arXiv subjects

Eve Armstrong

Publications and source records attributed to Eve Armstrong.

At least 19 recordsLinked to original sources

arXiv2024

Leveraging neutrino flavor physics for supernova model differentiation

Neutrino flavor evolution is critical for understanding the physics of dense astrophysical regimes, including core-collapse supernovae (CCSN). Powerful numerical integration codes exist for simulating these environments, yet a complete understanding of the inherent nonlinearity of collective neutrino flavor oscillations and how it fits within the overall framework of these simulations remains an open challenge. For this reason, we continue developing statistical data assimilation (SDA) to infer solutions to the flavor field in a CCSN envelope, given simulated measurements far from the source. SDA is an inference paradigm designed to optimize a model with sparse data. Our model consists of neutrino beams emanating from a CCSN and coherently interacting with each other and with a background of other matter particles in one dimension $r$. One model feature of high interest is the distribution of those matter particles as a function of radius $r$, or the "matter potential" $V(r)$ -- as it significantly dictates flavor evolution. In this paper, we expand the model beyond previous incarnations, by replacing the monotonically-decaying analytic form for $V(r)$ we previously used with a more complex -- and more physically plausible -- set of profiles derived from a one-dimensional (spherically symmetric) hydrodynamics simulation of a CCSN explosion. We ask whether the SDA procedure can use simulated flavor measurements at physically accessible locations (i.e. in vacuum) to determine the extent to which different matter density profiles through which the neutrinos propagate in the matter-dominated regime are compatible with these measurements. Within the scope of our small-scale model, we find that the neutrino flavor measurements in the vacuum regime are able to discriminate between different matter profiles, and we discuss implications regarding a future galactic CCSN detection.

astro-ph.HE↗

arXiv2024

Constraining solar electron number density via neutrino flavor data at Borexino

Understanding the physics of the deep solar interior, and the more exotic environs of core-collapse supernovae (CCSN) and binary neutron-star (NS) mergers, is of keen interest in many avenues of research. To date, this physics is based largely on simulations via forward integration. While these simulations provide valuable constraints, it could be insightful to adopt the "inverse approach" as a point of comparison. Within this paradigm, parameters of the solar interior are not output based on an assumed model, but rather are inferred based on real data. We take the specific case of solar electron number density, which historically is taken as output from the standard solar model. We show how one may arrive at an independent constraint on that density profile based on available neutrino flavor data from the Earth-based Borexino experiment. The inference technique's ability to offer a unique lens on physics can be extended to other datasets, and to analogous questions for CCSN and NS mergers, albeit with simulated data.

astro-ph.SR↗

arXiv2024

Everything's Fine

I investigate the peculiar situation in which I find myself healthy and strong, with a darling family, stimulating job, top-notch dental plan, and living far from active war and wildfire zones -- yet perpetually ill at ease and prone to sudden-onset exasperation when absolutely nothing has happened. My triggers include dinner parties, chairs, therapists, and shopping at Costco. In analysing this phenomenon, I consider epigenetics, the neuroscience of neuroticism, and possible environmental factors such as NSF grant budgets. Yet no obvious solution emerges. Fortunately, my affliction isn't really all that serious. In fact, it's good writing material. So while I'm open to better ideas, I figure I'll just continue being like this.

physics.gen-ph↗

arXiv2023

I Murdered Conan O'Brien and Nobody Will Ever Know -- an exercise in inference sabotage

I employ an optimization-based inference methodology together with an Ising model, in an intentionally ineffectual manner, to get away with murdering an obstreperous scientific collaborator. The antics of this collaborator, hereafter "Conan O'Brien," were impeding the publication of an important manuscript. With my tenure date looming, I found myself desperate. Luckily, I study inference, a computational means to find a solution to a physical problem, based on available measurements (say, a dead body) and a dynamical model assumed to give rise to those measurements (a murderer). If the measurements are insufficient and/or the model is incomplete, one obtains multiple "degenerate" solutions to the problem. Degenerate solutions are all equally valid given the information available, and thus render meaningless the notion of one "correct" solution. Typically in scientific research, degeneracy is undesirable. Here I describe the opposite situation: a quest to create degenerate solutions in which to cloak myself. Or even better: to render measurements incompatible with a solution in which I am the murderer. Moreover, I show how one may sabotage an inference procedure to commit an untraceable crime. I sit here now, typing victoriously, a free woman. Because you won't believe me anyway. And even if you do, you'll never prove a thing.

astro-ph.HE↗

arXiv2022

Inference finds consistency between a neutrino flavor evolution model and Earth-based solar neutrino measurements

