which aims to quantify LISA's ability to measure representative cosmic-string models.

astro-ph.CO↗

arXiv2025

Cosmic string gravitational wave backgrounds at LISA: I. Signal survey, template reconstruction, and model comparison

We present a catalog of gravitational wave background (GWB) signal templates from cosmic-string networks, based on relevant models proposed in the literature. We classify templates as conventional, based on standard cosmology and Nambu-Goto results (VOS and BOS), and beyond conventional, based on modifications of a) the loop number density (LRS, super, metastable, current-carrying strings), b) the expansion history (non-standard cosmologies, extra degrees of freedom, either thermal or secluded), or c) the loop properties (birth length, power emission). Using the SBI package $\texttt{GWBackFinder}$, we quantify the reconstruction precision of each signal by LISA, scanning over their parameter space, and performing model comparisons. For conventional signals, LISA reconstructs the tension $G\mu$ with an error $\lesssim 10\%$ for $G\mu \gtrsim 5\cdot 10^{-15}$, which decreases down to $2-3\%$ for $G\mu \gtrsim 10^{-12}$. BOS and VOS modelings become distinguishable confidently for $G\mu \gtrsim 5\cdot 10^{-13}$. For beyond-conventional signals, we identify SNR and error-threshold intervals for each parameter, and determine (for few examples) the regions where they can be distinguished from conventional signals. Analogous quality reconstruction studies of cosmic-string GWBs, superimposed over leading astrophysical foregrounds in the LISA window, will be presented in a series of upcoming papers.

astro-ph.CO↗

arXiv2025

Search for dark matter subhalos among unassociated Fermi-LAT sources in presence of dataset shift

We present a search for dark matter (DM) annihilating subhalos of the Milky Way halo among the {\Fermi} Large Area Telescope (LAT) unassociated sources. For this purpose, we construct the first statistical model of the unassociated sources at latitudes above 10 degrees, combining potential DM subhalos with Galactic and extragalactic astrophysical components. The distributions of astrophysical sources are constructed based on associated sources, while the DM subhalo distribution is derived from Monte Carlo simulations. We account for differences between associated and unassociated source distributions using a model with covariate and prior probability shifts, which are particular cases of more general dataset shifts. This approach is based on quantification learning, advancing beyond previous classify-and-count strategies by providing a well-defined statistical interpretation of the potential contribution from a DM subhalo population. For the $b\bar{b}$ annihilation channel and DM masses from 10 GeV to 1 TeV, we find no significant contribution from DM subhalos, and therefore derive 95% confidence upper limits on the annihilation cross section. Our analysis yields limits consistent with previous classify-and-count approaches, while the underlying generative model provides a more robust statistical framework, opening new avenues for population studies of Fermi-LAT sources and, more generally, for searches of anomalies, such as a new class of sources in addition to the known classes of sources, in presence of statistical and systematic uncertainties.

astro-ph.HE↗

arXiv2024

Simulation-based inference on warm dark matter from HERA forecasts

The redshifted 21cm signal from Cosmic Dawn promises to open a new window into the early history of our universe and enable the probing of an unprecedented comoving survey volume. In this work, we revisit the imprint of Warm Dark Matter (WDM) on the 21cm signal power spectrum using an updated implementation of the WDM effect in the public code $\texttt{21cmFast}$ and considering a single population of cosmic dawn galaxies. By focusing on inferring the WDM mass, we analyze the degeneracies between the latter and the astrophysics parameters characterizing star formation and X-ray heating and we emphasize the role of the threshold mass for star-forming galaxies, $M_{\rm turn}$. We study the capability of the recently built HERA telescope to reconstruct the WDM mass by adopting the statistical approach of simulation-based inference. We include a comparison of the per-parameter reconstruction quality for different number of simulations used in the training of the algorithm. Our results indicate that HERA could surpass current Lyman-$\alpha$ forest constraints if Cosmic Dawn galaxies exhibit a threshold mass $M_{\rm turn}\lesssim 10^{8}\, M_\odot$. The X-ray source properties considered in this study may also influence the strength of the WDM constraint for lower threshold masses.

