arXiv subjects

Carlos Blanco

Publications and source records attributed to Carlos Blanco.

At least 19 recordsLinked to original sources

arXiv2026

Solar Spin-Dependent Dark Matter-Neutron Cross Section Constraints: The Lost Case

The Sun has long served as a natural dark matter detector, capturing halo particles that scatter with solar nuclei and electrons and subsequently produce indirect signals from dark matter annihilation that can be probed by either neutrino or $\gamma$-ray instruments. Previous efforts have computed cross-section constraints for dark matter scattering with electrons, as well as spin-independent and spin-dependent dark matter-proton scattering. However, the spin-dependent dark matter-neutron scattering scenario has been neglected, despite its importance in direct detection. For the first time, we compute the capture and evaporation rates for spin-dependent dark matter-neutron scattering in the Sun, including odd-neutron isotope targets in the current standard solar model. Comparing the predicted annihilation signals against current neutrino and $\gamma$-ray observations, and projecting the reach of upcoming detectors, we find that solar constraints exceed direct detection limits for several annihilation channels. For models in which the dark matter annihilates into long-lived mediators, these constraints can extend below the neutrino fog.

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arXiv2026

Towards Quantum-Dot Detectors as Barcodes for Dark Matter Interactions

Quantum dots are tunable semiconductor nanocrystals that can be produced at industrial scales. We present the first ab initio calculation of the scattering of dark matter on electrons bound in quantum dots. The momentum-dependence of a quantum dot's electronic response depends on its morphology and on the dark matter mass, interaction operator, mediator coupling, and mediator mass. Therefore, the relative rates across an array of distinct quantum dot targets form a ``barcode'' that carries information about the nature of the dark matter interaction. We project the sensitivity of a detector concept in which a collection of independent target subunits, each loaded with silicon quantum dots of a particular morphology, are read out by Skipper CCDs. Given a future signal, this barcode could discriminate between interaction operators and mediator types. We quantify the discrimination power for a benchmark pair of models as a function of readout noise and exposure.

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arXiv2026

Ubiquitous Corotation of Dark Matter Halos: Implications for Direct Detection

Cosmological simulations have recently begun to quantify the halo-to-halo variance in the phase-space distribution of dark matter around the Sun. We use a sample of nearly one hundred Milky Way-like galaxies from the TNG50 simulation to determine what aspects of this variance control the predictions for dark matter direct detection. Contrary to the isotropy assumed in the standard halo model, we find the dark matter median azimuthal velocity is nonzero and preferentially corotating, i.e., in the direction of the baryonic disk's rotation, ranging from 6-70 km/s (16th-84th percentile). This corotation suppresses predicted scattering rates in laboratory experiments searching for dark matter lighter than 50 GeV and significantly affects the expected daily modulation amplitude for directional detectors. In particular, this induces a 21% uncertainty on the upper limit of the dark matter-nucleon interaction cross section at peak sensitivity for a typical isotropic ton-scale experiment. This uncertainty is not irreducible, however: it is strongly correlated with the rotational velocity. If studies of the Milky Way's formation history determine the rotation speed, this astrophysical uncertainty is reduced to 7%.

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arXiv2026

Statistics of Daily Modulation in Dark Matter Direct Detection Experiments

The time-dependent modulation of the event rate in dark matter direct detection experiments, arising from the motion of the Earth with respect to the Galactic rest frame, is a distinctive signature whose observation is crucial for claiming a discovery of dark matter. While annual modulation has been well studied for decades, daily modulation due to the Earth's rotation has attracted increased attention recently due to the identification of anisotropic solid-state detector materials that yield a direction-dependent scattering rate without sacrificing the overall rate. We perform a statistical analysis of daily modulation in dark matter scattering experiments, with the goal of maximizing the statistical significance of a modulating signal in the presence of an unknown background rate, which may be either flat (non-modulating), or modulating over a 24-hour period with a known or unknown phase. In the background-dominated regime, we find that the discovery significance scales as $f_\text{RMS} \sqrt{T}$, where $T$ is the total exposure time and $f_\text{RMS}$ is the root-mean-square modulation amplitude; in particular, the significance continues to improve with exposure rather than saturating due to systematic uncertainties in the background rate. Using anisotropic trans-stilbene detectors for sub-GeV dark matter as a benchmark example, we provide prescriptions for optimizing the significance for a given total detector mass and location. In an example analysis using three detectors, optimizing the detector orientations can reduce the required exposure by a factor of $\sim 5$ for a desired discovery or exclusion significance, even after profiling over an unknown modulating background phase.

