Search

We perform real-time simulation of fermion-bubble scattering during a first order phase transition by which the fermions become massive. This out-of-equilibrium dynamics can generate a CP asymmetry, which is a crucial ingredient for baryon asymmetry generation in the early Universe. As a prototype, we consider a 1+1-dimensional system, for which CP is replaced by charge conjugation C. We use tensor network methods to study the C asymmetry generation outside the bubble wall induced by a complex fermion mass profile. In the asymptotic region, where reflected particles are far from the scattering point, our lattice calculations are in good agreement with perturbative calculations, but are also applicable in the nonpertubative regime. Real-time evolution of the instantaneous asymmetry generation near the collision point is also accessible within our framework and can be an order of magnitude larger than the asymptotic value. This intriguing feature may have far-reaching consequences in a full model calculation of electroweak baryogenesis. Our studies provide a necessary step for guiding quantum simulations of early universe phase transitions., Comment: 12 pages, 3 figures

The search for weakly interacting matter particles (WIMPs) is one of the main objectives of the High Luminosity Large Hadron Collider (HL-LHC). In this work we use Machine-Learning (ML) techniques to explore WIMP radiative decays into a Dark Matter (DM) candidate in a supersymmetric framework. The minimal supersymmetric WIMP sector includes the lightest neutralino that can provide the observed DM relic density through its co-annihilation with the second lightest neutralino and lightest chargino. Moreover, the direct DM detection cross section rates fulfill current experimental bounds and provide discovery targets for the same region of model parameters in which the radiative decay of the second lightest neutralino into a photon and the lightest neutralino is enhanced. This strongly motivates the search for radiatively decaying neutralinos which, however, suffers from strong backgrounds. We investigate the LHC reach in the search for these radiatively decaying particles by means of cut-based and ML methods and estimate its discovery potential in this well-motivated, new physics scenario., Comment: 24 pages + references, 8 figures, 3 tables, 3 appendices. Version updated with published LZ results, appendix reserved for preliminary LZ results deleted

Strongly lensed quasars can be used to constrain cosmological parameters through time-delay cosmography. Models of the lens masses are a necessary component of this analysis. To enable time-delay cosmography from a sample of $\mathcal{O}(10^3)$ lenses, which will soon become available from surveys like the Rubin Observatory's Legacy Survey of Space and Time (LSST) and the Euclid Wide Survey, we require fast and standardizable modeling techniques. To address this need, we apply neural posterior estimation (NPE) for modeling galaxy-scale strongly lensed quasars from the Strong Lensing Insights into the Dark Energy Survey (STRIDES) sample. NPE brings two advantages: speed and the ability to implicitly marginalize over nuisance parameters. We extend this method by employing sequential NPE to increase precision of mass model posteriors. We then fold individual lens models into a hierarchical Bayesian inference to recover the population distribution of lens mass parameters, accounting for out-of-distribution shift. After verifying our method using simulated analogs of the STRIDES lens sample, we apply our method to 14 Hubble Space Telescope single-filter observations. We find the population mean of the power-law elliptical mass distribution slope, $\gamma_{\text{lens}}$, to be $\mathcal{M}_{\gamma_{\text{lens}}}=2.13 \pm 0.06$. Our result represents the first population-level constraint for these systems. This population-level inference from fully automated modeling is an important stepping stone towards cosmological inference with large samples of strongly lensed quasars.

