Metamorphosis of dwarf halo density profile under dark matter decays
Abstract
We study the density profile of a dwarf halo in the decaying dark matter (DDM) cosmology, using a new algorithm that resolves halo density profiles down to the innermost pc robustly with high efficiency. Following Schwarzschild’s orbit-based method, we have also developed a simplified model to calculate the DDM halo density profiles, which agree remarkably well with those from N-body simulations. Both zoom-in simulations and the simplified model reveal that dark matter decays lead to the flattening of central density and overall reduction of density in dwarf halos, and the underlying physics mechanisms are well illustrated by the simplified model. The slowly-rising scaled rotation curves of DDM dwarf halos agree with the observation of local dwarf galaxies. Our results suggest that the DDM holds great potential for resolving the small-scale problems faced by the cold dark matter (CDM) model.
1 Introduction
Dark matter (DM) is a crucial ingredient of modern cosmology. It is needed to account for the anisotropy of the cosmic microwave background (CMB) [Wright et al., 1992, Spergel et al., 2003], large-scale structure formation and evolution [see, e.g., Springel et al., 2006], dynamics of galaxy clusters and groups [see, e.g., Zwicky, 1933], flattened rotation curves of late-type galaxies [see, e.g., Bosma, 1981], and mismatch between mass centers revealed by gravitational lensing and X-ray emission respectively [see, e.g., Markevitch et al., 2002]. While the DM mass density is about times higher than that of ordinary matter [Planck Collaboration et al., 2018a], its nature remains a mystery. Regardless of its composition, the microscopic properties of DM directly affect its cosmic mass distribution. Therefore, through a comparison between theoretical calculation and observation on DM distributions, properties of DM can be constrained. From 1970s, N-body simulation has become an indispensable theoretical tool for dealing with DM’s nonlinear clustering. In 1980s, N-body simulations were used to rule out a neutrino-dominated Universe [White et al., 1983] and establish the canonical cold dark matter (CDM) model [Peebles, 1982, Blumenthal et al., 1984, Frenk et al., 1985, Davis et al., 1985]. With the improving resolution of N-body simulations, the inner structure of CDM halos were first resolved in 1990s [Dubinski & Carlberg, 1991, Navarro et al., 1996b]. These high-resolution simulations revealed a central cusp in CDM halos, characterized by a power-law density profile with , independent of the halo mass. The cuspy density profile results in a steeply rising rotation curve as , which is incompatible with the observed solid-body rotation , , of dark matter-dominated dwarf and low surface brightness (LSB) galaxies [Flores & Primack, 1994, Moore, 1994, Moore et al., 1999b]. This tension is referred to as the core-cusp problem [de Blok, 2010]. Together with other small-scale problems it challenges the standard CDM model [Bullock & Boylan-Kolchin, 2017].
Within the CDM framework, the resolution of the core-cusp problem relies on baryonic feedbacks. The idea is that the energy released from starburst radiation or supernovae explosions is indirectly transferred to DM particles via frequent fluctuations of potential well driven by repeated and fast gas outflows from the galactic centre [Navarro et al., 1996a, Pontzen & Governato, 2012]. The extra energy gained by DM particles expands their orbits and converts an initial cuspy density profile into a cored one. The degree of conversion depends on the total energy released from feedbacks measured by stellar mass , the total baryonic matter opposing the expansion, and the energy transfer efficiency. However, the last one is found to be very sensitive to the star formation gas density threshold , a numerical parameter commonly adopted in sub-grid models of galaxy formation [Benítez-Llambay et al., 2019, Dutton et al., 2019]. Cosmological simulations with a large report core formation in simulated dwarf galaxies [Governato et al., 2010, Tollet et al., 2016, Chan et al., 2015] while the dark matter cusp remains in simulations using a smaller [Sawala et al., 2016]. Till now, a consensus has not been reached about whether baryonic processes can solve the core-cusp problem, and it continues to cast doubt on the CDM model.
Apart from the cored profile, many dwarf and LSB galaxies also have similar shape of rotation curves, implying a self-similar dark matter structure [Kravtsov et al., 1998, Salucci & Burkert, 2000]. This conformity is unlikely to be due to chaotic and dramatic baryonic processes [Burkert, 1995, Navarro, 2019], but rather a clue to the nature of dark matter beyond CDM. In this work we study a decaying dark matter (DDM) model, which describes two-body decays of DM:
(1) |
where stands for the unstable mother DM particle, is a massive stable daughter DM particle and is a light and relativistic particle. The DM has therefore multiple components. The dynamics of the two-body decays in this model are fully controlled by two parameters: the decay rate , or half-life , of mother particles and the energy of in unit of the mother particle’s rest mass. The recoil velocity of daughter particles in the center-of-mass frame of their mothers can be expressed as , where is the speed of light.