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arXiv2025

A Numerical Method for the Efficient Calculation of Scattering Form Factors

Scintillating molecular crystals have emerged as prime candidates for directional dark matter detector targets. This anisotropy makes them exquisitely sensitive due to the daily modulation induced by the directional dark matter wind. However, predicting the interaction rate for arbitrary molecules requires accurate modeling of the many-body ground as well as excited states, a task that has been historically computationally expensive. Here, we present a theory and computational framework for efficiently computing dark matter scattering form factors for molecules. We introduce SCarFFF, a GPU-accelerated code to compute the fully three-dimensional anisotropic molecular form factor for arbitrary molecules. We use a full time-dependent density functional theory framework to compute the lowest-lying singlet excited states, adopting the B3YLP exchange functional and a double-zeta Gaussian basis set. Once the many-body electronic structure is computed, the form factors are computed in a small fraction of the time from the transition density matrix. We show that ScarFFF can compute the first 12 form factors for a molecule of 10 heavy atoms in approximately 5 seconds, opening the door to accurate, high-throughput material screening for optimal directional dark matter detector targets. Our code can perform the calculation in three independent ways, two semi-analytical and one fully numeric, providing optimised methods for every precision goal.

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arXiv2025

Sub-GeV Dark Matter Detection with Dark Rates in Liquid Scintillators

It was recently shown that standard sub-GeV dark matter candidates can be effectively probed by large neutrino observatories via annual modulation of the total photomultiplier hit rate. That work focused on the production of light by the excitation of scintillator molecules and considered the JUNO detector, surpassing limits from dedicated dark-matter detectors and reaching theoretical targets. Here, we significantly generalize that work, now also taking into account ionization channels and extending the analysis to other liquid-scintillator detectors, including SNO+, Daya Bay, Borexino, and KamLAND. Last, we present a call to action: with multiple detectors achieving competitive sensitivity, there is an opportunity to validate this new technique across experiments and to refine it using each detector's strengths.

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arXiv2025

Using Deep Learning for Robust Classification of Fast Radio Bursts

While the nature of fast radio bursts (FRBs) remains unknown, population-level analyses can elucidate underlying structure in these signals. In this study, we employ deep learning methods to both classify FRBs and analyze structural patterns in the latent space learned from the first CHIME catalog. We adopt a Supervised Variational Autoencoder (sVAE) architecture which combines the representational learning capabilities of Variational Autoencoders (VAEs) with a supervised classification task, thereby improving both classification performance and the interpretability of the latent space. We construct a learned latent space in which we perform further dimensionality reduction to find underlying structure in the data. Our results demonstrate that the sVAE model achieves high classification accuracy for FRB repeaters and reveals separation between repeater and non-repeater populations. Upon further analysis of the latent space, we observe that dispersion measure excess, spectral index, and spectral running are the dominant features distinguishing repeaters from non-repeaters. We also identify four non-repeating FRBs as repeater candidates, two of which have been independently flagged in previous studies.

astro-ph.HE↗

arXiv2025

Physics-Informed Neural Networks with Fourier Features and Attention-Driven Decoding

Physics-Informed Neural Networks (PINNs) are a useful framework for approximating partial differential equation solutions using deep learning methods. In this paper, we propose a principled redesign of the PINNsformer, a Transformer-based PINN architecture. We present the Spectral PINNSformer (S-Pformer), a refinement of encoder-decoder PINNSformers that addresses two key issues; 1. the redundancy (i.e. increased parameter count) of the encoder, and 2. the mitigation of spectral bias. We find that the encoder is unnecessary for capturing spatiotemporal correlations when relying solely on self-attention, thereby reducing parameter count. Further, we integrate Fourier feature embeddings to explicitly mitigate spectral bias, enabling adaptive encoding of multiscale behaviors in the frequency domain. Our model outperforms encoder-decoder PINNSformer architectures across all benchmarks, achieving or outperforming MLP performance while reducing parameter count significantly.

cs.LG↗

arXiv2025

The Impact of Muon and Pion Cooling on the Neutrino Spectrum of NGC 1068

The IceCube Neutrino Observatory has detected a flux of $\sim 1-10 \, {\rm TeV}$ neutrinos from the active galaxy, NGC 1068. The soft spectral index of these neutrinos has previously been interpreted as an indication that this source accelerates protons only up to energies of several hundred TeV. Here, we propose that this source might instead accelerate protons to significantly higher energies, but that the charged pions and muons produced in their interactions undergo significant synchrotron energy losses before they can decay, leading to a cutoff in the neutrino spectrum at TeV-scale energies. This scenario would require very strong magnetic fields to be present in the acceleration region of NGC 1068, on the order of $B \sim 10^7 \, {\rm G}$. We point out that this synchrotron cooling would impact the flavor ratios of the neutrinos from this source, providing a means to test this scenario with future very-large volume neutrino telescopes.

