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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Boosted Dark Matter Search with the ICARUS Detector at the Gran Sasso Underground National Laboratory

The quest to discover dark matter is one of the most pressing topics in modern physics. Evidence of gravitational anomalies is present at different cosmological scales, spanning the stability and rotation of galaxies to the overall density profile of the entire observable Universe. Due to the discrepancy between the mass measured and the gravitational influence on the mass, if General Relativity is to correctly describe gravity at different scales, extra electromagnetically invisible matter termed dark matter must exist. In recent decades, many experiments have been performed to detect dark matter with masses that span many orders of magnitude, from Mass Compact Halo Objects (MACHOs) with masses several orders of magnitude that of our sun to very light particles on the order of $\sim\mu$eV. However, experiments have only been able to constrain the possible parameter space of dark matter models and, in general, probe dark matter models with single constituent dark matter like Weakly Interacting Massive Particles (WIMPs). In the Boosted Dark Matter (BDM) paradigm, there are four particles in the dark sector: $\chi_0$, $\chi_1$, $\chi_2$, and the dark photon $X$. WIMP-like properties are ascribed to $\chi_0$ to describe the physics of the gravitational anomalies, while relativistic components are assigned to $\chi_1$ and $\chi_2$. The inelastic boosted dark matter (iBDM) channel $\chi_1 e^-\rightarrow \chi_2 e^-\rightarrow \chi_1 Xe^-\rightarrow \chi_1 (e^+ e^- ) e^-$, where $\chi_2$ is an excited state of $\chi_1$, has a unique signature that is distinguishable from neutrino interactions, making neutrino experiments a viable search environment for BDM. Using data collected by the ICARUS collaboration with ICARUS T-600 Liquid Argon Time Projection Chamber (LArTPC) during its operation at the Gran Sasso Underground Laboratory (LNGS), the first-ever LArTPC dark matter search using the ICARUS detector is presented. With a total detector exposure of 0.13 kton$\cdot$year, a total of 4 iBDM candidate events were identified by several stages of data scanning. All were rejected due to defined selection criteria and a wire-by-wire $dE/dx$ evaluation, giving a null result. Exclusion plots at 90\%C.L. for the dark photon ($m_X$, $\epsilon$) parameter space for fixed ($m_0,m_1,m_2$) DM mass sets are presented, setting new limits on the dark photon visible decay parameter space.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Active Learning‐Driven Inkless Additive Nanomanufacturing for Printed Electronics

Inkless additive nanomanufacturing for printed electronics promises broad material and substrate versatility, yet the high-dimensional print parameter space makes tuning print parameters time-intensive. We present a Bayesian optimization study that constructs a digital twin from printed-silver data to benchmark surrogate models, acquisition functions, and batch sizes head-to-head to achieve user-specified target resistance. Tested surrogate models included Gaussian process, random forest, and Bayesian neural network surrogates with expected improvement and confidence bound acquisition functions. In total, we evaluate 48 unique model configurations alongside a random sampling baseline for comparison. For printed silver, the Bayesian neural network with a batch size of one achieved the lowest average cumulative regret, approximately four times more efficient on average than random sampling. To balance performance and substrate space, a random forest model with expected improvement and a batch size of four was chosen as the model for validation testing. Applying this chosen configuration to copper with an additional print parameter, the model achieved a resistance within 0.15 Ω of a 1 Ω target in fewer than 30 printed lines across five validation sets. Altogether, the workflow yields a tuned and validated model that efficiently guides experiments toward the target while simultaneously learning the parameter space.

Bevel, Colton [Auburn University, AL (United State↗
DOE OSTI2026

Dark Energy Survey: Implications for cosmological expansion models from the final DES baryon acoustic oscillation and supernova data