There are diverse behaviours of the DDM model depending on the values of and . For a very small such that almost all mother particles decay into daughter particles before any non-linear structures have formed, the DDM model is similar to the warm dark matter (WDM) model with a free-streaming length determined by the recoil velocity [Kaplinghat, 2005, Strigari et al., 2007]. For a large such that the decays convert a fair amount of energy from the matter component to the relativistic species, the expansion history of the whole Universe will be altered [see, e.g., Vattis et al., 2019]. For a broad range of while km s-1, the 1D Lyman- forest power spectrum predicted by the DDM model is shown to be consistent with observation data [Wang et al., 2013], implying that the model behaves like CDM at large scale. Inside a parameter region outlined by km s-1 (or equivalently ) and Gyr, decays significantly heat up DM inside dwarf-sized halos, and are relevant for the small-scale problems of CDM [Abdelqader & Melia, 2008, Peter & Benson, 2010, Wang et al., 2014]. In light of this, we revisit the core-cusp problem and study the density profiles of dwarf halos in the DDM model using high-resolution cosmological N-body simuations, which is absent from previous studies.
This paper is structured as follows: we first review previous DDM algorithms for N-body simulations and then introduce our new DDM algorithm, test its performance and calibrate numerical parameters for high-resolution zoom-in simulations in Section 2. An overview of our highest-resolution simulation suite is given in Section 3. Section 4 details our mathematical modelling of the evolution of an isolated halo in the DDM cosmology. We present our main results in Section 5, followed by more extensive discussions about the DDM model in Section 6. We summarize in Section 7.
2 Methodology
2.1 Overview of DDM N-body algorithms
In the DDM model, light particle does not take part in the structure formation directly, and is ignored in the following discussion. For the mother particle and daughter particle , their Boltzmann equations are
(2) |
and
(3) |
respectively, where and are the corresponding phase-space mass densities. The DDM matter density evolution can be simulated by using the N-body method once the collision terms in equations (2) and (3) are properly handled. The first DDM N-body simulation was presented in Peter et al. [2010, hereafter PMK10], where the DDM model (1) was realized on simulation particle basis. In PMK10, each mother simulation particle has a decay probability. Once chosen for decay, the mother simulation particle is flagged to be a daughter simulation particle and receives a random velocity kick at the same time. This Monte-Carlo sampling of decays is carried out at each simulation timestep. Therefore, the global decay rate is sampled continually and precisely for the whole system, while the local decay rate fluctuates around the global value with an amplitude depending on the local number density of mother simulation particles. As time goes on, the total number of mother simulation particles drop and the matter density field becomes more and more nonlinear. It can be seen that the decay sampling precision of PMK10 is not uniform in both space and time domains. It is also an intrinsic challenge for the PMK10 algorithm to resolve the central structures of DM halos due to the limited number of mother simulation particles there.
To achieve a uniform decay sampling in both space and time domains, Cheng et al. [2015, hereafter CCT15] proposed a DDM algorithm based on a discretization of the Boltzmann equations (2) and (3). In CCT15, decays are only sampled at several simulation timings, when each mother simulation particle is split partly to generate a new daughter simulation particle which is kicked randomly at its birth. At other simulation times, all simulation particles are evolved according to the collisionless Boltzmann equation:
(4) |
The number of mother particles is kept unchanged, and the mass-splitting procedure is the same for all mother simulation particles. Therefore, the decays can be sampled uniformly both in space and time domains. Two numerical parameters are introduced in this algorithm: , the number of mass-splittings throughout a simulation, and , the number of daughter simulation particles produced per mother simulation particle at each mass-splitting.
Consider a simulation following the CCT15 algorithm with splittings. Initially it has mother simulation particles. As each mass-splitting procedure generates daughter simulation particles, the total number of daughter simulation particles will increase to by the end of the simulation. For typical values that achieve satisfactory numerical convergence, such as and , the final number of daughter simulation particles is larger than that of mother simulation particles by an order of magnitude. Generally the number of simulation particles serves as a measure of the precision of an N-body simulation. However this measure does not apply to the CCT15 algorithm, because the phase space distribution of daughter particles is inferred from discrete mother simulation particles, not from an underlying continuous distribution function. The sampling resolution of daughter simulation particles is limited by that of the mother simulation particles, which is set in the initial condition. This is also true for the PMK10 algorithm. Hence, increasing the number of daughter simulation particles contributes little to improving the simulation’s resolution. On the other hand, the large demand for computing resources of the CCT15 algorithm hinders its practical usage in large cosmological simulations. Therefore improvement of the CCT15 algorithm is needed for our purpose of running high resolution zoom-in simulations.
