Constraining axion and compact dark matter with interstellar medium heating

Cold interstellar gas systems have been used to constrain dark matter (DM) models by the condition that the heating rate from DM must be lower than the astrophysical cooling rate of the gas. Following the methodology of Wadekar and Farrar (2021), we use the interstellar medium of a gas-rich dwarf galaxy, Leo T, and a Milky Way-environment gas cloud, G33.4-8.0 to constrain DM. Leo T is a particularly strong system as its gas can have the lowest cooling rate among all the objects in the late Universe (owing to the low volume density and metallicity of the gas). Milky Way clouds, in some cases, provide complementary limits as the DM-gas relative velocity in them is much larger than that in Leo T. We derive constraints on the following scenarios in which DM can heat the gas: $(i)$ interaction of axions with hydrogen atoms or free electrons in the gas, $(ii)$ deceleration of relic magnetically charged DM in gas plasma, $(iii)$ dynamical friction from compact DM, $(iv)$ hard sphere scattering of composite DM with gas. Our limits are complementary to DM direct detection searches. Detection of more gas-rich low-mass dwarfs like Leo T from upcoming 21cm and optical surveys can improve our bounds.Comment: 10+6 pages, 8 figures. Version appearing in PRD. Added more realistic calculation of escape velocity in Leo

Zeldovich pancakes at redshift zero: the equilibration state and phase space properties

One of the components of the cosmic web are sheets, which are commonly referred to as Zeldovich pancakes. These are structures which have only collapsed along one dimension, as opposed to filaments or galaxies and cluster, which have collapsed along two or three dimensions. These pancakes have recently received renewed interest, since they have been shown to be useful tools for an independent method to determine galaxy cluster masses. We consider sheet-like structures resulting from cosmological simulations, which were previously used to establish the cluster mass determination method, and we show through their level of equilibration, that these structures have indeed only collapsed along the one dimension. We also extract the density profiles of these pancake, which agrees acceptably well with theoretical expectations. We derive the observable velocity distribution function (VDF) analytically by generalizing the Eddington method to one dimension, and we compare with the distribution function from the numerical simulation.Comment: 10 pages, 8 figures, accepted by MNRA

Comment on the paper "Calorimetric Dark Matter Detection with Galactic Center Gas Clouds"

The paper "Calorimetric Dark Matter Detection with Galactic Center Gas Clouds" (Bhoonah et al. 2018) aims to derive limits on dark matter interactions by demanding that heat transfer due to DM interactions is less than that by astrophysical cooling, using clouds in the hot, high-velocity nuclear outflow wind of the Milky Way ($T_{wind} \sim 10^{6-7}$ K, $V_{wind} \sim$ 330 km/s). We argue that clouds in such an extreme environment cannot be assumed to be stable over the long timescales associated with their radiative cooling rates. Furthermore, Bhoonah et al. (2018) uses incorrect parameters for their clouds.Comment: 2 pages, 1 figure. Version appearing in Phys. Rev. Let

Modelling the galaxy–halo connection with machine learning

To extract information from the clustering of galaxies on non-linear scales, we need to model the connection between galaxies and haloes accurately and in a flexible manner. Standard halo occupation distribution (HOD) models make the assumption that the galaxy occupation in a halo is a function of only its mass, however, in reality; the occupation can depend on various other parameters including halo concentration, assembly history, environment, and spin. Using the IllustrisTNG hydrodynamical simulation as our target, we show that machine learning tools can be used to capture this high-dimensional dependence and provide more accurate galaxy occupation models. Specifically, we use a random forest regressor to identify which secondary halo parameters best model the galaxy–halo connection and symbolic regression to augment the standard HOD model with simple equations capturing the dependence on those parameters, namely the local environmental overdensity and shear, at the location of a halo. This not only provides insights into the galaxy formation relationship but also, more importantly, improves the clustering statistics of the modelled galaxies significantly. Our approach demonstrates that machine learning tools can help us better understand and model the galaxy–halo connection, and are therefore useful for galaxy formation and cosmology studies from upcoming galaxy surveys

New binary black hole mergers in the LIGO-Virgo O3b data

We report the detection of 5 new candidate binary black hole (BBH) merger signals in the publicly released data from the second half of the third observing run (O3b) of advanced LIGO and advanced Virgo. The LIGO-Virgo-KAGRA (LVK) collaboration reported 35 compact binary coalescences (CBCs) in their analysis of the O3b data [1], with 30 BBH mergers having coincidence in the Hanford and Livingston detectors. We confirm 17 of these for a total of 22 detections in our analysis of the Hanford-Livingston coincident O3b data. We identify candidates using a search pipeline employing aligned-spin quadrupole-only waveforms. Our pipeline is similar to the one used in our O3a coincident analysis [2], except for a few improvements in the veto procedure and the ranking statistic, and we continue to use an astrophysical probability of one half as our detection threshold, following the approach of the LVK catalogs. Most of the new candidates reported in this work are placed in the upper and lower-mass gap of the black hole (BH) mass distribution. One BBH event also shows a sign of spin-orbit precession with negatively aligned spins. We also identify a possible neutron star-black hole (NSBH) merger. We expect these events to help inform the black hole mass and spin distributions inferred in a full population analysis.Comment: 16 pages, 12 figure

A new approach to template banks of gravitational waves with higher harmonics: reducing matched-filtering cost by over an order of magnitude

Searches for gravitational wave events use models, or templates, for the signals of interest. The templates used in current searches in the LIGO-Virgo-Kagra (LVK) data model the dominant quadrupole mode $(\ell,m)=(2,2)$ of the signals, and omit sub-dominant higher-order modes (HM) such as $(\ell,m)=(3,3)$, $(4,4)$, which are predicted by general relativity. Hence, these searches could lose sensitivity to black hole mergers in interesting parts of parameter space, such as systems with high-masses and asymmetric mass ratios. We develop a new strategy to include HM in template banks that exploits the natural connection between the modes. We use a combination of post-Newtonian formulae and machine learning tools to model aligned-spin $(3,3)$, $(4,4)$ waveforms corresponding to a given $(2,2)$ waveform. Each of these modes can be individually filtered against the data to yield separate timeseries of signal-to-noise ratios (SNR), which can be combined in a relatively inexpensive way to marginalize over extrinsic parameters of the signals. This leads to a HM search pipeline whose matched-filtering cost is just $\approx 3\times$ that of a quadrupole-only search (in contrast to being $\approx\! 100 \times$, as in previously proposed HM search methods). Our method is effectual and is generally applicable for template banks constructed with either stochastic or geometric placement techniques. Additionally, we discuss compression of $(2,2)$-only geometric-placement template banks using machine learning algorithms.Comment: 12+2 pages, 7+1 figures. The template bank described here will be publicly available at https://github.com/JayWadekar/GW_higher_harmonics_searc