Strong gravitational lenses are a singular probe of the universe's small-scale structure $\unicode{x2013}$ they are sensitive to the gravitational effects of low-mass $(<10^{10} M_\odot)$ halos even without a luminous counterpart. Recent strong-lensing analyses of dark matter structure rely on simulation-based inference (SBI). Modern SBI methods, which leverage neural networks as density estimators, have shown promise in extracting the halo-population signal. However, it is unclear whether the constraining power of these models has been limited by the methodology or the information content of the data. In this study, we introduce an accelerator-optimized simulation pipeline that can generate lens images with realistic subhalo populations in a matter of milliseconds. Leveraging this simulator, we identify the main methodological limitation of our fiducial SBI analysis: training set size. We then adopt a sequential neural posterior estimation (SNPE) approach, allowing us to iteratively refine the distribution of simulated training images to better align with the observed data. Using only one-fifth as many mock Hubble Space Telescope (HST) images, SNPE matches the constraints on the low-mass halo population produced by our best non-sequential model. Our experiments suggest that an over three order-of-magnitude increase in training set size and GPU hours would be required to achieve an equivalent result without sequential methods. While the full potential of the existing strong lens sample remains to be explored, the notable improvement in constraining power enabled by our sequential approach highlights that the current constraints are limited primarily by methodology and not the data itself. Moreover, our results emphasize the need to treat training set generation and model optimization as interconnected stages of any cosmological analysis using simulation-based inference techniques., Comment: Code available at https://github.com/swagnercarena/paltax

The resummation of large thermal corrections to the effective potential is mandatory for the accurate prediction of phase transitions. We discuss the accuracy of different prescriptions to perform this resummation at the one- and two-loop level and point out conceptual issues that appear when using a high-temperature expansion at the two-loop level. Moreover, we show how a particular prescription called partial dressing, which does not rely on a high-temperature expansion, consistently avoids these issues. We introduce a novel technique to apply this resummation method to the case of multiple mixing fields. Our approach significantly extends the range of applicability of the partial dressing prescription, making it suitable for phenomenological studies of beyond the Standard Model extensions of the Higgs sector., Comment: 56 pages, 14 figures; version accepted for publication in JHEP

In the quantum simulation of lattice gauge theories, gauge symmetry can be either fixed or encoded as a redundancy of the Hilbert space. While gauge-fixing reduces the number of qubits, keeping the gauge redundancy can provide code space to mitigate and correct quantum errors by checking and restoring Gauss's law. In this work, we consider the correctable errors for generic finite gauge groups and design the quantum circuits to detect and correct them. We calculate the error thresholds below which the gauge-redundant digitization with Gauss's law error correction has better fidelity than the gauge-fixed digitization. Our results provide guidance for fault-tolerant quantum simulations of lattice gauge theories., Comment: 16 pages, 7 figures

Designing and optimizing optical amplifiers to maximize system performance is becoming increasingly important as optical communication systems strive to increase throughput. Offline optimization of optical amplifiers relies on models ranging from white-box models deeply rooted in physics to black-box data-driven and physics-agnostic models. Here, we compare the capabilities of white-, grey- and black-box models on the challenging test case of optimizing a bidirectional distributed Raman amplifier to achieve a target frequency-distance signal power profile. We show that any of the studied methods can achieve similar frequency and distance flatness of between 1 and 3.6 dB (depending on the definition of flatness) over the C-band in an 80-km span. Then, we discuss the models' applicability, advantages, and drawbacks based on the target application scenario, in particular in terms of flexibility, optimization speed, and access to training data.

We study information-theoretic properties of scalar models containing two Higgs doublets $\Phi_a$, where $a=1,2$ is the flavor quantum number. Considering the 2-to-2 scattering $\Phi_a \Phi_b \to \Phi_c \Phi_d$ as a two-qubit system in the flavor subspace and the S-matrix as a quantum logic gate, we analyze the entanglement power of the S-matrix at the tree-level, in the limit the gauge coupling is turned off. Demanding the suppression of flavor entanglement during the scattering, the perturbative S-matrix in the broken phase can only be in the equivalent class of the Identity gate and the scalar potential exhibits a maximally enhanced $SO(8)$ symmetry acting on the 8 real components of the two doublets. The $SO(8)$ symmetry leads to the alignment limit naturally, giving rise to a Standard-Model-like Higgs boson as a consequence of entanglement suppression., Comment: 8 pages, 1 figure