astro-ph.CO↗

arXiv2024

Fast Likelihood-free Reconstruction of Gravitational Wave Backgrounds

We apply state-of-the-art, likelihood-free statistical inference (machine-learning-based) techniques for reconstructing the spectral shape of a gravitational wave background (GWB). We focus on the reconstruction of an arbitrarily shaped signal by the LISA detector, but the method can be easily extended to either template-dependent signals, or to other detectors, as long as a characterisation of the instrumental noise is available. As proof of the technique, we quantify the ability of LISA to reconstruct signals of arbitrary spectral shape (${\it blind}$ reconstruction), considering a diversity of frequency profiles, and including astrophysical backgrounds in some cases. As a teaser of how the method can reconstruct signals characterised by a parameter-dependent template (${\it template}$ reconstruction), we present a dedicated study for power-law signals. While our technique has several advantages with respect to traditional MCMC methods, we validate it with the latter for concrete cases. This work opens the door for both fast and accurate Bayesian parameter estimation of GWBs, with essentially no computational overhead during the inference step. Our set of tools are integrated into the package ${\tt GWBackFinder}$, which is publicly available in https://github.com/AndronikiDimitriou/GWBackFinder.

astro-ph.CO↗

arXiv2024

Deepening gamma-ray point-source catalogues with sub-threshold information

We propose a novel statistical method to extend Fermi-LAT catalogues of high-latitude $γ$-ray sources below their nominal threshold. To do so, we rely on a recent determination of the differential source-count distribution of sub-threshold sources via the application of deep learning methods to the $γ$-ray sky. By simulating ensembles of synthetic skies, we assess quantitatively the likelihood for pixels in the sky with relatively low-test statistics to be due to sources. Besides being useful to orient efforts towards multi-messenger and multi-wavelength identification of new $γ$-ray sources, we expect the results to be especially advantageous for statistical applications such as cross-correlation analyses.

astro-ph.HE↗

arXiv2024

The dependence of observables on action parameters

Many applications in Lattice field theory require to determine the Taylor series of observables with respect to action parameters. A primary example is the determination of electromagnetic corrections to hadronic processes. We show two possible solutions to this general problem, one based on reweigting, that can be considered a generalization of the RM123 method. The other based on the ideas of Numerical Stochastic Perturbation Theory (NSPT) in the Hamiltonian formulation. We show that 1) the NSPT-based approach shows a much reduced variance in the determination of the Taylor coefficients, and 2) That both approaches are related by a change of variables. Numerical results are shown for the case of $λ-ϕ^4$ in 4 dimensions, but we expect these observations to be general. We conclude by commenting on the possible use of Machine Learning techniques to find similar change of variables that can potentially reduce the variance in Taylor coefficients.

hep-lat↗

arXiv2023

Determining the Dark Matter distribution in galaxies with Deep Learning

We present a novel method to infer the Dark Matter (DM) content and spatial distribution within galaxies, based on convolutional neural networks trained within state-of-the-art hydrodynamical simulations (Illustris TNG100). The framework we have developed is capable of inferring the DM mass distribution within galaxies of mass $~10^{11}-10^{13}M_{\odot}$ with very high performance from the gravitationally baryon dominated internal regions to the DM-rich, baryon-depleted outskirts of the galaxies. With respect to traditional methods, the one presented here also possesses the advantages of not relying on a pre-assigned shape for the DM distribution, to be applicable to galaxies not necessarily in isolation, and to perform very well even in the absence of spectroscopic observations

astro-ph.GA↗

arXiv2023

Stochastic automatic differentiation for Monte Carlo processes

Monte Carlo methods represent a cornerstone of computer science. They allow to sample high dimensional distribution functions in an efficient way. In this paper we consider the extension of Automatic Differentiation (AD) techniques to Monte Carlo process, addressing the problem of obtaining derivatives (and in general, the Taylor series) of expectation values. Borrowing ideas from the lattice field theory community, we examine two approaches. One is based on reweighting while the other represents an extension of the Hamiltonian approach typically used by the Hybrid Monte Carlo (HMC) and similar algorithms. We show that the Hamiltonian approach can be understood as a change of variables of the reweighting approach, resulting in much reduced variances of the coefficients of the Taylor series. This work opens the door to find other variance reduction techniques for derivatives of expectation values.

hep-lat↗

arXiv2022

Function-space Inference with Sparse Implicit Processes

Implicit Processes (IPs) represent a flexible framework that can be used to describe a wide variety of models, from Bayesian neural networks, neural samplers and data generators to many others. IPs also allow for approximate inference in function-space. This change of formulation solves intrinsic degenerate problems of parameter-space approximate inference concerning the high number of parameters and their strong dependencies in large models. For this, previous works in the literature have attempted to employ IPs both to set up the prior and to approximate the resulting posterior. However, this has proven to be a challenging task. Existing methods that can tune the prior IP result in a Gaussian predictive distribution, which fails to capture important data patterns. By contrast, methods producing flexible predictive distributions by using another IP to approximate the posterior process cannot tune the prior IP to the observed data. We propose here the first method that can accomplish both goals. For this, we rely on an inducing-point representation of the prior IP, as often done in the context of sparse Gaussian processes. The result is a scalable method for approximate inference with IPs that can tune the prior IP parameters to the data, and that provides accurate non-Gaussian predictive distributions.

