Dark matter (DM) represents roughly 84% of the matter content in the Universe (Planck Collaboration et al., 2020). However, unveiling its nature has proven a difficult endeavour, and none of the proposed candidates (from several extensions of the Standard Model to primordial black holes) have yet been detected. Cold DM is expected to form subhalos with masses many orders of magnitude below (Blumenthal et al., 1984; Wang et al., 2020a), which is roughly the mass above which galaxies can form (Zavala and Frenk, 2019). The abundance of subhalos is dependent on the nature of DM. This dependency can be explained, on the one hand, by the effect of the properties of the DM on the linear matter power spectrum. If for cold DM the minimum halo mass might be as small as (Zybin et al., 1999; Bringmann, 2009), microscopic properties of the DM particle, e.g. non-negligible thermal velocities or quantum pressure, introduce a cut-off at the small scales in alternative DM scenarios. On the other hand, the nature of DM, e.g. self-interactions, further impacts the non-linear growth of structures (Schneider et al., 2012; Vogelsberger et al., 2016). Detecting a dark subhalo would be the first direct evidence of DM clustering at small scales. Furthermore, constraints on the subhalo abundance would provide valuable information about the particle nature of DM.
Subhalos with masses lower than are unable to form stars and remain dark, thus hindering their detection. Strategies that aim to detect dark subhalos rely on measuring their gravitational signatures via stellar dynamics (Ibata et al., 2002; Yoon et al., 2011; Carlberg, 2012; Bovy et al., 2017; Banik et al., 2018; Bonaca et al., 2019; Benito et al., 2020; Feldmann and Spolyar, 2015; Buschmann et al., 2018) or gravitational lensing (Hezaveh et al., 2016; Van Tilburg et al., 2018; Díaz Rivero et al., 2018; Gilman et al., 2019; Brehmer et al., 2019; Vattis et al., 2020) and, in the case of several DM candidates, e.g. Weakly Interacting Massive Particles (WIMPs), on detecting the flux of final stable particles produced by DM annihilation or decay (Moliné et al., 2017; Calore et al., 2019; Coronado-Blázquez et al., 2019, 2021; Mirabal and Bonaca, 2021). The goal of searches based on stellar dynamics is to detect perturbations in the phase-space distribution of Milky Way (MW) stars induced by gravitational effects of passing subhalos. We can look for these perturbations in stellar streams (Ibata et al., 2002; Yoon et al., 2011; Carlberg, 2012; Bovy et al., 2017; Banik et al., 2018; Bonaca et al., 2019) and in the disk or the halo stars (Feldmann and Spolyar, 2015; Buschmann et al., 2018). In the present work we investigate the usage of an anomaly detection and classification algorithms in the search for the imprint caused in halo stars by passing substructures. In this way, we exploit the increasing size of observational datasets and state-of-the-art techniques in deep learning.
In recent years, deep learning techniques have been applied in the search for substructures in our Galaxy (Ostdiek et al., 2020; Necib et al., 2020; Shih et al., 2021). These detection methods assume that stars in the MW sharing a common origin should cluster in orbital properties and/or composition. Our search differs in that we aim to identify stars that, regardless of their origin, have their distribution in phase-space perturbed by the passage of a dark matter subhalo. For any identified star, it must be possible to test the halo hypothesis independently of the methodology used to select the candidates. One possibility could be to preselect the stars using a ML
-based classifier, followed by detailed hypothesis tests using e.g. the orbital arc method(Kipper et al., 2020, 2021) or the stellar wakes technique (Buschmann et al., 2018).
The raw data that we used, which are described in section 2, are three MW-like galaxies from the Latte suite of FIRE-2 simulations (Wetzel et al., 2016) and nine synthetic Gaia DR2 surveys generated from the simulated galaxies by means of the Ananke framework (Sanderson et al., 2020). First, we processed the synthetic Gaia datasets to correlate the position of stars and the dark subhalos, which were previously identified in the simulated galaxies. In section 3, we estimate the detectability of the subhalo-associated stars using deep learning techniques. We conclude in section 4.
As our raw data, we have used three MW-like galaxies from the Latte suite of FIRE-2 simulations (Wetzel et al., 2016; Garrison-Kimmel et al., 2017; Hopkins et al., 2018) (dubbed m12f, m12i and m12m) and nine synthetic Gaia DR2 surveys (Sanderson et al., 2020). This section describes these datasets and the processing we have performed on them.
