Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAnalysis method for detecting topological defect dark matter with a global magnetometer network
51
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
The Global Network of Optical Magnetometers for Exotic physics searches (GNOME) is a network of time-synchronized, geographically separated, optically pumped atomic magnetometers that is being used to search for correlated transient signals heralding exotic physics. GNOME is sensitive to exotic couplings of atomic spins to certain classes of dark matter candidates, such as axions. This work presents a data analysis procedure to search for axion dark matter in the form of topological defects: specifically, walls separating domains of discrete degenerate vacua in the axion field. An axion domain wall crossing the Earth creates a distinctive signal pattern in the network that can be distinguished from random noise. The reliability of the analysis procedure and the sensitivity of the GNOME to domain-wall crossings is studied using simulated data.
rate research
Read More
Ultralight bosons such as axion-like particles are viable candidates for dark matter. They can form stable, macroscopic field configurations in the form of topological defects that could concentrate the dark matter density into many distinct, compact spatial regions that are small compared to the galaxy but much larger than the Earth. Here, we report the results of a search for transient signals from axion-like particle domain walls with the Global Network of Optical Magnetometers for Exotic physics searches (GNOME). We search the data, consisting of correlated measurements from optical atomic magnetometers located in laboratories all over the world, for patterns of signals propagating through the network consistent with domain walls. The analysis of data from a continuous month-long operation of the GNOME finds no statistically significant signals, thus placing experimental constraints on such dark matter scenarios.
Gamma ray observations from a few hundred MeV up to tens of TeV are a valuable tool for studying particle acceleration and diffusion within our galaxy. Constructing a coherent physical picture of particle accelerators such as supernova remnants, pulsar wind nebulae, and star-forming regions requires the ability to detect extended regions of gamma ray emission, to analyze small-scale spatial variation within these regions, and to synthesize data from multiple observatories across multiple wavebands. Imaging atmospheric Cherenkov telescopes (IACTs) provide fine angular resolution (<0.1$^circ$) for gamma rays above 100 GeV. However, their limited fields of view typically make detection of extended sources challenging. Maximum likelihood methods are well-suited to simultaneous analysis of multiple fields with overlapping sources and to combining data from multiple gamma ray observatories. Such methods also offer an alternative approach to estimating the IACT cosmic ray background and consequently an enhanced sensitivity to sources that may be as large as the telescope field of view. We report here on the current status and performance of a maximum likelihood technique for the IACT VERITAS.
Cold Dark Matter (CDM) theory, a pillar of modern cosmology and astrophysics, predicts the existence of a large number of starless dark matter halos surrounding the Milky Way (MW). However, clear observational evidence of these dark substructures remains elusive. Here, we present a detection method based on the small, but detectable, velocity changes that an orbiting substructure imposes on the stars in the MW disk. Using high-resolution numerical simulations we estimate that the new space telescope Gaia should detect the kinematic signatures of a few starless substructures provided the CDM paradigm holds. Such a measurement will provide unprecedented constraints on the primordial matter power spectrum at low-mass scales and offer a new handle onto the particle physics properties of dark matter.
Narrow stellar streams in the Milky Way halo are uniquely sensitive to dark-matter subhalos, but many of these subhalos may be tidally disrupted. I calculate the interaction between stellar and dark-matter streams using analytical and $N$-body calculations, showing that disrupting objects can be detected as low-concentration subhalos. Through this effect, we can constrain the lumpiness of the halo as well as the orbit and present position of individual dark-matter streams. This will have profound implications for the formation of halos and for direct and indirect-detection dark-matter searches.
Virialized Ultra-Light Fields (VULFs) are viable cold dark matter candidates and include scalar and pseudo-scalar bosonic fields, such as axions and dilatons. Direct searches for VULFs rely on low-energy precision measurement tools. While the previous proposals have focused on detecting coherent oscillations of the VULF signals at the VULF Compton frequencies at individual devices, here I consider a network of such devices. VULFs are essentially dark matter {em waves} and as such they carry both temporal and spatial phase information. Thereby, the discovery reach can be improved by using networks of precision measurement tools. To formalize this idea, I derive a spatio-temporal two-point correlation function for the ultralight dark matter fields in the framework of the standard halo model. Due to VULFs being Gaussian random fields, the derived two-point correlation function fully determines $N$-point correlation functions. For a network of $N_{d}$ devices within the coherence length of the field, the sensitivity compared to a single device can be improved by a factor of $sqrt{N_{d}}$. Further, I derive a VULF dark matter signal profile for an individual device. The resulting line shape is strongly asymmetric due to the parabolic dispersion relation for massive non-relativistic bosons. I discuss the aliasing effect that extends the discovery reach to VULF frequencies higher than the experimental sampling rate. I present sensitivity estimates and develop a stochastic field SNR statistic. Finally, I consider an application of the developed formalism to atomic clocks and their networks.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