stat.ML↗

arXiv2020

Multi-class Gaussian Process Classification with Noisy Inputs

It is a common practice in the machine learning community to assume that the observed data are noise-free in the input attributes. Nevertheless, scenarios with input noise are common in real problems, as measurements are never perfectly accurate. If this input noise is not taken into account, a supervised machine learning method is expected to perform sub-optimally. In this paper, we focus on multi-class classification problems and use Gaussian processes (GPs) as the underlying classifier. Motivated by a data set coming from the astrophysics domain, we hypothesize that the observed data may contain noise in the inputs. Therefore, we devise several multi-class GP classifiers that can account for input noise. Such classifiers can be efficiently trained using variational inference to approximate the posterior distribution of the latent variables of the model. Moreover, in some situations, the amount of noise can be known before-hand. If this is the case, it can be readily introduced in the proposed methods. This prior information is expected to lead to better performance results. We have evaluated the proposed methods by carrying out several experiments, involving synthetic and real data. These include several data sets from the UCI repository, the MNIST data set and a data set coming from astrophysics. The results obtained show that, although the classification error is similar across methods, the predictive distribution of the proposed methods is better, in terms of the test log-likelihood, than the predictive distribution of a classifier based on GPs that ignores input noise.

stat.ML↗

arXiv2020

Dark matter constraints from dwarf galaxies with data-driven J-factors

We present an updated analysis of the gamma-ray flux from the directions of classical dwarf spheroidal galaxies, deriving new constraints on WIMP dark matter (DM) annihilation using a decade of Fermi-LAT data. Among the major novelties, we infer the dwarfs' J-factors by including new observations without imposing any a priori parametric profile for the DM distribution. While statistically compatible with results obtained from more conventional parameterisations, this procedure reduces the theoretical bias imposed on the data. Furthermore, we retain the full data-driven shape of the J-factors' empirical probability distributions when setting limits on DM, without imposing log-normality as is typically done. In conjunction with the data-driven J-factors, we use a new method for estimating the probability distribution function of the astrophysical background at the dwarf position, fully profiling over background uncertainties. We show that, for most classical dwarfs, the background systematic uncertainty dominates over the uncertainty on their J-factors. Raw distributions of J- and D-factors (the latter being the analogous of J-factors for decaying DM) are available upon request.

astro-ph.HE↗

arXiv2020

Interaction-free measurements and counterfactual computation in IBM quantum computers

The possibility of interaction-free measurements and counterfactual computations is a striking feature of quantum mechanics pointed out around 20 years ago. We implement such phenomena in actual 5-qubit, 15-qubit and 20-qubit IBM quantum computers by means of simple quantum circuits. The results are in general close to the theoretical expectations. For the larger circuits (with numerous gates and consequently larger errors) we implement a simple error mitigation procedure which improve appreciably the performance.

quant-ph↗

arXiv2020

Dark Matter Abundance from Sequential Freeze-in Mechanism

We present a thorough analysis of the sequential freeze-in mechanism for dark matter production in the early universe. In this mechanism the dark matter relic density results from pair annihilation of mediator particles which are themselves produced by thermal collisions of standard model particles. Below some critical value of the mediator coupling to standard model fields, this sequential channel dominates over the usual freeze-in where dark matter is directly produced from thermal collisions, even when the mediator is not in thermal equilibrium. The latter case requires computing the full non-thermal distribution of the mediators, for which finite temperature corrections are particularly important.

hep-ph↗

arXiv2019

Probing Dark Matter freeze-in with long-lived particle signatures: MATHUSLA, HL-LHC and FCC-hh