The Dark Energy Survey (DES) recently released the final results of its two principal probes of the expansion history: Type Ia supernovae (SNe) and baryonic acoustic oscillations (BAO). In this paper, we explore the cosmological implications of these data in combination with external cosmic microwave background (CMB), big bang nucleosynthesis (BBN), and age-of-the-Universe information. The BAO measurement, which is ∼ 2 σ away from Planck ’s Λ CDM predictions, pushes for low values of Ω m compared to Planck, in contrast to SN which prefers a higher value than Planck. We identify several tensions among datasets in the Λ CDM model that cannot be resolved by including either curvature ( k Λ CDM ) or a constant dark energy equation of state ( w CDM ). By combining BAO + SN + CMB despite these mild tensions, we obtain Ω k = - 5.5 - 4.2 + 4.6 × 10 - 3 in k Λ CDM , and w = - 0.94 8 - 0.027 + 0.028 in w CDM . In w CDM , BAO and SN push again in different directions of parameter space, favoring, respectively, w < - 1 and w > - 1 . If we open the parameter space to w 0 w a CDM [where the equation of state of dark energy varies as w ( a ) = w 0 + ( 1 - a ) w a ], all the datasets are mutually more compatible, and we find concordance in the [ w 0 > - 1 , w a < 0 ] quadrant, with BAO pushing for w a < 0 and SN for [ w 0 > - 1 , w a < 0 ] . For DES BAO and SN in combination with Planck -CMB, we find a 3.2 σ deviation from Λ CDM , with w 0 = - 0.67 3 - 0.097 + 0.098 , w a = - 1.3 7 - 0.50 + 0.51 , a Hubble constant of H 0 = 67.8 1 - 0.86 + 0.96 km s - 1 Mpc - 1 , and an abundance of matter of Ω m = 0.310 9 - 0.0099 + 0.0086 . For the combination of all the background cosmological probes considered (including CMB’s angular acoustic scale θ ⋆ ), we still find a deviation of 2.8 σ from Λ CDM in the w 0 - w a plane. Assuming a minimal neutrino mass, this work provides tentative evidence for non- Λ CDM physics, which is consistent with recent claims in support of evolving dark energy, or a source of unknown systematics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Future Sterile Neutrino Searches in NOvA using the Booster Neutrino Beam

The NOvA experiment’s most recent search for eV-scale sterile neutrinos under a 3+1 model simultaneously analyses muon neutrino and neutral current datasets from the NuMI beam at it's Near ($\sim$1 km baseline) and Far (810 km baseline) detectors to look for oscillations consistent with a sterile neutrino. The analysis is systematically limited in the region of parameter space where $\Delta m^{2}_{41} $>~1 eV^2$. This region of parameter space is preferred by sterile neutrino interpretations of current experimental anomalies and so improving sensitivity here is high-priority. The Near Detector’s position on-site at Fermilab means that it is also able to observe neutrinos from a second neutrino beam, the BNB, 160 mrad off-axis. This data probes a similar L/E to the NuMI data, but using a different neutrino energy, and being subject to different beam-related uncertainties. Jointly analysing these datasets can help us to constrain our systematic uncertainties, improving our sensitivity. NOvA has been taking BNB data since 2015, but has not yet analysed these data. This poster will present first sensitivities from the combined analysis of the BNB and NuMI datasets.

Lister, Adam [U. Wisconsin, Madison (main)]↗
DOE OSTI2025

Piezo-Antenna: An Electromagnetic-Ultrasonic Data Transfer System

Electronic systems contained within sealed metallic enclosures, such as nuclear waste containers where wire penetration is not an option, require a through-wall data transfer technology to communicate. One such technology utilizes piezoelectric ultrasonic transducers to communicate through the barrier using elastic waves. Currently demonstrated ultrasonic through-wall data communication techniques have relied on having a direct interface with the ultrasonic transducers on either side. Here, this work studies the coupling of an antenna to such an ultrasonic communication system. A computationally efficient model of a piezobarrier ultrasonic communication system and antenna using a simplified antenna model and a transfer matrix piezobarrier model is utilized to perform a representative parameter space search. The system efficiency and bandwidth trends are shown for the system parameters as well as operating frequency. The tradeoff between efficiency and bandwidth is also presented. A system configuration within the parameter space is fabricated and demonstrated to achieve a data rate of up to 100 kb/s.