We continue examining statistical data assimilation (SDA), an inference methodology, to infer solutions to neutrino flavor evolution, for the first time using real - rather than simulated - data. The model represents neutrinos streaming from the Sun's center and undergoing a Mikheyev-Smirnov-Wolfenstein (MSW) resonance in flavor space, due to the radially-varying electron number density. The model neutrino energies are chosen to correspond to experimental bins in the Sudbury Neutrino Observatory (SNO) and Borexino experiments, which measure electron-flavor survival probability at Earth. The procedure successfully finds consistency between the observed fluxes and the model, if the MSW resonance - that is, flavor evolution due to solar electrons - is included in the dynamical equations representing the model.

astro-ph.SR↗

arXiv2022

The Three Little Pigs and the Big Bad Wolf: Case Studies of Peer Review

I present for your appraisal three independent cases of the manuscript referee process conducted by a venerable peer-reviewed scientific journal. Each case involves a little pig, who submitted for consideration a theoretical plan for a house to be constructed presently, in a faraway land. An anonymous big bad wolf was assigned by the journal to assess the merit of these manuscripts. The pigs proposed three distinct construction frameworks, which varied in physical and mathematical sophistication. The first little pig submitted a model of straw, based on the numerical method of toe-counting. His design included odd features, such as spilled millet and cloven-hoofprints on the window sill -- possibly a ploy to distract the wolf from the manuscript's facile mathematical foundation. The second little pig used a more advanced approach, employing Newton's classical laws of motion, to propose a house of sticks. This pig included in her manuscript copious citations by a specific wolf, possibly aiming to ensure acceptance by flattering the wolf whom she anticipated would be the referee. The third little pig described an ostentatious house of bricks based on an elaborate dynamical systems and stability analysis, possibly scheming to dazzle and impress. The big bad wolf did not appear moved by any of the pigs' tactics. His recommendations were, for straw: the minor revision of water-proofing; for sticks: the major revision of fire-proofing, given concerns surrounding climate change; for bricks: unequivocal rejection, accompanied by multiple derogatory comments regarding "high-and-mighty theorists." I describe each case in detail, and suggest that the wolf's reports might be driven as much by self interest as the manuscripts themselves -- namely, that at the time the wolf wrote the reviews, he was rather hungry. Finally, I examine morals learned, if any.

astro-ph.HE↗

arXiv2022

Inference of bipolar neutrino flavor oscillations near a core-collapse supernova, based on multiple measurements at Earth

Neutrinos in compact-object environments, such as core-collapse supernovae, can experience various kinds of collective effects in flavor space, engendered by neutrino-neutrino interactions. These include "bipolar" collective oscillations, which are exhibited by neutrino ensembles where different flavors dominate at different energies. Considering the importance of neutrinos in the dynamics and nucleosynthesis in these environments, it is desirable to ascertain whether an Earth-based detection could contain signatures of bipolar oscillations that occurred within a supernova envelope. To that end, we continue examining a cost-function formulation of statistical data assimilation (SDA) to infer solutions to a small-scale model of neutrino flavor transformation. SDA is an inference paradigm designed to optimize a model with sparse data. Our model consists of two mono-energetic neutrino beams with different energies emanating from a source and coherently interacting with each other and with a matter background, with time-varying interaction strengths. We attempt to infer flavor transformation histories of these beams using simulated measurements of the flavor content at locations in vacuum (that is, far from the source), which could in principle correspond to earth-based detectors. Within the scope of this small-scale model, we found that: (i) based on such measurements, the SDA procedure is able to infer \textit{whether} bipolar oscillations had occurred within the protoneutron star envelope, and (ii) if the measurements are able to sample the full amplitude of the neutrino oscillations in vacuum, then the amplitude of the prior bipolar oscillations is also well predicted. This result intimates that the inference paradigm can well complement numerical integration codes, via its ability to infer flavor evolution at physically inaccessible locations.

astro-ph.HE↗

arXiv2021

Inference solves a boundary-value collision problem, with relevance to neutrino flavor transformation