2.2 An improved DDM algorithm
We follow the framework outlined in CCT15. In our algorithm, the mother simulation particles decay and give birth to daughter simulation particles only at a limited number of decay instances, called breakpoints. The breakpoints are ordered on the time axis in a way that in each phase, the time interval between two adjacent breakpoints, the same number of mother simulation particles decay into daughter simulation particles. When a breakpoint is reached, each mother simulation particle is splitted into a less-massive mother simulation particle and a daughter simulation particle according to the decayed fraction. The newly generated daughter simulation particles are called auxiliary daughters. The mass-splitting procedure increases the total number of simulation particles by . To save memory as well as to speed up the whole simulation, we only track the motion of these auxiliary daughters for each phase. When the simulation arrives at the next breakpoint, the auxiliary daughters born at the last breakpoint will undergo a random selection process such that only a fraction of them survive and are renamed as permanent daughters. The remaining auxiliary daughter particles are eliminated so that the memory occupied by them is released. All permanent daughters will be traced to the end of the simulation. Therefore there are two states of the daughter simulation particles in our scheme: auxiliary and permanent.
The auxiliary daughters help to conserve the local matter mass when a decay occurs, a basic conservation observed in both CCT15 and PMK10. After diffusing into the environment, these auxiliary daughters are replaced by permanent daughters, which have a smaller population, hence heavier. The transition from the auxiliary state to permanent state decreases the resolution of daughter particles. Though numerical side-effects can be introduced from the resolution degradation, it is controllable by tuning the survival fraction . Suppose there are mother simulation particles initially and they go through breakpoints. When the simulation is finished, there are permanent daughters in total, with . Setting brings our algorithm back to CCT15. We implement this algorithm in an individual module named DDMPLUGIN, see Appendix A for details. It is designed to be compatible with other N-body codes such that the physics associated with dark matter decay is self-contained. This is achieved as follows: during each phase, the whole system is evolved by an N-body CDM code. When a breakpoint is reached, the system’s final state is output to the DDMPLUGIN module, which implement the physical effects of dark matter decays. Hence an updated system state is generated. The DDMPLUGIN module then outputs this new state to be the initial condition for next phase’s evolution, which is again tracked by the N-body CDM code. Therefore our DDM simulation can be easily implemented in any N-body code, an advantage rooted in the CCT15 algorithm.
2.3 Cosmological zoom-in simulation
To study the density profiles of dark matter halos in the DDM cosmology, we run cosmological zoom-in simulations [Oñorbe et al., 2014] using the DDMPLUGIN module with the N-body code P-GADGET3, a descendant of the public TreePM code GADGET2 [Springel, 2005]. In all simulations, a flat geometry with a cosmological constant is assumed for the background cosmology, where the cosmological parameters are taken from the final results of Planck TT,TE, EElowE measurements: , , , and [Planck Collaboration et al., 2018b]. Initial conditions are generated by using the code MUSIC [Hahn & Abel, 2011] with the BBKS transfer function [Bardeen et al., 1986]. The uncertainties induced by the choice of transfer functions are quantified in Appendix B.1. The effect turns out to be negligible. Dark matter halos are identified by the halo finder AHF [Gill et al., 2004, Knollmann & Knebe, 2009], with Bryan and Norman’s fitting formula [Bryan & Norman, 1998] for overdensity calculation. For our adopted cosmology, dark matter halos at redshift are defined by the virial radius within which the mean overdensity is about 103.4 times the critical density . Based on the position of halo centre, the virial radius, and the best-fit Navarro-Frenk-White (NFW) profile provided by AHF, we collect all particles bound to the target halo and use our own code to measure its radial mass distribution , the average matter density inside radius . The two-body relaxation time is estimated for certain radii using the method documented in Binney & Tremaine [2008]. Only those radii with larger than the Hubble time are considered to be reliably resolved [Fukushige & Makino, 2001].

To select a candidate halo for zoom-in simulations, we run a full-box CDM cosmological simulation starting from redshift . It uses simulation particles inside a periodic cubic box with a width of Mpc, corresponding to a physical length of Mpc at . Each CDM simulation particle has a mass of , which is the coarse resolution of the full-box simulation. We select dark matter halos at redshift by two criteria: small Lagrangian volume and being isolated from larger structures. The selected dark matter halo has a virial mass and an NFW concentration , closely following the theoretically motivated relation of Diemer & Joyce [2018]. Its large-scale environment is illustrated in Figure 1, showing that the selected halo is far away from surrounding larger structures.