Collider searches for long-lived particles yield a promising avenue to probe the freeze-in production of Dark Matter via the decay of a parent particle. We analyze the prospects of probing the parameter space of Dark Matter freeze-in from the decay of neutral parent particles at the LHC and beyond, taking as a case study a freeze-in Dark Matter scenario via the Standard Model Higgs. We obtain the projected sensitivity of the proposed MATHUSLA surface detector (for MATHUSLA100 and MATHUSLA200 configurations) for long-lived particle searches to the freeze-in Dark Matter parameter space, and study its complementarity to searches by ATLAS and CMS at HL-LHC, as well as the interplay with constraints from Cosmology: Big-Bang Nucleosynthesis and Lyman-$α$ forest observations. We then analyze the improvement in sensitivity that would come from a forward detector within a future 100 TeV $pp$-collider. In addition, we discuss several technical aspects of the present Dark Matter freeze-in scenario: the role of the electroweak phase transition; the inclusion of thermal masses, which have been previously disregarded in freeze-in from decay studies; the impact of $2\to 2$ scattering processes on the Dark Matter relic abundance; and the interplay between freeze-in and super-WIMP Dark Matter production mechanisms.

hep-ph↗

arXiv2019

Long-Lived Particles at the Energy Frontier: The MATHUSLA Physics Case

We examine the theoretical motivations for long-lived particle (LLP) signals at the LHC in a comprehensive survey of Standard Model (SM) extensions. LLPs are a common prediction of a wide range of theories that address unsolved fundamental mysteries such as naturalness, dark matter, baryogenesis and neutrino masses, and represent a natural and generic possibility for physics beyond the SM (BSM). In most cases the LLP lifetime can be treated as a free parameter from the $μ$m scale up to the Big Bang Nucleosynthesis limit of $\sim 10^7$m. Neutral LLPs with lifetimes above $\sim$ 100m are particularly difficult to probe, as the sensitivity of the LHC main detectors is limited by challenging backgrounds, triggers, and small acceptances. MATHUSLA is a proposal for a minimally instrumented, large-volume surface detector near ATLAS or CMS. It would search for neutral LLPs produced in HL-LHC collisions by reconstructing displaced vertices (DVs) in a low-background environment, extending the sensitivity of the main detectors by orders of magnitude in the long-lifetime regime. In this white paper we study the LLP physics opportunities afforded by a MATHUSLA-like detector at the HL-LHC. We develop a model-independent approach to describe the sensitivity of MATHUSLA to BSM LLP signals, and compare it to DV and missing energy searches at ATLAS or CMS. We then explore the BSM motivations for LLPs in considerable detail, presenting a large number of new sensitivity studies. While our discussion is especially oriented towards the long-lifetime regime at MATHUSLA, this survey underlines the importance of a varied LLP search program at the LHC in general. By synthesizing these results into a general discussion of the top-down and bottom-up motivations for LLP searches, it is our aim to demonstrate the exceptional strength and breadth of the physics case for the construction of the MATHUSLA detector.

hep-ph↗

arXiv2018

micrOMEGAs5.0 : freeze-in

We present a major upgrade of the micrOMEGAs dark matter code to compute the abundance of feebly interacting dark matter candidates through the freeze-in mechanism in generic extensions of the Standard Model of particle physics. We develop the necessary formalism in order to solve the freeze-in Boltzmann equations while making as few simplifying assumptions as possible concerning the phase-space distributions of the particles involved in the dark matter production process. We further show that this formalism allows us to treat different freeze-in scenarios and discuss the way it is implemented in the code. We find that, depending on the New Physics scenario under consideration, the effect of a proper treatment of statistics on the predicted dark matter abundance can range from a few percent up to a factor of two, or more. We moreover illustrate the underlying physics, as well as the various novel functionalities of micrOMEGAs, by presenting several example results obtained for different dark matter models.

hep-ph↗

arXiv2018

Dark matter constraints from dwarf galaxies: a data-driven analysis

Dwarf galaxies represent a powerful probe of annihilating dark matter particle models, with gamma-ray data setting some of the best bounds available. A major issue in improving over existing constraints consists in the limited knowledge of the astrophysical background (mostly diffuse photons, but also unresolved sources). Perhaps more worrisome, several approaches in the literature suffer of the difficulty of assessing the systematic error due to background mis-modelling. Here we propose a data-driven method to estimate the background at the dwarf position and its uncertainty, relying on an appropriate use of the whole-sky data, via an optimisation procedure of the interpolation weights. While this article is mostly methodologically oriented, we also report the bounds based on latest Fermi-LAT data and updated information for J-factors for both isolated and stacked dwarfs. Our results are very competitive with the Fermi-LAT ones, while being derived with a more general and flexible method. We discuss the impact of profiling over the J-factor as well as over the background probability distribution function, with the latter resulting for instance crucial in drawing conclusions of compatibility with DM interpretations of the so-called Galactic Centre Excess.

astro-ph.HE↗