We show that simultaneously explaining dark matter and the observed value of the muon's magnetic dipole moment may lead to yet unexplored photon signals at the LHC. We consider the Minimal Supersymmetric Standard Model with electroweakino masses in the few-to-several hundred GeV range, and opposite sign of the Bino mass parameter with respect to both the Higgsino and Wino mass parameters. In such region of parameter space, the spin-independent elastic scattering cross section of a Bino-like dark matter candidate in direct detection experiment is suppressed by cancellations between different amplitudes, and the observed dark matter relic density can be realized via Bino-Wino co-annihilation. Moreover, the observed value of the muon's magnetic dipole moment can be explained by Bino and Wino loop contributions. Interestingly, "radiative" decays of Wino-like neutralinos into the lightest neutralino and a photon are enhanced, whereas decays into leptons are suppressed. While these decay patterns weaken the reach of multi-lepton searches at the LHC, the radiative decay opens a new window for probing dark matter at the LHC through the exploration of parameter space regions beyond those currently accessible. To complement the current electroweakino searches, we propose searching for a single (soft) photon plus missing transverse energy, accompanied by a hard initial state radiation jet.

We present a Bayesian graph neural network (BGNN) that can estimate the weak lensing convergence ($\kappa$) from photometric measurements of galaxies along a given line of sight. The method is of particular interest in strong gravitational time delay cosmography (TDC), where characterizing the "external convergence" ($\kappa_{\rm ext}$) from the lens environment and line of sight is necessary for precise inference of the Hubble constant ($H_0$). Starting from a large-scale simulation with a $\kappa$ resolution of $\sim$1$'$, we introduce fluctuations on galaxy-galaxy lensing scales of $\sim$1$''$ and extract random sightlines to train our BGNN. We then evaluate the model on test sets with varying degrees of overlap with the training distribution. For each test set of 1,000 sightlines, the BGNN infers the individual $\kappa$ posteriors, which we combine in a hierarchical Bayesian model to yield constraints on the hyperparameters governing the population. For a test field well sampled by the training set, the BGNN recovers the population mean of $\kappa$ precisely and without bias, resulting in a contribution to the $H_0$ error budget well under 1\%. In the tails of the training set with sparse samples, the BGNN, which can ingest all available information about each sightline, extracts more $\kappa$ signal compared to a simplified version of the traditional method based on matching galaxy number counts, which is limited by sample variance. Our hierarchical inference pipeline using BGNNs promises to improve the $\kappa_{\rm ext}$ characterization for precision TDC. The implementation of our pipeline is available as a public Python package, Node to Joy., Comment: 15 pages, 8 figures (+ 6 pages, 2 figures in Appendix). Submitted to ApJ. Code at https://github.com/jiwoncpark/node-to-joy

We investigate the electroweak phase transition patterns for a recently proposed baryogenesis model with CP violation originated in the dark sector. The model includes a complex scalar singlet-Higgs boson portal, a $U(1)_l$ gauge lepton symmetry with a $Z^\prime$ gauge boson portal and a fermionic dark matter particle. We find a novel thermal history of the scalar sector, featuring a $Z_2$ breaking singlet vacuum in the early Universe driven by a dark Yukawa coupling, that induces a one-step strongly first order electroweak phase transition. We explore the parameter space that generates the observed matter-antimatter asymmetry and dark matter relic abundance, while being consistent with constraints from electric dipole moment and collider searches, and dark matter direct detection bounds. The complex singlet can be produced via the Higgs portal and decays into Standard Model particles after traveling a certain distance. We explore the reach for long-lived singlet scalars at the {\unit[13]{TeV}} Large Hadron Collider with $\mathcal{L}=\unit[139]{fb^{-1}}$ and show its impact on the parameter space of the model. Setting aside currently unresolved theoretical uncertainties, we estimate the gravitational wave signatures detectable at future observatories.