For zoom-in simulations centred at the selected halo, the initial density fluctuations inside its Lagrangian volume are sampled by high-resolution particles while its large-scale environment is represented by coarse resolution particles. A buffer volume is created between the above two regions to avoid large resolution gradient. We label the resolution level of a zoom-in simulation by an integer . A level- zoom-in simulation has a mass resolution equivalent to that of a full-box N-body simulation using CDM particles in its initial condition. We adopt the empirical formula recommended by Power et al. [2003] to calculate the gravitational softening lengths for high-resolution CDM zoom-in simulation particles. As for DDM zoom-in simulations, the decays of mother simulation particles are only switched on inside the high-resolution volume. It is reasonable since we do not consider decay parameters that result in significant deviations from the CDM large-scale matter distribution. Gravitational softening lengths for high-resolution DDM particles are set to be the same as those of CDM zoom-in particles which have the same resolution level. This reflects the fact that the resolution of a DDM simulation is constrained by its initial condition. We also require that zoom-in halos are free of contamination by lower resolution particles within their virial radii for a clean analysis. In the following subsection, we test and calibrate and of our DDM algorithm for dark matter halo density profile study.
2.4 Numerical parameters calibration


The parameter controls the decay sampling frequency, and a larger value of it leads to a finer sampling of the decay history. In CCT15, the total number of daughter particles is linearly proportional to . Hence it cannot be arbitrarily large, otherwise the computational work load will be huge. However this constraint on is released in our algorithm as the total number of daughter particles is solely determined by . For DDM zoom-in simulations used in this study, we set by default. It proves to be large enough for acceptable numerical convergences (see Appendix B.2 for details) .
In our algorithm, controls the total number of simulation particles. To test its effects on the halo average density profile, we tried three values: and . A simulation using the CCT15 algorithm (equivalent to ) is also carried out for comparison. All simulations are run in level- resolution using the same decay parameters km s-1 and Gyr. The density profiles at are measured down to the inner-most resolved radii and are shown in the left panel of Figure 2. The density profiles with different values of all converge to CCT15’s density profile within at all resolved radii. It implies that the radial mass distribution is insensitive to the value of when . We further run a level- simulation using the same decay parameters for . In the right panel of Figure 2, we compare all level- profiles with the level- one. For kpc, all level- profiles are systematically lower than the level- profile. The differences continue to grow when approaches to the halo centre. A large does help to narrow the differences down, however the cost is also huge: the gain in precision is only about by increasing from to (the CCT15 algorithm). As a larger value of brings forth a larger number of simulation particles, the two-body relaxation converged radius decreases as the value of increases. However, for , cannot be taken as the inner-most resolved radii anymore, because the deviation from the level- profile at also grows with the and reaches level for . Hence for the level- runs, increasing the value of does not decrease the inner-most resolved radius reliably. On the contrary, the inner-most resolved limit set by the of the run is worthy: the density profile of the level- resolution converges to that of the level- resolution within for all radii larger than kpc. Then we can safely use without worrying about possible degradation of resolution and take as the inner-most resolved radius. For our highest resolution zoom-in simulations, the level- runs, we use and . The results are presented in the next section.
