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S. Garg

Publications and source records attributed to S. Garg.

24 records · Page 2Linked to original sources
arXiv2019

Electromagnetic Backgrounds and Potassium-42 Activity in the DEAP-3600 Dark Matter Detector

The DEAP-3600 experiment is searching for WIMP dark matter with a 3.3 tonne single phase liquid argon (LAr) target, located 2.1 km underground at SNOLAB. The experimental signature of dark matter interactions is keV-scale $^{40}$Ar nuclear recoils (NR) producing 128 nm LAr scintillation photons observed by PMTs. The largest backgrounds in DEAP-3600 are electronic recoils (ER) induced by $β$ and $γ$-rays originating from internal and external radioactivity in the detector material. A background model of the ER interactions in DEAP-3600 was developed and is described in this work. The model is based on several components which are expected from radioisotopes in the LAr, from ex-situ material assay measurements, and from dedicated independent in-situ analyses. This prior information is used in a Bayesian fit of the ER components to a 247.2 d dataset to model the radioactivity in the surrounding detector materials. While excellent discrimination between ERs and NRs is reached with pulse shape discrimination, utilizing the large difference between fast and slow components of LAr scintillation light, detailed knowledge of the ER background and activity of detector components, sets valuable constraints on other key types of backgrounds in the detector: neutrons and alphas. In addition, the activity of $^{42}$Ar in LAr in DEAP-3600 is determined by measuring the daughter decay of $^{42}$K. This cosmogenically activated trace isotope is a relevant background at higher energies for other rare event searches using atmospheric argon e.g. DarkSide-20k, GERDA or LEGEND. The specific activity of $^{42}$Ar in the atmosphere is found to be $40.4 \pm 5.9$ $μ$Bq/kg of argon.

nucl-ex↗
arXiv2018

Matching Graphs with Community Structure: A Concentration of Measure Approach

In this paper, matching pairs of random graphs under the community structure model is considered. The problem emerges naturally in various applications such as privacy, image processing and DNA sequencing. A pair of randomly generated labeled graphs with pairwise correlated edges are considered. It is assumed that the graph edges are generated based on the community structure model. Given the labeling of the edges of the first graph, the objective is to recover the labels in the second graph. The problem is considered under two scenarios: i) with side-information where the community membership of the nodes in both graphs are known, and ii) without side-information where the community memberships are not known. A matching scheme is proposed which operates based on typicality of the adjacency matrices of the graphs. Achievability results are derived which provide theoretical guarantees for successful matching under specific assumptions on graph parameters. It is observed that for the proposed matching scheme, the conditions for successful matching do not change in the presence of side-information. Furthermore, a converse result is derived which characterizes a set of graph parameters for which matching is not possible.

cs.CR↗
arXiv2018

Typicality Matching for Pairs of Correlated Graphs

In this paper, the problem of matching pairs of correlated random graphs with multi-valued edge attributes is considered. Graph matching problems of this nature arise in several settings of practical interest including social network de-anonymization, study of biological data, web graphs, etc. An achievable region for successful matching is derived by analyzing a new matching algorithm that we refer to as typicality matching. The algorithm operates by investigating the joint typicality of the adjacency matrices of the two correlated graphs. Our main result shows that the achievable region depends on the mutual information between the variables corresponding to the edge probabilities of the two graphs. The result is based on bounds on the typicality of permutations of sequences of random variables that might be of independent interest.

cs.IT↗
arXiv2018

Optimal Active Social Network De-anonymization Using Information Thresholds

In this paper, de-anonymizing internet users by actively querying their group memberships in social networks is considered. In this problem, an anonymous victim visits the attacker's website, and the attacker uses the victim's browser history to query her social media activity for the purpose of de-anonymization using the minimum number of queries. A stochastic model of the problem is considered where the attacker has partial prior knowledge of the group membership graph and receives noisy responses to its real-time queries. The victim's identity is assumed to be chosen randomly based on a given distribution which models the users' risk of visiting the malicious website. A de-anonymization algorithm is proposed which operates based on information thresholds and its performance both in the finite and asymptotically large social network regimes is analyzed. Furthermore, a converse result is provided which proves the optimality of the proposed attack strategy.

cs.SI↗
arXiv2017

Seeded Graph Matching: Efficient Algorithms and Theoretical Guarantees

In this paper, a new information theoretic framework for graph matching is introduced. Using this framework, the graph isomorphism and seeded graph matching problems are studied. The maximum degree algorithm for graph isomorphism is analyzed and sufficient conditions for successful matching are rederived using type analysis. Furthermore, a new seeded matching algorithm with polynomial time complexity is introduced. The algorithm uses `typicality matching' and techniques from point-to-point communications for reliable matching. Assuming an Erdos-Renyi model on the correlated graph pair, it is shown that successful matching is guaranteed when the number of seeds grows logarithmically with the number of vertices in the graphs. The logarithmic coefficient is shown to be inversely proportional to the mutual information between the edge variables in the two graphs.

cs.IT↗