Antenna↗
DOE OSTI2025

Search for inelastic boosted dark matter with the ICARUS detector at the Gran Sasso Underground National Laboratory

We present the result of a search for inelastic boosted dark matter using the data corresponding to an exposure of 0.13 kton · year , collected by the ICARUS T-600 detector during its 2012–2013 operational period at the INFN Gran Sasso Underground National Laboratory. The benchmark boosted dark matter model features a multiparticle dark sector with a U ( 1 ) ′ gauge boson, the dark photon. The kinetic mixing of the dark photon with the Standard Model photon allows for a portal between the dark sector and the visible sector. The inelastic boosted dark matter interaction occurs when a dark matter particle inelastically scatters with an electron in the ICARUS detector, producing an outgoing, heavier dark sector state which subsequently decays back down to the dark matter particle, emitting a dark photon. The dark photon subsequently couples to a Standard Model photon through kinetic mixing. The Standard Model photon then converts to an electron-positron pair in the detector. This interaction process provides a distinct experimental signature that consists of a recoil electron from the primary interaction and an associated electron-positron pair from the secondary vertex. After analyzing 4,134 triggered events, the search results in zero observed events. Exclusion limits are set in the dark photon mass and coupling ( m X , ε ) parameter space for several selected optimal boosted dark matter mass sets and cover previously unexplored parameter space. Published by the American Physical Society 2025

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

First Sterile Neutrino Search at NOvA Experiment Using both Neutrino and Anti-Neutrino Data

Neutrinos are among the most abundant fundamental particles in the universe, and yet they are so elusive that they pass through almost everything without leaving a trace. For decades, researchers have measured neutrino oscillations in great detail, refining our understanding of the theories. Yet, once again, neutrino shows their incredibly surprising nature through some recent experimental results, suggesting that our current understanding might be incomplete. Several experiments have observed an unexpected difference in the observed neutrino behaviour from what the Standard Model predicts, showing an excess in some cases (e.g. LSND, MiniBooNE) and a deficit of neutrino events in others (e.g. SAGE, GALLEX). These anomalies challenge our conventional theories of standard three neutrino flavors and phenomenologically motivate the sterile neutrino hypothesis, which does not interact through the usual weak force. While some results appear consistent with this idea, others have found no supporting evidence (e.g. MicroBooNE), leaving the question unanswered. NOvA is one such long-baseline neutrino experiment with its Near Detector (ND) at Fermilab and a Far Detector (FD) at 810 km in Minnesota. It uses the NuMI beam as its primary source of neutrinos for oscillation studies. Its two-detector setup, extended baseline, and ability to conduct combined $\nu$-$\bar\nu$ analyses position it to explore these phenomena in new ways. We use the joint $\nu_{\mu}$ and neutral current (NC) disappearance channels to probe active-sterile mixing in the 3+1 model. This analysis utilizes a data sample of 27$\times$10$^{20}$ protons on target (POT) in neutrino mode and 12.5$\times$10$^{20}$ protons on target (POT) in antineutrino mode. We found no evidence of sterile neutrino at 90$\%$ C.L. We present the limits in $\sin^{2}\theta_{24}$ vs $\Delta m^{2}_{41}$, $\sin^{2}\theta_{34}$ vs $\Delta m^{2}_{41}$, and $\sin^{2}2\theta_{\mu\tau}$ vs $\Delta m^{2}_{41}$ parameter spaces. The limits provided by this analysis are world leading in most of the parameter spaces.

Chaudhary, Shivam [Indian Inst. of Info. Tech. Guw↗
DOE OSTI2025

On unitarity of the hypergeometric amplitude

The hypergeometric amplitude is a one-parameter deformation of the Veneziano amplitude for four-point tachyon scattering in bosonic string theory that is consistent with S-matrix bootstrap constraints. In this article we construct a similar hypergeometric generalization of the Veneziano amplitude for type-I superstring theory. We then rule out a large region of the (r, m 2 , D) parameter space as non-unitary, and establish another large subset of the (r, m 2 , D) parameter space where all of the residue’s partial wave coefficients are positive. We also analyze positivity in various limits and special cases. As a corollary to our analysis, we are able to directly demonstrate positivity of a wider set of Veneziano amplitude partial wave coefficients than what has been presented elsewhere.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Primordial black hole dark matter: A quantitative parameter sensitivity comparison across formation mechanisms and particle candidates