3 High-resolution zoom-in simulations
Name | |||||||||
---|---|---|---|---|---|---|---|---|---|
(km s-1) | ( kpc) | ( kpc) | ( kpc) | (km s-1) | |||||
CDM | … | … | 5.17 | 35.0 | 0.254 | 0.407 | 27.7 | 21.6 | 1.33 |
V20T3 | 20.0 | 3.00 | 4.22 | 32.7 | 0.291 | 0.904 | 26.0 | 15.0 | 2.35 |
V20T7 | 20.0 | 6.93 | 4.41 | 33.2 | 0.224 | 0.805 | 27.4 | 17.3 | 2.36 |
V20T14 | 20.0 | 14.0 | 4.70 | 33.9 | 0.200 | 0.684 | 28.7 | 19.3 | 2.41 |
V30T3 | 30.0 | 3.00 | 2.89 | 28.8 | 0.366 | 1.72 | 20.1 | 6.99 | 1.93 |
V30T7 | 30.0 | 6.93 | 3.32 | 30.2 | 0.254 | 1.23 | 23.3 | 11.4 | 1.91 |
V30T14 | 30.0 | 14.0 | 4.05 | 32.3 | 0.215 | 0.810 | 26.4 | 16.8 | 2.08 |
V40T3 | 40.0 | 3.00 | 0.350 | 14.3 | 0.470 | 3.58 | 10.0 | 4.64 | 0.377 |
V40T7 | 40.0 | 6.93 | 1.79 | 24.6 | 0.278 | 1.69 | 18.1 | 9.93 | 1.19 |
V40T14 | 40.0 | 14.0 | 3.26 | 30.0 | 0.234 | 1.00 | 23.7 | 13.5 | 1.67 |
Our level- simulation suite comprises 10 cosmological zoom-in simulations: 1 CDM and 9 DDM realizations. All 10 simulations use the same initial condition at redshift . The high-resolution volume in the initial condition is a cuboid with three edge-lengths being , and Mpc, centering at its parent simulation box. The mass of each high-resolution particle is . The gravitational softening length of high-resolution CDM particles is set to be pc, frozen to a physical length about pc after . All DDM realizations follow the same softening length assignment scheme such that the force resolution of our simulation suite is uniform. All our zoom-in halos are free of contamination by low-resolution particles within their virial radii. The DDM realizations differ from each other in the combination of and . We have used values of : , and Gyr, with corresponding decayed fractions , and , respectively. For each , we use different values of : , and km s-1. These realizations constitute a rough sampling of the interesting region in the parameter space. Halo expansion has been observed in the 9 DDM zoom-in halos as they generally have smaller virial masses but lower concentrations compared to the corresponding CDM halo. The virial masses, virial radii and other global properties of all zoom-in halos are summarized in Table 1. We study the physics accounting for the DDM halo expansion in next section.
4 A simplified semi-analytic model of DDM density profiles
The halo expansion in the DDM model is driven by two primary physical processes. The first one is the decay itself. On average, the kinetic energy of newly born daughter particles are greater than those of their mothers. This extra kinetic energy drives the orbits of daughters outwards, hence expanding the whole halo. We call this the Step- expansion. A consequence of this expansion is the weakening of the gravitational potential, triggering the Step- expansion: the bulk particles’ orbits expand outwards to rebalance the weakened gravity with the inertial force seen in the orbits’ rotating frames. Cen [2001] considers a special case of the two-step expansion: and , where and are the halo’s typical escape velocity and dynamical time, respectively. The large unbinds all daughter particles during the Step- expansion while the slow decay simplifies the Step- evolution to an adiabatic expansion. Starting from an NFW density profile, the resulting density profile turns out to remain an NFW shape, but with a smaller concentration and a lower normalization density (see also the relavant discussion in Peter [2010]). Sánchez-Salcedo [2003] considers the situation where most of daughter particles are bound to the halo () and the halo expands adiabatically (). Through simple semi-analytic calculations, he argued that the cored profile is a natural result of the two-step expansion. In this section, we present a general formalism to implement the two-step expansion.
Our model assumes that a dark matter halo forms at a high redshift when only a small fraction of mother particles have decayed. Initially the halo is CDM-like with an NFW density profile. Then decays proceed and the density profile evolves. The decay of a mother particle is a random process and does not have a preferential direction. On average, each newborn daughter particle acquires an additional amount of kinetic energy from the mass deficit of its mother particle:
(5) |
where is the kinetic energy of the decayed mother particle and is the expected kinetic energy of its daughter particle with mass . Similarly, the mean angular momentum of a daughter particle is the same as that of its mother, ,
(6) |
Based on equation (5) and equation (6), we build our simplified model using Schwarzschild’s orbit-based method [Schwarzschild, 1979] (see Chanamé [2010] for a short overview), which represents a collisionless system by a large library of particle orbits. Physical quantities, such as mass distribution, are then derived through constructing superpositions of these orbits.
Given a gravitational potential , a particle’s orbit is a function of its total energy and angular momentum . The joint distribution of and gives the whole library of particle orbits. To make the model as simple as possible, we assume all mother particles take circular orbits around the halo center, with random orientations of orbital planes. The daughter particles take up rosette orbits.
Now we consider the enclosed mass profile as a superposition of orbits. Mathematically, all orbits in the library form a set. We name it as Olib. For each element in Olib, a weighting factor is assigned such that is the summation of all particle masses weighted by over the set Olib:
(7) |
where is the mass of the particle moving in the orbit . The weighting factor can be constructed as the fraction of time the particle spends inside the sphere :
(8) |
where is the duration that a particle travels inside the sphere within an orbital period . The weighting factors for circular orbits are step functions since a circle with radius is either totally inside or totally outside the sphere :
(9) |
Similarly for a rosette orbit, if its perihelion (aphelion ) is larger (smaller) than , then its weighting factor is 0 (1). If , the weighting factor is calculated as follows:
(10) |
where is the effective potential.
