Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.34325

Near-exact Upper Confidence Limits for Axion Haloscopes

Andrea Gallo Rosso·Jan Conrad
View original source ↗Read source document ↗Export metadata ↓

Abstract

The data acquisition of axion haloscopes commonly implements sequential adaptive frequency scans. We present a method to calculate likelihood-based upper confidence limits on axion-photon coupling under this circumstance. We compare the likelihood-based method conceptually and quantitatively with tailor-made methods, i.e. the one proposed by Brubaker et al. (Geometric) and its upgrade the Bayesian Power-Measured (BPM) framework proposed by Palken et al. We find that the likelihood method yields average upper limits that are significantly more constraining than those obtained from these methods. For example, expected constraints improve by a factor of 1.4 as compared with the current state of the art when applied to projections for the ALPHA Plasma-haloscope. We further clarify the statistical basis for the Geometric and BPM methods. In particular, we point out the correspondence of BPM to e-values, proposed as an alternative to conventional p-values in statistics.

Explore related subjects

physics.data-anastro-ph.IMhep-exhep-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Andrea Gallo Rosso, Jan Conrad. 2026-10-25. Near-exact Upper Confidence Limits for Axion Haloscopes. https://arxiv.org/abs/2610.34325

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Transient Supercriticality in Hawkes Processes: Detection, Identifiability, and Apparent Criticality

Self-exciting (Hawkes) models of epidemics, earthquakes, financial markets, and social cascades are usually calibrated with a branching ratio $n$ below one. This is a condition for stationarity, not for existence: a process with $n>1$ grows exponentially but remains finite on any finite horizon. Imposing stationarity is also misleading, because transient supercritical episodes then masquerade as near-criticality. Reconstructing the latent genealogy of events, we show that a single finite record can reveal an excursion into the regime $n(t)>1$, and back, and date its onset and end. Detectability is governed by the excess $n-1$ times the square root of the number of exposed parents. Even when the memory time of the triggering is unknown, the data determine the growth rate of activity during the episode nearly as precisely as when it is known, and hence whether $n$ crossed unity, whereas the value of $n$ requires independent knowledge of the memory time. Letting the data choose between endogenous amplification and external forcing prevents confusing the two. In the 2015 Korean MERS outbreak, our reconstruction shows that transmission by hospital cohorts had collapsed by 28 May, before the incidence peak and two weeks before the instantaneous reproduction number fell below one; in real time, it establishes the collapse a few days earlier than this standard indicator. Our framework separates stationarity, non-explosion, and transient supercritical amplification, and provides a practical route for detecting short-lived endogenous instabilities in self-exciting systems.

physics.data-an↗
arXiv2026

Track finding and particle identification performance with particle physics foundation models

Modern high energy nuclear and particle physics experiments produce large amounts of sparse data that must be reduced significantly in size before performing physics analysis. Recently, there has been an effort to evaluate Foundation Models as a potential tool for performing data analysis. These models have been shown to learn features in high dimensional datasets based on the multi-modal data they are trained on. To show their effectiveness at performing complex pattern recognition tasks, such as reconstructing charged particles in a magnetic field, we evaluated the performance of a Foundation Model based reconstruction on several downstream tasks with a dataset composed of spacepoints in a Time Projection Chamber. We show that the performance of the Foundation Model based data analysis significantly improves upon that of traditional methods, and furthermore can be adapted to learn additional tasks that are not necessarily related to the initial training objective.

physics.data-an↗
arXiv2026

An Algebra Bundle for Non-Composable Coincidence Probabilities in Gamma-ray Spectroscopy

Understanding nuclear level schemes from the detection of emitted gamma-rays is, at its core, a combinatorial problem. The transition matrix formalism that treats nuclear decay as a Markov process is a well established treatment for the prediction of emitted and detected gamma-rays. However, with the path algebra being the underlying algebra of such matrix methods, the formalism is restricted to calculating probabilities only for composable paths through the decay. This paper presents a new algebra that allows for the calculations of non-composable coincidence probabilities offering a more complete formalism for the prediction of gamma-ray spectra. The aim of this \textit{coincidence algebra} is to provide a general treatment for predicting all full energy summing peaks for composable and non-composable transitions. Non-composable summing peaks often corrupt peaks of interest by creating doublets in the energy spectra. The coincidence algebra serves as a tool for investigating any possible doublets produced from full energy coincidence summing. This formalism is tested for the prediction of gamma-ray peaks in the electron capture of $^{133}$Ba and compared to the results a Geant4 simulation of the decay within the GRIFFIN spectrometer. It is also shown how the path algebra formalism may be useful for understanding uncertainty propagation in nuclear level schemes.

physics.data-an↗