2.1 Milky Way-like Galaxies
We have used the simulation snapshots at of three MW-like galaxies, namely m12f, m12i and m12m (Wetzel et al., 2016; Garrison-Kimmel et al., 2017; Hopkins et al., 2018). In the following we briefly describe how these MW analogues were obtained. For a complete description of this and the details of the N-body simulations we refer the interested reader to (Wetzel et al., 2016) and references therein. The MW analogues were first identified in a DM-only cosmological simulation requiring that at : (i) their virial mass is in the range of 111Virial mass and virial radius follow the relation , with the average matter density of the Universe. (which agrees with recent measurements Wang et al. 2020b; Karukes et al. 2020; Shen et al. 2021) and (ii) there is no neighboring halo of similar mass within . Three halos selected in this manner were then simulated using the zoom-in technique (Oñorbe et al., 2014). Simulations were run using the Gizmo gravity plus hydrodynamics code in meshless finite-mass (MFM) mode (Hopkins, 2015) and the FIRE-2 baryonic physics model (Hopkins et al., 2018).
We have identified DM subhalos in snapshots at of the MW-like galaxies using the Amiga Halo Finder (AHF) code (Knollmann and Knebe, 2009). The AHF algorithm identifies bound DM
structures by hierarchically clustering 3D positions ofDM particles in the simulation. Following (Garrison-Kimmel et al., 2017), AHF was run only on DM particles. We have selected subhalos with more than 85 DM particles (corresponding to subhalos with masses ) since those substructures are reliably resolved in the simulation (Garrison-Kimmel et al., 2017). Approximately subhalos222AHF identifies 1298, 1001 and 1281 subhalos with for m12f, m12i and m12m, respectively. for each MW-like galaxy remain as potentially observable. Figure 1 shows the cumulative subhalo mass function normalized by the virial mass of the host halo (left panel) and the radial distribution of the subhalo population normalized by the virial radius (right panel). The virial masses are , and for m12f, m12i and m12m, respectively.333In our work we have used the virial mass definition given by , with the critical density of the Universe at . In figure 2 we show the mass of the subhalos as a function of their galactocentric distance for m12f, m12i and m12m. It should be noted that no subhalos are identified below from the center of the galaxies, as previously noted in (Garrison-Kimmel et al., 2017). Furthermore, the 97%, 91% and 94% of the subhalos below 50 kpc for m12f, m12i and m12m, respectively, have masses lower than . The most massive subhalo below 50 kpc is identified at 20 kpc with for m12f, at 43 kpc with for m12i and at 43 kpc with for m12m. The depletion of the most massive dark subhalos in the inner 50 kpc of the MW-like galaxies conditions the ability to identify stars in the stellar halo, which have been perturbed by the passage of a dark matter subhalo, using the deep-learning techniques explored in this study.
2.2 Synthetic Gaia Surveys
The nine synthetic Gaia DR2 surveys were generated by applying the Ananke framework (Sanderson et al., 2020) to the three MW-like galaxies. Per simulated galaxy, three synthetic surveys were generated by adopting three local standards of rest. Each synthetic survey contains approximately a billion mock stellar observations resembling Gaia DR2. We restrict our attention to stellar halo stars, applying a selection in true vertical distances kpc. In this way, we remove disk stars that could suffer from disturbances induced, for example, by spiral arms, the Galactic bar or giant molecular clouds. We are left with mock stars for each LSR for the subsequent analysis. This reduced dataset consists of nearly 2 billion observed stars for the three different MW-like galaxies, three LSRs for each, correlated with potentially observable DM subhalo locations. Table 1 summarizes some statistics of this dataset.
Stars are tagged as halo-associated if their true distance to the central position of a subhalo is lower than 1 kpc. It is to be noticed that these halo-associated stars might not be bound to the subhalos. Figure 3 shows the total number of stars associated to a subhalo as a function of the subhalo’s mass for each LSR and each simulated galaxy. Within each galaxy, less than of the subhalos contain associated stars, and 66%, 84% and 75% of this fraction contain less than 10 associated stars for m12f, m12i and m12m galaxies, respectively. Furthermore, approximately 40% of the halos that have associated stars contain only one star. The m12f galaxy has a larger percentage of subhalos which are associated to more than 100 stars compared to that of m12i or m12f galaxies. This is because the former galaxy has a larger fraction of subhalos below 30 kpc. We plot the projected stellar number densities for all LSRs on figure 4, along with the halo locations and halo-associated observed stars.
|stars with kpc||halo-associated stars ||with ||subhalos w/ halo-associated stars|
3 Deep Learning Search of Subhalo-associated Stars
Dark subhalos perturb the positions and velocities of nearby stars. We wish to estimate if these imprints are detectable in MW-like galaxies and in synthetic data that accounts for observational uncertainties. Let us assume, without loss of generality, that the properties
of each star particle (or observed star) are drawn from the probability distributionor
if the star particle (observed star) has or has not been affected, respectively, by a dark subhalo at a given time in the Latte (Ananke) simulation. Then, if the probabilities are known, the likelihood ratiois the optimal discriminator between the two hypotheses for a given observation according to the Neyman-Pearson lemma (Neyman and Pearson, 1992). These probabilities are not known, however, and we only have simulated examples of either halo-associated or background star particles (observed stars). In the following, we investigate the possibility of using machine learning to define an approximate discriminator between the two hypotheses, and thus quantify the difference between the halo-associated stars and the background.