Consider the Step- expansion during a small time interval such that the gravitational potential remains static. Consider a pair of mother and daughter particles: a circular orbit with radius before decay and the corresponding rosette orbit after decay. According to equation (6), they share the same specific angular momentum , thus the same effective potential:
(11) |
The value of can be obtained by considering the circular motion at radius , where reaches its minimum. As for the rosette orbit, its perihelion and aphelion are the two roots of the following equation from equation (5):
(12) |
They both depend on the radius of its mother particle’s orbit. If equation (12) has only one root, daughter particle is considered to be unbound and makes no contribution to the mass profile ().
After , the mass increment of daughter particles inside the sphere can be calculated by considering the contributions from the daughter particles which are born during this time interval:
(13) |
where is the fraction of mother particles decayed during :
(14) |
is the density profile of mother particles at time . The mass profile of mother particles declines uniformly by a factor of :
(15) |
Then after the Step- expansion, the total mass profile changes by an amount of
(16) |
We proceed to consider the Step- expansion in the time interval . For the sphere at , the adiabatic expansion reads:
(17) |
from which , the radius at , can be derived. Initially the total mass inside is . After , the total mass inside is . Therefore the mass profile () evolves a bit.
Through the two-step expansion we have evolved the whole system from to . We loop the procedures listed above and evolve the whole system from the initial time to the final time .
5 Results
The simplified semi-analytic model presented in Section 4 is implemented with a well-tested numerical code called SemiCore. Starting from the best-fit NFW profile of the level- CDM halo ( and ), we run SemiCore with the combinations of and listed in Table 1, with the initial and final times being the same as those of cosmological N-body simulations. In Figure 3, we show the present () average density profile ratios, , between the DDM-CDM halo pairs which evolve from the same primeval local overdensity field. We compare the results of the SemiCore model with those from the level- N-body simulations. For the SemiCore model, is calculated from the input NFW profile.

The simulation data reveals that dark matter decays reduce the mass profile throughout the dwarf halos. The global reduction amplitude increases as increases or decreases. For a given pair of decay parameters, the difference between DDM and CDM halos become more pronounced as approaches to the halo center. These trends are well reproduced by the SemiCore model. Furthermore, the average density ratios predicted by SemiCore agree with those from sophisticated N-body simulations to better than , for all resolved radii and for all combinations of and considered in this study. The triumph of SemiCore confirms the two-step expansion scenario for explaining the halo expansion induced by dark matter decays.
Figure 3 also shows that SemiCore systematically overpredicts the mass reduction in the inner region (). It may be related to the simplified assumption of circular orbits for all mother particles. The same effect was observed in modelling adiabatic contraction of dark matter by circular orbits, which leads to an enhancement of the central density relative to the results of high resolution simulations [Gnedin et al., 2004]. In the outer region (), N-body simulations show that the ratios continue to grow while the SemiCore model predicts a flattened or slightly declining shape. Notice that the decay of a mother particle in a rosette orbit generally produces daughter particles that reach larger , compared to those from a circular mother orbit. Also, there is mass accretion from the environment in N-body simulations, which is absent in the SemiCore model. Both factors can be responsible for the discrepancy seen in the outer halo region.

In Figure 3, the average density ratios from N-body simulations display a common shape: rising inner and outer regions connected by an extended plateau. A positive slope of the curve implies the flattening of DDM density profile compared to that of its CDM countpart. We calculate the rotation curve scaled by , the circular velocity at radius where [Hayashi & Navarro, 2006]. In Figure 4, we plot the scaled rotation curves for all level- DDM halos, and their values of and are listed in Table 1. The scaled rotation curves based on the cuspy NFW profile and cored Burkert profile [Burkert, 1995] are also plotted together for comparison. The DDM curves spread between the two theoretical curves in two groups. One is made up of DDM halos: , , and . Their scaled rotation curves are closer to the NFW curve than the Burkert one. The remaining halos form the second group which features a significant deviation from the NFW curve and is much closer to the Burkert curve. For , a member of the pro-NFW group, and , a member of the pro-Burkert group, we further plot their scaled rotation curves of mother and daughter components in Figure 4. Surprisingly, the scaled daughter (mother) rotation curve of () follows the Burkert (NFW) shape. Since has the strongest decay effect while has the weakest, the simulation results show that the halo expansion due to dark matter decays can flatten the halo density profile and transform it from a cuspy shape to the cored shape, depending on the combination of and for a given dwarf halo mass.
