arXiv subjects

Adam Berger

Publications and source records attributed to Adam Berger.

3 recordsLinked to original sources

arXiv2026

Light Dark Matter Discovery Potential and Model Selection at LDMX

Light dark matter (DM) is a compelling scenario for the observed relic abundance, with accelerator-based searches as a powerful discovery strategy. The upcoming Light Dark Matter eXperiment (LDMX) is designed to probe light DM by measuring the energy and transverse momentum of recoil electrons in high-intensity electron-nucleus collisions. We evaluate the discovery potential of LDMX to light DM at four benchmark points along the thermal target for complex scalar DM mediated by a dark photon, for different background assumptions, and assess its model selection power at a representative benchmark. We find that LDMX has strong projected 5$\sigma$ discovery potential along the relic target across both background scenarios for certain benchmarks, and we further compute projected 90% C.L. exclusion limits assuming no measured signal events. The normalization and shape of the two-dimensional recoil electron distribution encodes the coupling and dark photon mass, respectively, enabling parameter inference in the event of a signal excess. We perform parameter estimation on simulated data and find that both parameters are recovered within their uncertainties along the relic target. We assess whether the data can distinguish between competing dark sector hypotheses, in particular, dark photons with additional higher electromagnetic moment interactions. We demonstrate that model comparison using the Bayes factor allows dark sector hypotheses to be statistically distinguished, with the two-dimensional analysis affording substantially greater discriminating power than the one-dimensional analysis. These results are obtained within a likelihood-based statistical framework, incorporating signal and background modelling with their associated systematic uncertainties and employing both frequentist and Bayesian methods. The framework is designed for direct application to real LDMX data.

hep-ph↗

arXiv1997

A Model of Lexical Attraction and Repulsion

This paper introduces new methods based on exponential families for modeling the correlations between words in text and speech. While previous work assumed the effects of word co-occurrence statistics to be constant over a window of several hundred words, we show that their influence is nonstationary on a much smaller time scale. Empirical data drawn from English and Japanese text, as well as conversational speech, reveals that the ``attraction'' between words decays exponentially, while stylistic and syntactic contraints create a ``repulsion'' between words that discourages close co-occurrence. We show that these characteristics are well described by simple mixture models based on two-stage exponential distributions which can be trained using the EM algorithm. The resulting distance distributions can then be incorporated as penalizing features in an exponential language model.

cmp-lg↗

arXiv1997

Text Segmentation Using Exponential Models

This paper introduces a new statistical approach to partitioning text automatically into coherent segments. Our approach enlists both short-range and long-range language models to help it sniff out likely sites of topic changes in text. To aid its search, the system consults a set of simple lexical hints it has learned to associate with the presence of boundaries through inspection of a large corpus of annotated data. We also propose a new probabilistically motivated error metric for use by the natural language processing and information retrieval communities, intended to supersede precision and recall for appraising segmentation algorithms. Qualitative assessment of our algorithm as well as evaluation using this new metric demonstrate the effectiveness of our approach in two very different domains, Wall Street Journal articles and the TDT Corpus, a collection of newswire articles and broadcast news transcripts.

cmp-lg↗