Primordial black holes (PBHs) in the asteroid-mass window ( 10 17 – 10 22 g ) can account for all of the dark matter without violating any observational constraint, yet are routinely dismissed as fine-tuned. I put that dismissal to the test by applying three complementary sensitivity measures uniformly across a broad landscape: three noninflationary PBH production mechanisms, six classes of inflationary PBH models, and seven particle dark matter benchmarks, all evaluated against the same observable target. Three distinct naturalness universality classes emerge, determined entirely by the analytic structure of the abundance map rather than by the nature of the dark matter candidate. Biased-domain-wall PBHs, in their least model-dependent (free- V b ) form, have the same low sensitivity, Δ = 4.5 , as off-resonance weakly interacting massive particles and freeze-in particles ( Δ = 2 ), a sensitivity that, because it is constant over the entire parameter space of the construction, also coincides trivially with its own Wilson-normalized average within that parameter space (Section Definition and conventions), an equivalence that concerns only the space over which Δ is computed and is not a naturalness statement about the construction as a whole; a further reduction to Δ = 2 is possible only under the additional, independently motivated but not required, assumption that the domain-wall bias is generated by Planck-suppressed operators; early matter-domination PBHs occupy an intermediate tier alongside coannihilating weakly interacting massive particles (WIMPs), unified by a structural identity in which the sensitivity measure equals the logarithm of the ratio of the formation scale to the matter–radiation equality scale; first-order phase transition PBHs, once the more accurate super-exponential collapse probability is used in place of the single-exponential approximation, instead belong to the same highly sensitive tier as resonant WIMP annihilation and single-field inflationary collapse, for a structurally distinct reason; single-field ultraslow-roll inflationary collapse is severely tuned for a distinct reason: a double exponential in which the power spectrum amplitude is itself exponentially sensitive to the inflaton potential coefficients, on top of the exponential collapse sensitivity of the abundance map. My main conclusion is that the claim that PBH dark matter is generically fine-tuned conflates the worst case with a landscape spanning every naturalness tier. The Barbieri-Giudice sensitivity computed here and the Wilson-normalized measure of Iovino and Riotto answer distinct and mutually consistent questions about the same construction, a distinction I clarify within the two-layer decomposition.

Profumo, Stefano [University of California, Santa ↗
DOE OSTI2025

Computationally efficient subglacial drainage modelling using Gaussian process emulators: GlaDS-GP v1.0

Subglacial drainage models represent water flow at the ice–bed interface through coupled distributed and channelized systems to determine water pressure, discharge, and drainage system geometry. While they are used to understand processes such as the relationship between surface melt and ice flow, the number of uncertain model parameters and the computational cost of running models makes it difficult to adequately explore the high-dimensional parameter space and evaluate uncertainty in model predictions. Here, we develop Gaussian process (GP) emulators that make fast predictions with associated uncertainty of subglacial drainage model outputs. Using a truncated principal component (PC) basis representation, we construct a GP emulator for diurnally averaged subglacial water pressure. We also explore emulation of scalar variables describing drainage efficiency and configuration. We train the emulators using ensembles of up to 512 simulations varying eight parameters of the Glacier Drainage System (GlaDS) model on a synthetic domain intended to represent an ice-sheet margin. The emulators make predictions ∼ 1000 times faster than GlaDS simulations, with errors <3 % for the water pressure field and ∼ 5 %–9 % for drainage efficiency and configuration. We apply the emulators to explore the eight-dimensional parameter space by computing variance-based parameter sensitivity indices, finding that three parameters (ice flow coefficient, bed bump aspect ratio, and the subglacial cavity system conductivity) explain 90 % of the variance in modelled water pressure in response to parameter changes. The GP emulator approach described here is well suited to integrating observational data with models to make calibrated, credible predictions of subglacial drainage.

58 GEOSCIENCES↗
DOE OSTI2025

Inertial Confinement Fusion Design Search Using Bayesian Optimization

Inertial confinement fusion (ICF) experiments rely on complex multi-physics simulation codes such as the Lawrence Livermore National Laboratory-developed HYDRA to guide design work. However, these simulations have several dozen tunable parameters and can be computationally expensive. This makes searching the parameter space challenging and time-consuming. Recently developed automated tools utilize Bayesian optimization to search these high-dimensional parameter spaces for optimal designs. The optimization tools run 2D integrated simulations in HYDRA to converge on a design that produces specified scalar or vector outputs. In this paper, we apply the Bayesian optimization tools to two common tuning scenarios. First, we tune simulation inputs to match measurements of a well-characterized experiment at the National Ignition Facility. This type of tuning is commonly performed to compensate for the use of simplified simulation settings (e.g. reduced resolution) or to account for missing physics in the simulations. Second, we search for an ICF simulation design that has a particular radiation drive profile. These optimizations replicate the kinds of tuning researchers routinely perform, but do so with significantly reduced manual effort. This approach demonstrates a powerful and efficient pathway toward autonomous, high-fidelity design optimization for future ICF experiments.