Understanding neutrino flavor transformation in dense environments such as core-collapse supernovae (CCSN) is critical for inferring nucleosynthesis and interpreting a detected neutrino signal. The role of direction-changing collisions in shaping the neutrino flavor field in these environments is important and poorly understood; it has not been treated self-consistently. There has been progress, via numerical integration, to include the effects of collisions in the dynamics of the neutrino flavor field. While this has led to important insights, integration is limited by its requirement that full initial conditions must be assumed known. On the contrary, feedback from collisions to the neutrino field is a boundary value problem. Numerical integration techniques are poorly equipped to handle that formulation. This paper demonstrates that an inference formulation of the problem can solve a simple collision-only model representing a CCSN core -- without full knowledge of initial conditions. Rather, the procedure solves a two-point boundary value problem with partial information at the bounds. The model is sufficiently simple that physical reasoning may be used as a confidence check on the inference-based solution, and the procedure recovers the expected model dynamics. This result demonstrates that inference can solve a problem that is artificially hidden from integration techniques -- a problem that is an important feature of flavor evolution in dense environments. Thus, it is worthwhile to explore means of augmenting the existing powerful integration tools with inference-based approaches.

physics.comp-ph↗

arXiv2021

Inference of neutrino flavor evolution through data assimilation and neural differential equations

The evolution of neutrino flavor in dense environments such as core-collapse supernovae and binary compact object mergers constitutes an important and unsolved problem. Its solution has potential implications for the dynamics and heavy-element nucleosynthesis in these environments. In this paper, we build upon recent work to explore inference-based techniques for estimation of model parameters and neutrino flavor evolution histories. We combine data assimilation, ordinary differential equation solvers, and neural networks to craft an inference approach tailored for non-linear dynamical systems. Using this architecture, and a simple two-neutrino, two-flavor model, we test various optimization algorithms with the help of four experimental setups. We find that employing this new architecture, together with evolutionary optimization algorithms, accurately captures flavor histories in the four experiments. This work provides more options for extending inference techniques to large numbers of neutrinos.

astro-ph.HE↗

arXiv2021

My cat Chester's dynamical systems analysyyyyy7777777777777777y7is of the laser pointer and the red dot on the wall: correlation, causation, or SARS-Cov-2 hallucination?

My cat Chester investigates the elusive relationship between the appearance in my hand of a silver laser pointer and that of a red dot on the wall, or on the floor, or on any other object that resides within the vicinity of the laser pointer. Chester first assesses preliminary establishments for causality, including mutual information, temporal precedence, and control for third variables. These assessments are all inconclusive for various reasons. In particular, mutual information fails to illuminate the problem due to a dearth of information regarding what the laser pointer might have been doing at times following Chester's first awareness of the dot. Next Chester performs a formal reconstruction of phase space via time-delay embedding, to unfold the gggggggggggfffgfgtredvteometry ,mmmm.........,.,,......,.mmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmm of the underlying dynamical system giving rise to the red dot's trajectory. The resulting attractor does not resemble a laser pointer. The reconstruction could, however, be flawed, for example, due to the short temporal duration of the dot's observed trajectory. Finally, the red dot could be a hallucination: a symptom brought on by COVID-19 - because, well, these days pretty much anything might be a symptom brought on by COVID-19. On this note, Chester's kitten brother Mad Dog Lapynski offers an independent check on the red dot's existence. Moreover, the results of this study are inconclusive and ca[pokilki[[[[[ll for follow-up.

physics.pop-ph↗

arXiv2020

Inference offers a metric to constrain dynamical models of neutrino flavor transformation

The multi-messenger astrophysics of compact objects presents a vast range of environments where neutrino flavor transformation may occur and may be important for nucleosynthesis, dynamics, and a detected neutrino signal. Development of efficient techniques for surveying flavor evolution solution spaces in these diverse environments, which augment and complement existing sophisticated computational tools, could leverage progress in this field. To this end we continue our exploration of statistical data assimilation (SDA) to identify solutions to a small-scale model of neutrino flavor transformation. SDA is a machine learning (ML) formula wherein a dynamical model is assumed to generate any measured quantities. Specifically, we use an optimization formulation of SDA wherein a cost function is extremized via the variational method. Regions of state space in which the extremization identifies the global minimum of the cost function will correspond to parameter regimes in which a model solution can exist. Our example study seeks to infer the flavor transformation histories of two mono-energetic neutrino beams coherently interacting with each other and with a matter background. We require that the solution be consistent with measured neutrino flavor fluxes at the point of detection, and with constraints placed upon the flavor content at various locations along their trajectories, such as the point of emission, and the locations of the Mikheyev-Smirnov-Wolfenstein (MSW) resonances. We show how the procedure efficiently identifies solution regimes and rules out regimes where solutions are infeasible. Overall, results intimate the promise of this "variational annealing" methodology to efficiently probe an array of fundamental questions that traditional numerical simulation codes render difficult to access.