6 Discussion


The flattening of the central density profile of dwarf halos is needed to resolve the core-cusp problem of CDM. We show DDM and CDM’s rotation curves together with observational data in Figure 5. Halo is a representative for the flattened DDM rotation curves. Two dwarf galaxies UGC07866 and UGCA444 are selected from the Spitzer Photometry Accurate Rotation Curves (SPARC) database [Lelli et al., 2016] for comparison as they have roughly the same mass as the halo [Li et al., 2020]. In order to cover the possible mass range where the two SPARC galaxies reside, we use SemiCore to calculate the rotation curves of two DDM halos, with current virial masses of and , respectively. The two SemiCore runs both use km s-1 and . Their initial halos are set to follow the CDM relation [Diemer & Joyce, 2018]. For the CDM rotation curve, we assume an NFW profile and a fixed virial mass , the same as the halo . In the left panel of Figure 5, the DM contributed rotation curves of dwarf galaxy UGC07866 and UGCA444 are shown with observational uncertainties. The simulated or modelled DDM and CDM’s rotation curves have been scaled by a factor of , where is the cosmic baryon fraction of global matter density. It shows that the DDM rotation curves naturally follow the observational data while the CDM ones give good fit to data only with a small concentration, which is unusual for halos formed in CDM N-body simulations [Dutton & Macciò, 2014]. The data points at small radii ( ) favour DDM curves as the NFW curves systematically overpredict the rotation velocities there. In the right panel of Figure 5, we plot the corresponding scaled rotation curves. With considerably large uncertainties, the observational data distribute between the NFW and Burkert curves at small radii, in good agreement with the DDM scaled rotation curves for dwarf galaxies. Our results agree with Sánchez-Salcedo [2003] in that the core-cusp problem can be solved in the DDM model with a recoil velocity smaller than the typical escape velocities of dwarf halos, provided that the decay half-life .

In Figure 6, we show the positions of the zoom-in dwarf halos in the DDM parameter space. They reside inside a region, shown in gold, where dark matter decays have prominent effects on the number density and inner structure of dwarf galaxies [Peter & Benson, 2010]. The zoom-in halos with a much more flattened central density are indicated by blue squares and the remaining by blue triangles. For km s-1 and , Wang et al. [2014] have run a zoom-in simulation on a Milky-Way sized host halo with the PMK10 algorithm. They found that the circular velocity profiles of the most massive subhalos pass through most of the data points from the classical Milky-Way dSphs, and therefore the too-big-to-fail problem [Boylan-Kolchin et al., 2011] is potentially resolved. The brown strip is the parameter region, shown by Abdelqader & Melia [2008] using a semi-analytic model that incorporates dark matter decays in the hierarchical formation history of dark matter halos, that can account for the deficit of dwarf galaxies in our local group, a puzzle closely related to the missing-satellites problem [Klypin et al., 1999, Moore et al., 1999a]. It can be seen that several different CDM problems can be solved by a common parameter subspace in the DDM model.

In Figure 7, we show that and have a power-law relation for DDM zoom-in dwarf halos:
(18) |
where the power index increases as decreases. Given the values of and , both being observable quantities, a relation between and can be derived from relation (18), implying a possible degeneracy in the DDM parameter space as far as the halo’s inner structure is concerned. As and are constants in the DDM model, from relation (18) we also find
(19) |
Since dwarf halos have a narrow virial mass spectrum and equation (19) depends only weakly on , an approximate universal density profile is expected. The observed value of can then be used to measure . We will study this possibility in a future work.
7 Conclusions
In this work we improved the DDM N-body algorithm by combining the advantages of the PMK10 and CCT15 algorithms. The new algorithm outperforms PMK10 in accuracy while demands much less computing resources than CCT15. Same as CCT15, the new algorithm samples dark matter decays only at certain times and evolves the whole N-body system in a collisionless way at other times. This feature enables the algorithm to be implemented in a plugin module called DDMPLUGIN, which can be used with any CDM N-body code.