An unprecedented comparison of closed-form incoherent GN (InGN) models is presented with heterogeneous spans and partially loaded links in elastic optical networks. Results reveal that with accumulated dispersion correction and modulation format terms, the InGN shows higher accuracy.

Two Higgs doublet models (2HDM) provide the low energy effective field theory (EFT) description in many well motivated extensions of the Standard Model. It is therefore relevant to study their properties, as well as the theoretical constraints on these models. In this article we concentrate on three relevant requirements for the validity of the 2HDM framework, namely the perturbative unitarity bounds, the bounded from below constraints, and the vacuum stability constraints. In this study, we concentrate on the most general renormalizable version of the 2HDM -- without imposing any parity symmetry, which may be violated in many UV extensions. We derive novel analytical expressions that generalize those previously obtained in more restrictive scenarios to the most general case. We also discuss the phenomenological implications of these bounds, focusing on CP violation., Comment: 39 pages, 6 figures; v2: matches published version

We experimentally validate a real-time machine learning framework, capable of controlling the pump power values of Raman amplifiers to shape the signal power evolution in two-dimensions (2D): frequency and fiber distance. In our setup, power values of four first-order counter-propagating pumps are optimized to achieve the desired 2D power profile. The pump power optimization framework includes a convolutional neural network (CNN) followed by differential evolution (DE) technique, applied online to the amplifier setup to automatically achieve the target 2D power profiles. The results on achievable 2D profiles show that the framework is able to guarantee very low maximum absolute error (MAE) (<0.5 dB) between the obtained and the target 2D profiles. Moreover, the framework is tested in a multi-objective design scenario where the goal is to achieve the 2D profiles with flat gain levels at the end of the span, jointly with minimum spectral excursion over the entire fiber length. In this case, the experimental results assert that for 2D profiles with the target flat gain levels, the DE obtains less than 1 dB maximum gain deviation, when the setup is not physically limited in the pump power values. The simulation results also prove that with enough pump power available, better gain deviation (less than 0.6 dB) for higher target gain levels is achievable.

This is the Snowmass2021 Energy Frontier (EF) Beyond the Standard Model (BSM) report. It combines the EF topical group reports of EF08 (Model-specific explorations), EF09 (More general explorations), and EF10 (Dark Matter at Colliders). The report includes a general introduction to BSM motivations and the comparative prospects for proposed future experiments for a broad range of potential BSM models and signatures, including compositeness, SUSY, leptoquarks, more general new bosons and fermions, long-lived particles, dark matter, charged-lepton flavor violation, and anomaly detection., Comment: 108 pages + 38 pages references and appendix, 37 figures, Report of the Topical Group on Beyond the Standard Model Physics at Energy Frontier for Snowmass 2021. The first nine authors are the Conveners, with Contributions from the other authors

We present Symphony, a compilation of $262$ cosmological, cold-dark-matter-only zoom-in simulations spanning four decades of host halo mass, from $10^{11}$$-$$10^{15}~M_{\mathrm{\odot}}$. This compilation includes three existing simulation suites at the cluster and Milky Way$-$mass scales, and two new suites: $39$ Large Magellanic Cloud-mass ($10^{11}~M_{\mathrm{\odot}}$) and $49$ strong-lens-analog ($10^{13}~M_{\mathrm{\odot}}$) group-mass hosts. Across the entire host halo mass range, the highest-resolution regions in these simulations are resolved with a dark matter particle mass of $\approx 3\times 10^{-7}$ times the host virial mass and a Plummer-equivalent gravitational softening length of $\approx 9\times 10^{-4}$ times the host virial radius, on average. We measure correlations between subhalo abundance and host concentration, formation time, and maximum subhalo mass, all of which peak at the Milky Way host halo mass scale. Subhalo abundances are $\approx 50\%$ higher in clusters than in lower-mass hosts at fixed sub-to-host halo mass ratios. Subhalo radial distributions are approximately self-similar as a function of host mass and are less concentrated than hosts' underlying dark matter distributions. We compare our results to the semianalytic model $\mathrm{\texttt{Galacticus}}$, which predicts subhalo mass functions with a higher normalization at the low-mass end and radial distributions that are slightly more concentrated than Symphony. We use $\mathrm{\texttt{UniverseMachine}}$ to model halo and subhalo star formation histories in Symphony, and we demonstrate that these predictions resolve the formation histories of the halos that host nearly all currently observable satellite galaxies in the universe. To promote open use of Symphony, data products are publicly available at http://web.stanford.edu/group/gfc/symphony., Comment: 39 pages, 20 figures, 2 tables. Updated to published version