astro-ph.HE↗

arXiv2025

Complementary Planetary Spectroscopy Probes of Dark Matter

We investigate dark matter (DM) interactions via spectroscopic signatures of energy injection in planetary environments. We develop a general framework to account for how DM energy injection signals depend on the DM spatial distribution, planetary structure, and DM energy deposition profile. We combine UV airglow data on the Solar System's gas giants from the Voyager and New Horizons flybys, and ionospheric measurements from AMS-02 and ELFIN CubeSat on Earth, with internal heat flow data from Cassini, Voyager, and terrestrial boreholes, to constrain DM-nucleon scattering across both heavy and light mediator scenarios. We show that Earth, gas giants, and ice giants probe complementary DM masses and mediator properties, and forecast the reach of a free-floating Super-Jupiter. These results establish planetary spectroscopy as a powerful and versatile probe of the dark sector, complementary to direct detection, cosmology, and collider searches.

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arXiv2025

Searching for Axion Dark Matter Near Relaxing Magnetars

Axion dark matter passing through the magnetospheres of magnetars can undergo hyper-efficient resonant mixing with low-energy photons, leading to the production of narrow spectral lines that could be detectable on Earth. Since this is a resonant process triggered by the spatial variation in the photon dispersion relation, the luminosity and spectral properties of the emission are highly sensitive to the charge and current densities permeating the magnetosphere. To date, a majority of the studies investigating this phenomenon have assumed a perfectly dipolar magnetic field structure with a near-field plasma distribution fixed to the minimal charge-separated force-free configuration. While this {may} be a reasonable treatment for the closed field lines of conventional radio pulsars, the strong magnetic fields around magnetars are believed to host processes that drive strong deviations from this minimal configuration. In this work, we study how realistic magnetar magnetospheres impact the electromagnetic emission produced from axion dark matter. Specifically, we construct charge and current distributions that are consistent with magnetar observations, and use these to recompute the prospective sensitivity of radio and sub-mm telescopes to axion dark matter. We demonstrate that the two leading models yield vastly different predictions for the frequency and amplitude of the spectral line, indicating systematic uncertainties in the plasma structure are significant. Finally, we discuss various observational signatures that can be used to differentiate the local plasma loading mechanism of an individual magnetar, which will be necessary if there is hope of using such objects to search for axions.

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arXiv2025

Dark Matter Velocity Distributions for Direct Detection: Astrophysical Uncertainties are Smaller Than They Appear

The sensitivity of direct detection experiments depends on the phase-space distribution of dark matter near the Sun, which can be modeled theoretically using cosmological hydrodynamical simulations of Milky Way-like galaxies. However, capturing the halo-to-halo variation in the local dark matter speeds -- a necessary step for quantifying the astrophysical uncertainties that feed into experimental results -- requires a sufficiently large sample of simulated galaxies, which has been a challenge. In this Letter, we quantify this variation with nearly 100 Milky Way-like galaxies from the TNG50 simulation, the largest sample to date at this resolution. Moreover, we introduce a novel phase-space scaling procedure that endows every system with a reference frame that accurately reproduces the local standard-of-rest speed of our Galaxy, providing a principled way of extrapolating the simulation results to real-world data. The ensemble of predicted speed distributions is well characterized by the standard halo model, a Maxwell-Boltzmann distribution truncated at the escape speed, though the individual distributions can deviate from it, especially at high speeds. The dark matter-nucleon cross section limits placed by these speed distributions vary by ~60% about the median. This places the 1-sigma astrophysical uncertainty at or below the level of the systematic uncertainty of current ton-scale detectors, even down to the energy threshold. The predicted uncertainty remains unchanged when subselecting on those TNG50 galaxies with merger histories similar to the Milky Way. Tabulated speed distributions, as well as Maxwell-Boltzmann fits, are provided for use in computing direct detection bounds or projecting sensitivities.