astro-ph.HE↗

arXiv2020

Identifying the measurements required to estimate rates of COVID-19 transmission, infection, and detection, using variational data assimilation

We demonstrate the ability of statistical data assimilation to identify the measurements required for accurate state and parameter estimation in an epidemiological model for the novel coronavirus disease COVID-19. Our context is an effort to inform policy regarding social behavior, to mitigate strain on hospital capacity. The model unknowns are taken to be: the time-varying transmission rate, the fraction of exposed cases that require hospitalization, and the time-varying detection probabilities of new asymptomatic and symptomatic cases. In simulations, we obtain accurate estimates of undetected (that is, unmeasured) infectious populations, by measuring the detected cases together with the recovered and dead - and without assumed knowledge of the detection rates. Given a noiseless measurement of the recovered population, excellent estimates of all quantities are obtained using a temporal baseline of 101 days, with the exception of the time-varying transmission rate at times prior to the implementation of social distancing. With low noise added to the recovered population, accurate state estimates require a lengthening of the temporal baseline of measurements. Estimates of all parameters are sensitive to the contamination, highlighting the need for accurate and uniform methods of reporting. The aim of this paper is to exemplify the power of SDA to determine what properties of measurements will yield estimates of unknown parameters to a desired precision, in a model with the complexity required to capture important features of the COVID-19 pandemic.

q-bio.PE↗

arXiv2020

An Artificially-intelligent Means to Escape Discreetly from the Departmental Holiday Party; guide for the socially awkward

We employ simulated annealing to identify the global solution of a dynamical model, to make a favorable impression upon colleagues at the departmental holiday party and then exit undetected as soon as possible. The procedure, Gradual Freeze-out of an Optimal Estimation via Optimization of Parameter Quantification - GFOOEOPQ, is designed for the socially awkward. The socially awkward among us possess little instinct for pulling off such a maneuver, and may benefit from a machine to do it for us. The method rests upon Bayes' Theorem, where the probability of a future model state depends on current knowledge of the model. Here, model state vectors are party attendees, and the future event of interest is their disposition toward us at times following the party. We want these dispositions to be favorable. To this end, we first interact so as to make favorable impressions, or at least ensure that these people remember having seen us there. Then we identify the exit that minimizes the chance that anyone notes how early we high-tailed it. Now, poorly-resolved estimates will correspond to degenerate solutions. As noted, we possess no instinct to identify a global optimum by ourselves. This can have disastrous consequences. For this reason, GFOOEOPQ employs annealing to iteratively home in on this optimum. The method is illustrated via a simulated event hosted by someone in the physics department (I am not sure who), in a two-bedroom apartment on the fifth floor of an elevator building in Manhattan, with viable Exit parameters: front door, side door to a stairwell, fire escape, and a bathroom window that opens onto the fire escape. Preliminary tests are reported at two real social celebrations. The procedure is generalizable to corporate events and family gatherings. Readers are encouraged to report novel applications of GFOOEOPQ, to expand the algorithm.

physics.pop-ph↗

arXiv2019

Statistical data assimilation for estimating electrophysiology simultaneously with connectivity within a biological neuronal network

A method of data assimilation (DA) is employed to estimate electrophysiological parameters of neurons simultaneously with their synaptic connectivity in a small model biological network. The DA procedure is cast as an optimization, with a cost function consisting of both a measurement error and a model error term. An iterative reweighting of these terms permits a systematic method to identify the lowest minimum, within a local region of state space, on the surface of a non-convex cost function. In the model, two sets of parameter values are associated with two particular functional modes of network activity: simultaneous firing of all neurons, and a pattern-generating mode wherein the neurons burst in sequence. The DA procedure is able to recover these modes if: i) the stimulating electrical currents have chaotic waveforms, and ii) the measurements consist of the membrane voltages of all neurons in the circuit. Further, this method is able to prune a model of unnecessarily high dimensionality to a representation that contains the maximum dimensionality required to reproduce the provided measurements. This paper offers a proof-of-concept that DA has the potential to inform laboratory designs for estimating properties in small and isolatable functional circuits.