We carried out high-resolution cosmological N-body simulations to study the density profiles of dwarf halos with the new DDM algorithm. Good numerical convergence was achieved, and we succeeded to resolve the halo structure robustly down to about pc. Compared to CDM counterparts, DDM dwarf halos have lower mass concentration and shallower density profile at the inner region. Adopting the orbit-superposition method for mass profile construction, we developed a simplified semi-analytic model for the DDM halo mass profile, which features rosette orbits for daughter particles and incorporates effects of dark matter decays and adiabatic expansion. Although simple, the model predicts DDM halo mass profiles that agree semi-quantitatively with resolved simulation profiles. It therefore illustrates clearly the physics mechanisms involved in the transformation from cusp to core density profiles.
We also calculated the scaled rotation curves for DDM simulation halos and compared them with dwarf galaxies from the SPARC database. The shape of the DDM rotation curve is shallower than that based on the NFW profile but steeper than the Burkert’s. The two SPARC dwarf galaxies favour the DDM shape of the rotation curve. Furthermore, we show that there is an approximate universal power-law relation between and for dwarf halos, which can be used to extract DDM parameters from observation data. Together with previous studies, this work supports the DDM cosmology, which keeps the success of CDM at large scale and reconciles the differences between observations and predictions from N-body simulations at small scale.
Appendix A DDMPLUGIN implementation
A.1 Decay of mother particles
At each breakpoint, the mother simulation particles will decay and release auxiliary daughter particles. Due to the random nature of dark matter decay, the N-body mother particles do not receive velocity kicks when decay occurs. Only their masses are affected and reduced by :
(A1) |
where and are the masses of a mother simulation particle just before and after the decay. Other information associated with the post-decay mother are exactly the same as those of the pre-decay mother. As for the newly generated auxiliary daughter particles, the decay kicks them away from their mother particles:
(A2) |
(A3) |
where is the recoil velocity, is a unit vector pointing to a random direction. The IDs of the auxiliary daughters are assigned as follows:
(A4) |
where is the total number of particles in the initial condition, is the smallest particle ID taken by the simulation, and is an integer ranging from to . Each auxiliary daughter particle gets a different value of randomly such that their IDs are different from each other. The masses of auxiliary daughter particles are determined by the decay half-life and the number of breakpoints :
(A5) |
where is the initial mass of a mother particle and is the time span of the whole simulation.
A.2 Auxiliary-permanent transition
The auxiliary-permanent transition of a daughter simulation particle is implemented as follows in the DDMPLUGIN module. First, the particle ID of an auxiliary daughter is decomposed into a pair of integers :
(A6) |
where is the quotient of by and the remainder. Auxiliary daughters satisfying survive and are flagged as permanent daughters. Auxiliary daughters with are eliminated from the simulation. This scheme ensures that the survival fraction is . Once an anxiliary daughter is flagged to be permanent, its mass and ID have to be modified while its position and velocity are retained. The particle mass needs modification such that the daughter particles’ total mass is unaffected by the transition:
(A7) |
where and are the particle masses of permanent and auxiliary daughters, respectively. A new ID is assigned to the permanent daughter in order to distinguish it from its pre-transition auxiliary state:
(A8) |
where is the total number of particles in the simulation just before the transition, is the smallest particle ID taken by the simulation, is the integer pair derived from the minimum ID of the auxiliary daughters that are flagged to be permanent daughters. The ID assignment scheme (A8) ensures that the particle IDs in the simulation are continuous and there is no spatial bias in the selection process. Position and velocity are invariant under the auxiliary-permanent transition:
(A9) |
(A10) |
The auxiliary-permanent transition will also be applied when the state of the system is output at certain simulation times. This operation keeps the consistency that only permanent daughter particles appear in the output file(s) of the simulation.
A.3 Gravitational softening length
In our implementation of the DDMPLUGIN module, the mother simulation particles, auxiliary daughter particles and permanent daughter particles are all labelled as halo particles, which are type according to Gadget’s classification. As they have the same particle type, their gravitational softening lengths are also the same.
Appendix B Numerical tests
B.1 Transfer function
Here we test the effects of uncertainties in the transfer function on the dark matter halo’s average density profile . Two different transfer functions, the default BBKS and Eisenstein-Hu [Eisenstein & Hu, 1998] with baryonic features, are used in the test. We run level- resolution CDM zoom-in simulations and measure the resulting profiles at . The results are shown in the left panel of Figure 8. The uncertainties on the average density profile are well within for most resolved radii. Hence the choice of transfer functions has little impact on our results.


B.2
We used three values to test the effects of on the halo average density profile : and with being . The decayed mass fraction per phase are , and for and , respectively. From the right panel of Figure 8, it can be seen that the average densities near a halo’s center are more easily affected by varying . It is clear that convergence can be achieved by increasing the value of . The relative differences in the average density profiles between and are within .
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