The advantage of simulating lattice field theory with quantum computers is hamstrung by the limited resources that induce large errors from finite volume and sizable lattice spacings. Previous work has shown how classical simulations near the Hamiltonian limit can be used for setting the lattice spacings in real-time through analytical continuation, thereby reducing errors in quantum simulations. In this work, we derive perturbative relations between bare and renormalized quantities in Euclidean spacetime at any anisotropy factor -- the ratio of spatial to temporal lattice spacings -- and in any spatial dimension for $U(N)$ and $SU(N)$. This reduces the required classical preprocessing for quantum simulations. We find less than $10\%$ discrepancy between our perturbative results and those from existing nonperturbative determinations of the anisotropy for $SU(2)$ and $U(1)$ gauge theories. For the discrete groups $\mathbb{Z}_{10}$, $\mathbb{Z}_{100}$ and $\mathbb{BI}$, we perform lattice Monte Carlo simulations to extract anisotropy factors and observe similar agreement with our perturbative results., Comment: 10 pages, 8 figures

The problem of Raman amplifier optimization is studied. A differentiable interpolation function is obtained for the Raman gain coefficient using machine learning (ML), which allows for the gradient descent optimization of forward-propagating Raman pumps. Both the frequency and power of an arbitrary number of pumps in a forward pumping configuration are then optimized for an arbitrary data channel load and span length. The forward propagation model is combined with an experimentally-trained ML model of a backward-pumping Raman amplifier to jointly optimize the frequency and power of the forward amplifier's pumps and the powers of the backward amplifier's pumps. The joint forward and backward amplifier optimization is demonstrated for an unrepeatered transmission of 250 km. A gain flatness of $<$ 1~dB over 4 THz is achieved. The optimized amplifiers are validated using a numerical simulator., Comment: submitted to Journal of Lightwave Technology. Extended version of the previous conference paper M. Yankov, D. Zibar, A. Carena, and F. Da Ros, "Forward Raman amplifier optimization using machine learning-aided physical modeling," accepted, Optoelectronics and Communications Conference (OECC), 2022

We experimentally validate a machine learning-enabled Raman amplification framework, capable of jointly shaping the signal power evolution in two domains: frequency and fiber distance. The proposed experiment addresses the amplification in the whole C-band, by optimizing four first-order counter-propagating Raman pumps., Comment: 4 pages, 5 figures

It is for the first time that Quantum Simulation for High Energy Physics (HEP) is studied in the U.S. decadal particle-physics community planning, and in fact until recently, this was not considered a mainstream topic in the community. This fact speaks of a remarkable rate of growth of this subfield over the past few years, stimulated by the impressive advancements in Quantum Information Sciences (QIS) and associated technologies over the past decade, and the significant investment in this area by the government and private sectors in the U.S. and other countries. High-energy physicists have quickly identified problems of importance to our understanding of nature at the most fundamental level, from tiniest distances to cosmological extents, that are intractable with classical computers but may benefit from quantum advantage. They have initiated, and continue to carry out, a vigorous program in theory, algorithm, and hardware co-design for simulations of relevance to the HEP mission. This community whitepaper is an attempt to bring this exciting and yet challenging area of research to the spotlight, and to elaborate on what the promises, requirements, challenges, and potential solutions are over the next decade and beyond., Comment: This is a whitepaper prepared for the topical groups CompF6 (Quantum computing), TF05 (Lattice Gauge Theory), and TF10 (Quantum Information Science) within the Computational Frontier and Theory Frontier of the U.S. Community Study on the Future of Particle Physics (Snowmass 2021). 103 pages and 1 figure