q-bio.NC↗

arXiv2019

Forecasting Future Murders of Mr. Boddy by Numerical Weather Prediction

Despite a previous description of his state as a stable fixed point, just past midnight this morning Mr. Boddy was murdered again. In fact, over 70 years Mr. Boddy has been reported murdered $10^6$ times, while there exist no documented attempts at intervention. Using variational data assimilation, we train a model of Mr. Boddy's dynamics on the time series of observed murders, to forecast future murders. The parameters to be estimated include instrument, location, and murderer. We find that a successful estimation requires three additional elements. First, to minimize the effects of selection bias, generous ranges are placed on parameter searches, permitting values such as the Cliff, the Poisoned Apple, and the Wife. Second, motive, which was not considered relevant to previous murders, is added as a parameter. Third, Mr. Boddy's little-known asthmatic condition is considered as an alternative cause of death. Following this morning's event, the next local murder is forecast for 17:19:03 EDT this afternoon, with a standard deviation of seven hours, at The Kitchen at 4330 Katonah Avenue, Bronx, NY, 10470, with either the Lead Pipe or the Lead Bust of Washington Irving. The motive is: Case of Mistaken Identity, and there was no convergence upon a murderer. Testing of the procedure's predictive power will involve catching the D train to 205th Street and a few transfers over to Katonah Avenue, and sitting around waiting with our eyes peeled. We discuss the problem of identifying a global solution - that is, the best reason for murder on a landscape riddled with pretty-decent reasons. We also discuss the procedure's assumption of Gaussian-distributed errors, which will under-predict rare events. This under-representation of highly improbable events may be offset by the fact that the training data, after all, consists of multiple murders of a single person.

physics.pop-ph↗

arXiv2018

An optimization method to simultaneously estimate electrophysiology and connectivity in a model central pattern generator

Central pattern generators (CPGs) appear to have evolved multiple times throughout the animal kingdom, indicating that their design imparts a significant evolutionary advantage. Insight into how this design is achieved is hindered by the difficulty inherent in examining relationships among electrophysiological properties of the constituent cells of a CPG and their functional connectivity. That is: experimentally it is challenging to estimate the values of more than two or three of these properties simultaneously. We employ a method of statistical data assimilation (D.A.) to estimate the synaptic weights, synaptic reversal potentials, and maximum conductances of ion channels of the constituent neurons in a multi-modal network model. We then use these estimates to predict the functional mode of activity that the network is expressing. The measurements used are the membrane voltage time series of all neurons in the circuit. We find that these measurements provide sufficient information to yield accurate predictions of the network's associated electrical activity. This experiment can apply directly in a real laboratory using intracellular recordings from a known biological CPG whose structural mapping is known, and which can be completely isolated from the animal. The simulated results in this paper suggest that D.A. might provide a tool for simultaneously estimating tens to hundreds of CPG properties, thereby offering the opportunity to seek possible systematic relationships among these properties and the emergent electrical activity.

physics.bio-ph↗

arXiv2018

Colonel Mustard in the Aviary with the Candlestick: a limit cycle attractor transitions to a stable focus via supercritical Andronov-Hopf bifurcation

We establish the means by which Mr. Boddy came to transition from a stable trajectory within the global phase space of Philadelphia, Pennsylvania to a stable point on the cement floor of an aviary near the west bank of the Schuylkill River. There exist no documented murder motives, and so the dynamical interaction leading to the crime must be reconstructed from circumstantial data. Our investigation proceeds in two stages. First we take an audio stream recorded within the aviary near the time of death to identify the local embedding dimension, thereby enumerating the suspects. Second, we characterize Mr. Boddy's pre- and post-mortem behavior in the phase space in terms of an attractor that undergoes an abrupt change in stability. A supercritical Andronov-Hopf bifurcation can explain this transition. Then we uniquely identify the murderer. Finally, we note long-term plans to construct an underlying dynamical model capable of predicting the stability of equilibria in different parameter regimes, in the event that Mr. Boddy is ever murdered again.

q-bio.PE↗

arXiv2018

Computational model of avian nervous system nuclei governing learned song

The means by which neuronal activity yields robust behavior is a ubiquitous question in neuroscience. In the songbird, the timing of a highly stereotyped song motif is attributed to the cortical nucleus HVC, and to feedback to HVC from downstream nuclei in the song motor pathway. Control of the acoustic structure appears to be shared by various structures, whose functional connectivity is largely unknown. Currently there exists no model for functional synaptic architecture that links HVC to song output in a manner consistent with experiments. Here we build on a previous model of HVC in which a distinct functional architecture may act as a pattern generator to drive downstream regions. Using a specific functional connectivity of the song motor pathway, we show how this HVC mechanism can generate simple representations of the driving forces for song. The model reproduces observed correlations between neuronal and respiratory activity and acoustic features of song. It makes testable predictions regarding the electrophysiology of distinct populations in the robust nucleus of the arcopallium (RA), the connectivity within HVC and RA and between them, and the activity patterns of vocal-respiratory neurons in the brainstem.

q-bio.NC↗