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arXiv2025

Looking for the {\gamma}-Ray Cascades of the KM3-230213A Neutrino Source

The extreme energy of the KM3-230213A event could transform our understanding of the most energetic sources in the Universe. However, it also reveals an inconsistency between the KM3NeT detection and strong IceCube constraints on the ultra-high energy neutrino flux. The most congruous explanation for the KM3NeT and IceCube data requires KM3-230213A to be produced by a (potentially transient) source fortuitously located in a region where the KM3NeT acceptance is maximized. In hadronic models of ultra-high-energy neutrino production, such a source would also produce a bright {\gamma}-ray signal, which would cascade to GeV--TeV energies due to interactions with extragalactic background light. We utilize the {\gamma}-Cascade package to model the spectrum, spatial extension, and time-delay of such a source, and scan a region surrounding the KM3NeT event to search for a consistent {\gamma}-ray signal. We find no convincing evidence for a comparable \textit{Fermi}-LAT source and place constraints on a combination of the source redshift and the intergalactic magnetic field strength between the source and Earth.

astro-ph.HE↗

arXiv2024

Deep learning optimal molecular scintillators for dark matter direct detection

Direct searches for sub-GeV dark matter are limited by the intrinsic quantum properties of the target material. In this proof-of-concept study, we argue that this problem is particularly well suited for machine learning. We demonstrate that a simple neural architecture consisting of a variational autoencoder and a multi-layer perceptron can efficiently generate unique molecules with desired properties. In specific, the energy threshold and signal (quantum) efficiency determine the minimum mass and cross section to which a detector can be sensitive. Organic molecules present a particularly interesting class of materials with intrinsically anisotropic electronic responses and $\mathcal{O}$(few) eV excitation energies. However, the space of possible organic compounds is intractably large, which makes traditional database screening challenging. We adopt excitation energies and proxy transition matrix elements as target properties learned by our network. Our model is able to generate molecules that are not in even the most expansive quantum chemistry databases and predict their relevant properties for high-throughput and efficient screening. Following a massive generation of novel molecules, we use clustering analysis to identify some of the most promising molecular structures that optimise the desired molecular properties for dark matter detection.

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arXiv2024

Search for Dark Matter Induced Airglow in Planetary Atmospheres

We point out that dark matter can illuminate planetary skies via ultraviolet airglow. Dark matter annihilation products can excite molecular hydrogen, which then deexcites to produce ultraviolet emission in the Lyman and Werner bands. We search for this new effect by analyzing nightside ultraviolet radiation data from Voyager and New Horizons flybys of Neptune, Uranus, Saturn, and Jupiter. We set new constraints on the dark matter-nucleon scattering cross section for DM above the electron mass; these extend down to $10^{-40}~$cm$^2$ for DM masses around 1 GeV when most of the energy is deposited in the atmosphere. We highlight that future ultraviolet airglow measurements of Solar System planets or other worlds provide a new dark matter discovery avenue.

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arXiv2024

$\gamma$-Cascade V4: A Semi-Analytical Code for Modeling Cosmological Gamma-Ray Propagation

Since the universe is not transparent to gamma rays with energies above around one hundred GeV, it is necessary to account for the interaction of high-energy photons with intergalactic radiation fields in order to model gamma-ray propagation. Here, we present a public numerical software for the modeling of gamma-ray observables. This code computes the effects on gamma-ray spectra from the development of electromagnetic cascades and cosmological redshifting. The code introduced here is based on the original $\gamma$-Cascade, and builds on it by improving its performance at high redshifts, introducing new propagation modules, and adding many more extragalactic radiation field models, which enables the ability to estimate the uncertainties inherent to EBL modeling. We compare the results of this new code to existing Monte Carlo electromagnetic transport models, finding good agreement within EBL uncertainties.

astro-ph.HE↗

arXiv2023

Search for Dark Matter Ionization on the Night Side of Jupiter with Cassini

We present a new search for dark matter using planetary atmospheres. We point out that annihilating dark matter in planets can produce ionizing radiation, which can lead to excess production of ionospheric $H_3^+$. We apply this search strategy to the night side of Jupiter near the equator. The night side has zero solar irradiation, and low latitudes are sufficiently far from ionizing auroras, leading to an effectively background-free search. We use Cassini data on ionospheric $H_3^+$ emission collected 3 hours either side of Jovian midnight, during its flyby in 2000, and set novel constraints on the dark matter-nucleon scattering cross section down to about $10^{-38}$ cm$^2$. We also highlight that dark matter atmospheric ionization may be detected in Jovian exoplanets using future high-precision measurements of planetary spectra.

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arXiv2023

Sensitivity of JWST to eV-Scale Decaying Axion Dark Matter

The recently-launched James Webb Space Telescope (JWST) can resolve eV-scale emission lines arising from dark matter (DM) decay. We forecast the end-of-mission sensitivity to the decay of axions, a leading DM candidate, in the Milky Way using the blank-sky observations expected during standard operations. Searching for unassociated emission lines will constrain axions in the mass range $0.18$ eV to $2.6$ eV with axion-photon couplings $g_{a\gamma\gamma}\gtrsim 5.5 \times 10^{-12}$ GeV$^{-1}$. In particular, these results will constrain astrophobic QCD axions to masses $\lesssim$ 0.2 eV.

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