Joint Institute for Laboratory Astrophysics

Abstract Recent three-dimensional flare models suggest that flare ribbon substructure is linked to the fragmentation of the reconnecting current sheet in the corona. Flare ribbon substructure can therefore potentially serve as a unique diagnostic tool for physical processes in the flare current sheet. In this paper, we describe a new method to quantify the evolution of ribbon substructure that first extracts the ribbon’s bright leading edge and then quantifies its morphology using the box-counting dimension and correlation dimension mapping (CDM). We first test our method using synthetic observations. We then apply it to an M6.5-class solar flare on 2015 June 22 observed by the Interface Region Imaging Spectrograph (IRIS) 1330 Å slit-jaw imager. We find that when the flare ribbon boundary has more multiple-spatial-scale features (a higher box-counting dimension), hard X-ray emission and magnetic reconnection rates are the strongest. We also find that the flare ribbon complexity characterized by CDM has a moderate correlation with the IRIS Si IV 1402.77 Å nonthermal velocity (in the negative polarity ribbon) and reconnection flux rates (in ribbons of both magnetic polarities). We conclude that the buildup of the spatial complexity of the ribbons at multiple spatial scales can serve as an observational proxy for current-sheet fragmentation in the corona.

Recent theoretical results in quantum machine learning have demonstrated a general trade-off between the expressive power of quantum neural networks (QNNs) and their trainability; as a corollary of these results, practical exponential separations in expressive power over classical machine learning models are believed to be infeasible as such QNNs take a time to train that is exponential in the model size. We here circumvent these negative results by constructing a hierarchy of efficiently trainable QNNs that exhibit unconditionally provable, polynomial memory separations of arbitrary constant degree over classical neural networks—including state-of-the-art models, such as Transformers—in performing a classical sequence modeling task. This construction is also computationally efficient, as each unit cell of the introduced class of QNNs only has constant gate complexity. We show that contextuality—informally, a quantitative notion of semantic ambiguity—is the source of the expressivity separation, suggesting that other learning tasks with this property may be a natural setting for the use of quantum learning algorithms.

Many Gram-negative pathogens critically depend on the Type III secretion system (T3SS) to inject effector proteins into host cells for colonization. Because the channel of the T3SS is narrow (~2 nm), effectors must be unfolded for secretion. However, the T3SS cannot unfold mechanically robust substrates (GFP, ubiquitin, and dihydrofolate reductase), severely impairing their secretion. Consistent with this, effectors are exceptionally mechanically labile, unfolding at low forces. Thus, secretion competency is correlated with mechanical properties. Effector sequences have significantly diverged from non-effectors, suggesting that secretion exerts evolutionary pressure selecting mechanical lability. Here, using atomic-force-microscopy–based force spectroscopy, we show that effector NleC is mechanically labile ( at 100 nm/s) and mechanically compliant, as characterized by a large distance to the transition state . In contrast, the non-effector homolog protealysin is mechanically stable ( at 100 nm/s) and brittle , comparable to proteins known to impair secretion (; ). Denaturant-induced unfolding assays demonstrate that effectors exhibit rates typical of their fold, further reinforcing mechanical properties rather than fast unfolding kinetics () predicts secretion. Steered molecular dynamic simulations revealed NleC unfolding initiates at the N-terminus, consistent with current secretion models, whereas protealysin unfolding initiates at the C-terminus. Notably, the NleC N-terminus is primarily -helical while non-effector homologs contain -sheets, which may account for the distinct unfolding pathway. Together, these results support the notion that mechanical lability is an evolved, structurally encoded feature underlying effector secretion.

We use a quantum-logic spectroscopy (QLS) protocol to control the quantum state of a CaH^{+} ion in a cryogenic environment in which reduced thermal radiation extends rotational state lifetimes by an order of magnitude over those at room temperature. By repeatedly and adaptively probing the molecule, detecting the outcome of each probe via an atomic ion, and using a Bayesian update scheme to quantify confidence in the molecular state, we demonstrate state preparation and measurement (SPAM) in a single quantum state with infidelity less than 6×10^{-3} and measure Rabi flopping between two states with greater than 99% contrast. The protocol does not require molecule-specific lasers and can be generalized, paving the way for high-fidelity quantum control of many other molecular ions.

Quantum-enhanced metrology surpasses classical metrology by improving estimation precision scaling with a resource N (e.g., particle number or energy) from 1/sqrt[N] to 1/N. Through the use of nonlinear effects, Roy and Braunstein [Exponentially enhanced quantum metrology, Phys. Rev. Lett. 100, 220501 (2008)PRLTAO0031-900710.1103/PhysRevLett.100.220501] derived a 1/2^{N} scaling. However, later works argued this exponential improvement is unphysical and that even modest gains, like 1/N^{2}, may vanish under noise. We show that, in the presence of small errors, the nonlinear interactions enabling metrological enhancement induce emergent errors. The errors propagate through the sensing protocol and are magnified proportional to any intended nonlinear enhancement. We identify a critical value of the parameter to be estimated, for a fixed error, below which the emergent errors can be avoided.

Abstract Solar Energetic Particles (SEPs) are present during increased solar activity, often associated with solar flares and coronal mass ejections (CMEs). Measuring and understanding these particles is important both for fundamental solar physics knowledge as well as the determination of radiation risks in interplanetary space. Solid‐state particle telescopes are a useful tool to measure these particles. The Relativistic Electron and Proton Telescope integrated little experiment‐2 (REPTile‐2) was a solid‐state energetic particle telescope that flew onboard the Colorado Inner Radiation Belt Experiment (CIRBE) and demonstrated a capability to measure electrons from 0.25 to 6 MeV and protons from 7 to 100 MeV with high energy and time resolution. REPTile‐2 operated in a low‐Earth orbit (LEO) and primarily measured radiation belt particles but was also able to measure SEPs during high‐latitude passes. Because of REPTile‐2's solid performance and its CubeSat‐scale size, weight, and power, an opportunity arose to fly a modified REPTile‐2, dubbed REPTile‐3, on the Emirates Mission to the Asteroid Belt (EMA). In this paper, Geometry and tracking 4 (Geant4) Monte Carlo simulations are used to motivate changes to improve REPTile‐3's ability to measure SEPs. Additionally, full instrument response functions and estimated count rates are used to understand the instrument's response to SEP fluxes. REPTile‐3 is shown to be able to measure 1.2–35 MeV protons with ΔE/E < 9%, 35–100 MeV protons with ΔE/E < 50%, 0.1–5 MeV electrons with ΔE/E < 14%, 18–131 MeV helium ions with ΔE/E < 7%, and 131–200 MeV helium ions with ΔE/E < 50% with a 102° field of view (FOV).

We present infrared photodissociation spectra of messenger-tagged, deprotonated valine and deprotonated aminovaleric acid, showing the difference in the vibrational spectra of isomers with the same functional groups. Through comparison of experimental results and density functional theory calculations, we find that the deprotonated states of both valine and aminovaleric acid adopt a configuration in which the carboxylate group forms a hydrogen bond with the amine group. Despite the similarities of the intramolecular hydrogen bonding, the spectra of the two molecules under study are sufficiently different to use the infrared signatures of the carboxylate and amine functional groups as a means to distinguish between them. The conformational search in the case of aminovaleric acid is particularly challenging due to the large number of possible conformations induced by torsion around individual carbon-carbon bonds. For both valine and aminovaleric acid, their infrared spectra in their deprotonated states suggest that two of the lowest energy conformers are likely to be populated.