Bayesian optimization↗
DOE OSTI2025

Accelerating Hamiltonian Monte Carlo for Bayesian inference in neural networks and neural operators

Hamiltonian Monte Carlo (HMC) is a powerful and accurate method to sample from the posterior distribution in Bayesian inference. However, HMC techniques are computationally demanding for Bayesian neural networks due to the high dimensionality of the network’s parameter space and the non-convexity of their posterior distributions. Therefore, various approximation techniques, such as variational inference (VI) or stochastic gradient MCMC, are often employed to infer the posterior distribution of the network parameters. Such approximations introduce inaccuracies in the inferred distributions, resulting in unreliable uncertainty estimates. In this work, we propose a hybrid approach that combines inexpensive VI and accurate HMC methods to efficiently and accurately quantify uncertainties in neural networks and neural operators. The proposed approach leverages an initial VI training on the full network. We examine the influence of individual parameters on the prediction uncertainty, which shows that a large proportion of the parameters do not contribute substantially to uncertainty in the network predictions. This information is then used to significantly reduce the dimension of the parameter space, and HMC is performed only for the subset of network parameters that strongly influence prediction uncertainties. This yields a framework for accelerating the full batch HMC for posterior inference in neural networks. We demonstrate the efficiency and accuracy of the proposed framework on deep neural networks and operator networks, showing that inference can be performed for large networks with tens to hundreds of thousands of parameters. Finally, we show that this method can effectively learn surrogates for complex physical systems by modeling the operator that maps from upstream conditions to wall-pressure data on a cone in hypersonic flow.

Bayesian inference↗
DOE OSTI2025

Physics-guided dual implicit neural representations for source separation

Significant challenges exist in efficient data analysis of most advanced experimental and observational techniques because the collected signals often include unwanted contributions, such as background and signal distortions, that can obscure the physically relevant information of interest. To address this, we have developed a self-supervised machine-learning approach for source separation using a dual implicit neural representation framework that jointly trains two neural networks: one for approximating distortions of the physical signal of interest and the other for learning the effective background contribution. Our method learns directly from the raw data by minimizing a reconstruction-based loss function without requiring labeled data or pre-defined dictionaries. We demonstrate the effectiveness of our framework by considering a challenging case study involving large-scale simulated, as well as experimental, momentum-energy-dependent inelastic neutron scattering data in a four-dimensional parameter space, characterized by heterogeneous background contributions and unknown distortions to the target signal. The method is found to successfully separate physically meaningful signals from a complex or structured background even when the signal characteristics vary across all four dimensions of the parameter space. An analytical approach that informs the choice of the regularization parameter is presented. Our method offers a versatile framework for addressing source separation problems across diverse domains, ranging from superimposed signals in astronomical measurements to structural features in biomedical image reconstructions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Towards an intuitive application of WEC control co-design

A simple co-design example in a reduced parameter space is presented for an oscillating flap device. Initially, the WEC geometry and mass properties are considered along with drivetrain gear ratio, inertia, motor constant and stiffness under both PI and optimal control. This parameter space is reduced to those to which performance is most sensitive for a fixed geometry. The gear ratio, drivetrain stiffness, and flap mass are found to be the most impactful design criteria as they can create orders of magnitude variations in power performance. The performance of the optimized system is compared with several sub-optimal variants in terms of electrical and mechanical power capture, transmission coefficients, and transducer power gain. Notably, though substantial power capture improvements are demonstrated when an optimal controller is employed, this power capture remains sensitive to appropriate selections of drivetrain and flap design parameters, implying that control co-design procedures remain necessary for high-performing WECs. Furthermore, a number of practical caveats and extensions to the presented co-design methodology are suggested, including the characterization of system static friction, especially in the presence of high gear ratios.

