Analytical derivation of the radial distribution function in spherical dark matter halos

The velocity distribution of dark matter near the Earth is important for an accurate analysis of the signals in terrestrial detectors. This distribution is typically extracted from numerical simulations. Here we address the possibility of deriving the velocity distribution function analytically. We derive a differential equation which is a function of radius and the radial component of the velocity. Under various assumptions this can be solved, and we compare the solution with the results from controlled numerical simulations. Our findings complement the previously derived tangential velocity distribution. We hereby demonstrate that the entire distribution function, below 0.7 v_esc, can be derived analytically for spherical and equilibrated dark matter structures.Comment: 6 pages, 5 figures, submitted to MNRA

Measuring the 3D shape of X-ray clusters

Observations and numerical simulations of galaxy clusters strongly indicate that the hot intracluster x-ray emitting gas is not spherically symmetric. In many earlier studies spherical symmetry has been assumed partly because of limited data quality, however new deep observations and instrumental designs will make it possible to go beyond that assumption. Measuring the temperature and density profiles are of interest when observing the x-ray gas, however the spatial shape of the gas itself also carries very useful information. For example, it is believed that the x-ray gas shape in the inner parts of galaxy clusters is greatly affected by feedback mechanisms, cooling and rotation, and measuring this shape can therefore indirectly provide information on these mechanisms. In this paper we present a novel method to measure the three-dimensional shape of the intracluster x-ray emitting gas. We can measure the shape from the x-ray observations only, i.e. the method does not require combination with independent measurements of e.g. the cluster mass or density profile. This is possible when one uses the full spectral information contained in the observed spectra. We demonstrate the method by measuring radial dependent shapes along the line of sight for CHANDRA mock data. We find that at least 10^6 photons are required to get a 5-{\sigma} detection of shape for an x-ray gas having realistic features such as a cool core and a double powerlaw for the density profile. We illustrate how Bayes' theorem is used to find the best fitting model of the x-ray gas, an analysis that is very important in a real observational scenario where the true spatial shape is unknown. Not including a shape in the fit may propagate to a mass bias if the x-ray is used to estimate the total cluster mass. We discuss this mass bias for a class of spacial shapes.Comment: 29 pages, 16 figure

Multi-Agent Programming Contest 2010 - The Jason-DTU Team

We provide a brief description of the Jason-DTU system, including the methodology, the tools and the team strategy that we plan to use in the agent contest.Comment: 4 page

Business Level Service-Oriented Enterprise Application Integration

In this paper we propose a new approach for service-oriented enterprise application integration (EAI). Unlike current EAI solutions, which mainly focus on technological aspects, our approach allows business domain experts to get more involved in the integration process. First, we provide a technique for modeling application services at a sufficiently high level of abstraction for business experts to work with. Next, these business experts can model the orchestration as well as the information mappings that are required to achieve their integration goals. Our mediation framework then takes over and realizes the integration solution by transforming these models to existing service orchestration technology

RoomKey:Extracting a Volatile Key with Information from the Local WiFi Environment Reconstructable Within a Designated Area

We present a WiFi signal-based, volatile key extraction system called RoomKey. We derive a room’s key by creating a deterministic key from the ever-changing WiFi environment and investigating the extraction capabilities of a designated area. RoomKey uses wireless beacon frames as a component, which we combine with a strong random key to generate and reconstruct the same volatile key in the room. We provide an exemplary use case using RoomKeyas an authentication factor using the location-specific WiFi environment as an authentication claim. We identified and solved two problems in using location as an authentication factor: location being sensitive to privacy and the location of a user constantly changing. We mitigate privacy concerns by recognizing a particular location without the need to localize its precise geographical coordinates. To overcome the problem of location change, we restrict locations to work environments for laptop usage and allow a per-location-predetermined, designated area (e.g., a room). With the concept RoomKey, we demonstrate the potential of including environmental WiFi measurements for volatile key extraction and show the possibility of creating location-aware and privacy-preserving authentication systems for continuous authentication and adaptive security measures.</p

LocKey:Location-Based Key Extraction from the WiFi Environment in the User′s Vicinity

We investigate extracting persistent information from semi-volatile signals in the user’s vicinity to extend existing authentication factors. We use WiFi as a representative of semi-volatile signals, as WiFi signals and WiFi receiver hardware are ubiquitous. WiFi hardware is mostly bound to a physical location and WiFi signals are semi-volatile by nature. By comparing different locations, we confirm our expectation that location-specific information is present in the received WiFi signals. In this work, we study whether and how this information can be transformed to satisfy the following properties of a cryptographic key so that we can use it as an extension of an authentication factor: it must be uniformly random, contain sufficient entropy, and the information must be secret. We further discuss two primary use cases in the authentication domain: using extracted low-entropy information (48 bits) for password hardening and using extracted high-entropy information (128 bits and 256 bits) as a location-specific key. Using the WiFi-signal composition as an authentication component increases the usability, introduces the factor of ‘location’ to the authentication claims, and introduces another layer of defense against key or password extraction attacks. Next to these advantages, it has intrinsic limitations, such as the need for the receiver to be in proximity to the signal and the reliance on WiFi signals, which are outside the user’s control. Despite these challenges, using signals in the proximity of a user works in situations with a fallback routine in place while increasing usability and transparency. LocKey is capable to extract low-entropy information at all locations measured, and high-entropy from 68% locations for 128-bit keys (48% of the locations respectively for 256-bit keys). We further show that with an initial measurement time of at most five minutes, we can reconstruct the key in at least 75% of the cases in less than 15, 30, and 40 s depending on the desired key strength

The Measurable Environment as Nonintrusive Authentication Factor on the Example of WiFi Beacon Frames

We explore a method to fingerprint a location in terms of its measurable environment to create an authentication factor that is nonintrusive in the sense that a user is not required to engage in the authentication process actively. Exemplary, we describe the measurable environment by beacon frames from the WiFi access points in the user’s proximity. To use the measurable environment for authentication, measurements must be sufficiently discriminating between locations and similar at the same location. An authentication factor built from the measurable environment allows us to describe a user’s location in terms of measurable signals. Describing a location in terms of its measurable signals implies that we do not require an actual geographical mapping of the user’s location; comparing the measured signals is sufficient to create a location-based authentication factor. Only recognizing an earlier observed environment distinguishes our approach from other location-based authentication factors. We elaborate on using signals in the user’s environment in the background without user involvement to create a privacy-preserving but nonintrusive authentication factor suitable for integration into existing multi-factor authentication schemes.</p