This is a Snowmass white paper on the utility of existing and future superconducting cavities to probe fundamental physics. Superconducting radio frequency (SRF) cavity technology has seen tremendous progress in the past decades, as a tool for accelerator science. With advances spear-headed by the SQMS center at Fermilab, they are now being brought to the quantum regime becoming a tool in quantum science thanks to the high degree of coherence. The same high quality factor can be leveraged in the search for new physics, including searches for new particles, dark matter, including the QCD axion, and gravitational waves. We survey some of the physics opportunities and the required directions of R&D. Given the already demonstrated integration of SRF cavities in large accelerator systems, this R&D may enable larger scale searches by dedicated experiments., Comment: Submitted to the Proceedings of the US Community Study on the Future of Particle Physics (Snowmass 2021). 19 pages, 21 figures

An essential goal of the Higgs physics program at the LHC and beyond is to explore the nature of the Higgs potential and shed light on the mechanism of electroweak symmetry breaking. An important class of models defining the strength and order of the electroweak phase transition is driven by the Higgs boson coupling to a light new state. This Snowmass white paper points out the existence of a region of parameter space where a strongly first order electroweak phase transition is compatible with exotic decays of the SM-like Higgs boson. A dedicated search for exotic Higgs decays can actively explore this framework at the Large Hadron Collider (LHC), while future exotic Higgs decay searches at the high-luminosity LHC and future Higgs factories will be vital to conclusively probe the scenario., Comment: 10 pages + references, 4 figures, Contribution to Snowmass 2021

We give Feffermain-Stein type inequalities related to mixed estimates for Calder\'on-Zygmund operators. More precisely, given $\delta>0$, $q>1$, $\varphi(z)=z(1+\log^+z)^\delta$, a nonnegative and locally integrable function $u$ and $v\in \mathrm{RH}_\infty\cap A_q$, we prove that the inequality \[uv\left(\left\{x\in \mathbb{R}^n: \frac{|T(fv)(x)|}{v(x)}>t\right\}\right)\leq \frac{C}{t}\int_{\mathbb{R}^n}|f|\left(M_{\varphi, v^{1-q'}}u\right)M(\Psi(v))\] holds with $\Psi(z)=z^{p'+1-q'}\mathcal{X}_{[0,1]}(z)+z^{p'}\mathcal{X}_{[1,\infty)}(z)$, for every $t>0$ and every $p>\max\{q,1+1/\delta\}$. This inequality provides a more general version of mixed estimates for Calder\'on-Zygmund operators proved in \cite{CruzUribe-Martell-Perez}. It also generalizes the Fefferman-Stein estimates given in \cite{P94} for the same operators. We further get similar estimates for operators of convolution type with kernels satisfying an $L^\Phi-$H\"ormander condition, generalizing some previously known results which involve mixed estimates and Fefferman-Stein inequalities for these operators., Comment: 17 pages

Quantum simulations of lattice gauge theories for the foreseeable future will be hampered by limited resources. The historical success of improved lattice actions in classical simulations strongly suggests that Hamiltonians with improved discretization errors will reduce quantum resources, i.e. require $\gtrsim 2^d$ fewer qubits in quantum simulations for lattices with $d$ spatial dimensions. In this work, we consider $\mathcal{O}(a^2)$-improved Hamiltonians for pure gauge theories and design the corresponding quantum circuits for its real-time evolution in terms of primitive gates. An explicit demonstration for $\mathbb{Z}_2$ gauge theory is presented including exploratory tests using the ibm_perth device., Comment: 5 pages, 5 figures, matched to journal version