As a starting point, we first focus on the Latte simulations, where for each star particle, the full six-dimensional phase-space coordinates, namely the three-dimensional Galactocentric Cartesian positions and velocities, are known. Unlike for the synthetic Gaia dataset, the disk is not excluded at this stage. For each star particle, we compute the Euclidean distance to the nearest dark subhalo , and if it is below a threshold kpc, we identify the star particle as a halo-associated or signal particle. We then use an anomaly detection approach to estimate the strength of the subhalo signal (Baldi and Hornik, 1989; Sakurada and Yairi, 2014). For this purpose, the background-only likelihood
Each star particle is characterized by the feature vectorcontaining its three-dimensional position and velocity, i.e. . Let us define an encoder and a decoder as
respectively, such that approximates for any given input via a lower-dimensional representation. Both the encoder and decoder are implemented as feedforward neural networks, optimized by tuning the weights using only the background examples as follows:
By construction, the encoder-decoder will tend to reconstruct well the background-like samples that it was optimized on. On the other hand, for any other that is not distributed as , we would expect on average higher values for the reconstruction loss . Therefore, we can use the distribution , optimized only on the background particles, as an empirical discriminator between the background and signal samples. We have checked this approach by defining a fake signal consisting of a random sub-population of stars irrespective of the dark subhalo locations. In this case, no detectable difference between the main sample and the random subpopulation of stars is expected with this method.
We optimize the model on the m12m galaxy, while cross-checking the performance on the m12i
galaxy. This ensures that the model is not simply memorizing the locations of the halos, as the result in this case would not be generalizable to other galaxy simulations. Training is carried out for 100 iterations (epochs) over the full dataset using the Adam optimizer(Kingma and Ba, 2014) with a learning rate of and a minibatch size of star particles. We show the evolution of the total reconstruction loss over training epochs on figure 5 for both the real subhalo signal (left panel) and the fake signal cases (right panel).
We observe that the model converges for m12m and exhibits in general stable behaviour for m12i.
Figure 6 shows the distributions for the signal and background stars for the m12f dataset never used for training. It is observed that for the real subhalo signal case (left panel), there is a distinction between the distribution of halo-associated (signal) and non-halo associated (background) stars, with the signal stars having on average higher values of the reconstruced distribution. No such distinction is observed for the model trained and tested on the random subset (right panel), as would be expected.
We quantify the performance of the anomaly detection in terms of the true positive and false negative rates. The true positive rate (TPR) gives the fraction of signal stars that are correctly identified as signal particles at a particular threshold (i.e. a given value of ),
Contrary, the false positive rate (FPR) is the fraction of background stars that are incorrectly identified as signal, namely
Figure 7 shows the FPR versus TPR while scanning over for the real (solid blue) and fake (dashed black) signal cases.
For the real or subhalo case, we see that at a , the FPR is (i.e. 80% of the signal stars are correctly identified while we misclassify 15% of the background stars as signal), presenting a significant improvement over a random selection.
Based on this statistical anomaly detection model optimized on the m12m simulated galaxy, we can conclude that halo-associated star particles in the m12f dataset have a distinguishable distribution in 6-dimensional phase-space consisting of positions and momenta and that the anomaly detection method is able to correctly identify halo-associated stars.
3.2 Feasibility in Synthetic Gaia Survey
In this section, we investigate if the subhalo-associated stars are detectable in the synthetic Gaia survey derived from this simulation, that is, under the effects of extinction, partial measurement of the radial velocity and measurement errors. This is done by searching for dark subhalos on the reduced synthetic surveys described in section 2.2. The goal is again to select candidate stars which are likely to be perturbed by a nearby dark subhalo, such that they could potentially be further analyzed with more detailed approaches. The search for dark subhalos in the reduced Gaia-like catalogs differs from the previous section on two fronts. On the one hand, the mock observed stars in the synthetic Gaia datasets are divided into patches using the hierarchical pixelization algorithm HEALPY (Zonca et al., 2019; Górski et al., 2005) with a pixel level 6. This allows to process the data in manageable subsets in a physically meaningful fashion. In addition, as the subhalo-associated stars are located in well-defined, localized regions in the sky, we avoid using the absolute right ascension and declination coordinates to unfairly bias the model. Instead, we compute the positional information with respect to the pixel center. For a Gaia DR2-like dataset, a pixel can contain up to stars.