Control co-design↗
DOE OSTI2024

Constraining Galaxy-Halo connection using machine learning

We investigate the potential of machine learning (ML) methods to model small-scale galaxy clustering for constraining Halo Occupation Distribution (HOD) parameters. Our analysis reveals that while many ML algorithms report good statistical fits, they often yield likelihood contours that are significantly biased in both mean values and variances relative to the true model parameters. This highlights the importance of careful data processing and algorithm selection in ML applications for galaxy clustering, as even seemingly robust methods can lead to biased results if not applied correctly. ML tools offer a promising approach to exploring the HOD parameter space with significantly reduced computational costs compared to traditional brute-force methods if their robustness is established. Using our ANN-based pipeline, we successfully recreate some standard results from recent literature. Properly restricting the HOD parameter space, transforming the training data, and carefully selecting ML algorithms are essential for achieving unbiased and robust predictions. Among the methods tested, artificial neural networks (ANNs) outperform random forests (RF) and ridge regression in predicting clustering statistics, when the HOD prior space is appropriately restricted. We demonstrate these findings using the projected two-point correlation function (w p (r p )), angular multipoles of the correlation function (ξ ℓ (r)), and the void probability function (VPF) of Luminous Red Galaxies from Dark Energy Spectroscopic Instrument mocks. Our results show that while combining w p (r p ) and VPF improves parameter constraints, adding the multipoles ξ 0 , ξ 2 , and ξ 4 to w p (r p ) does not significantly improve the constraints.

cosmology↗
DOE OSTI2025

Multifrequency Models of Black Hole Photon Rings from Low-luminosity Accretion Disks

Images of black holes encode both astrophysical and gravitational properties. Detecting highly lensed features in images can differentiate between these two effects. We present an accretion disk emission model coupled to the Adaptive Analytical Ray Tracing (AART) code that allows a fast parameter space exploration of black hole photon ring images produced from synchrotron emission from 10 to 670 GHz. As an application, we systematically study several disk models and compute their total flux density, average radii, and optical depth. The model parameters are chosen around fiducial values calibrated to general relativistic magnetohydrodynamic (GRMHD) simulations and observations of M87*. For the parameter space studied, we characterize the transition between optically thin and thick regimes and the frequency at which the first photon ring is observable. Our results highlight the need for careful definitions of photon ring radius in the image domain, as in certain models the highly lensed photon ring is dimmer than the direct emission at certain angles. We find that at low frequencies the ring radii are set by the electron temperature, while at higher frequencies the magnetic field strength plays a more significant role, demonstrating how multifrequency analysis can also be used to infer plasma parameters. Lastly, we show how our implementation can qualitatively reproduce multifrequency black hole images from GRMHD simulations when adding time variability to our disk model through Gaussian random fields. This approach provides a new method for simulating observations from the Event Horizon Telescope and the proposed Black Hole Explorer space mission.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Robust calibration of non-perturbative models with history matching

We apply, for the first time, Bayes Linear Emulation and History Matching to the calibration of non-perturbative models in Monte Carlo event generators. In contrast to the usual approach of "Monte Carlo tuning", History Matching does not result in best-fit plus ellipsoidal parameter uncertainty estimates but instead identifies all parameter space regions that are consistent with data. This approach leads to a systematic and robust quantification of parametric uncertainties in the models, especially in those challenging cases where different, possibly disjoint, regions of parameter space deliver similar results, which are usually not properly treated with current methodology. We highlight the power of this method with the hadronisation models available through Sherpa : the built-in cluster fragmentation Ahadic and string fragmentation through an interface to Pythia .

Iskauskas, Andrew [Durham U. (main)] (ORCID:000000↗
DOE OSTI2026

Accidentally Stable Dark Matter in a Parity Solution to the Strong CP Problem

Parity symmetry, with an extended gauge group $SU(3)_c \times SU(2)_L \times SU(2)_R \times U(1)_X$, can solve the strong CP problem. In particular, the model where $SU(2)_R\times U(1)_X$ is broken by the Parity partner of the Standard Model Higgs solves the strong CP problem without the necessity of introducing extra symmetry. We discuss the possibility of accidentally stable dark matter in this framework and show that $SU(2)_L \times SU(2)_R$ bi-triplet fermions can be stable over cosmological timescales. We compute the relic abundance of the bi-triplet dark matter and derive constraints on the parameter space from collider, direct-detection, and indirect-detection experiments. The $SU(2)_R\times U(1)_X$ symmetry breaking scale is required to be below 150 TeV, and most of the parameter space can be probed by near-future indirect-detection experiments.

Baldwin, Matthew J. [Chicago U., EFI] (ORCID:00090↗