On the other hand, the input feature of each observed star is different. For each synthetic dataset realization , , we then have a list of (star observation, label) pairs
Each stellar feature vector consists of the following astrometric observables:
the right ascension with respect to the pixel center [deg],
the declination with respect to the pixel center [deg],
the proper motion in the right ascension direction (multiplied by ) [mas/year],
the proper motion in the declination [mas/year] and
the radial velocity [km/s].
These observables are available with estimated uncertainties, resulting in 12 input features. Features which are not always measured, such as the radial velocities, are filled with placeholder values for a consistent numerical treatment.
For the synthetic Gaia dataset, we cross-check the anomaly detection approach described in the previous section against a simple binary classifier, where the signal model is used explicitly, but which is thus limited by the available statistics for the halo-associated stars. Contrary, in the anomaly-detection based approach, the star labels based on the proximity to a dark subhalo were only used to exclude the signal samples from the optimization. The supervised classification model uses the signal sample labels directly, i.e. the optimization target is the star label for background and signal stars, respectively. Thus, it can be used to determine the upper limit detectability for this particular signal model, assuming training statistics are not a limiting factor.
The binary star classification model is defined as a parametric function using a deep neural network
which can be optimized by tuning the weights
to minimize a classification loss function. We use the the focal loss, which is a modification of the binary cross entropy loss, originally proposed for rare object detection in(Lin et al., 2017), and is defined as the following sum over the total number of pixels in the dataset:
where and are empirical factors that adjust the weight of easy-to-classify background-like examples in the loss. We choose based on the defaults introduced in (Lin et al., 2017). By this construction, the model output for star
is a continuous value between 0 and 1 that can be interpreted as a test statistic for the star being labeled as signal. We use dropout regularization(Srivastava et al., 2014) with a probability of in the training phase to limit the amount of overtraining. The anomaly detection model was trained for 200 epochs, while the classification model for 50 epochs. As before, we use m12m for the optimization, m12i for testing, while m12f is used for the final physics validation. The training and testing is done on stars from all three LSRs simultaneously. Overall, as summarized in Table 1, the optimization, testing and final validation is carried out on nearly 1.5 billion mock stars, of which less than 0.01% are identified as signal, resulting in extreme class imbalance as well as an overall low number of independent signal samples.
The sensitivity of the anomaly detection and classifier methods for identifying halo-associated stars in the synthetic Gaia dataset can be seen on figure 8. As before, the m12f dataset was never used in the optimization. We observe that the binary classification distinguishes between the halo-associated and background stars at a non-negligible level, with a FPR of at a TPR of . On the other hand, the anomaly detection approach, where we only attempt to learn the background distribution, does not differ significantly from a purely random selection in the synthetic survey.
Based on this, we conclude that the detectability of the halo-associated stars from the astrometric properties in the synthetic Gaia-DR2 surveys is not straightforward, even if the dark matter subhalos have a detectable kinematic effect on the underlying simulated star particles, as demonstrated in the previous subsection.
ML techniques, either alone or combined with classical methods, have been demonstrated to be helpful in uncovering new structures in Gaia-scale datasets (e.g. Necib et al. 2020). Dark subhalos are among the most challenging substructures to search for. In this paper, we study the detectability of dark subhalos by means of ML in three MW-like galaxies and in nine synthetic Gaia DR2 surveys. Rather than attempting to pinpoint the exact subhalo locations and determine their properties, we attempt to identify candidate stars that are likely to be close to a subhalo on a statistical basis.
We have first correlated star particles in the simulated galaxies and mock stars in the synthetic catalogs with the position of dark subhalos found by the AHF. In section 3.2 we then tested against simulated galaxies the feasibility of an anomaly detection algorithm to detect the phase-space imprint in stellar halo stars of nearby subhalos. This algorithm builds a likelihood function of the background star particles and is able to correctly identify 80% of signal stars while misclassifying as signal 15% of background particles. We concluded that the distribution function of the 6-dimensional phase-space coordinates of signal and background star particles are distinguishable in the MW-like galaxies used in this work. Therefore, on a statistical basis, the position and velocity information can be combined into a statistical discriminator for the halo-associated signal.
Finally, we have tested the feasibility of the anomaly detection algorithm in Gaia DR2-like surveys in section 3.2. The unsupervised anomaly detection algorithm was cross-checked against a binary supervised classification algorithm that uses the signal model explicitly. The anomaly detection approach has no sensitivity to distinguish between signal and background stars, while the binary classification algorithm is able to select 50% of signal stars while wrongly identifying 15% of background stars as signal. Although the binary classification shows a mild sensitivity, overall both approaches are of limited effectiveness in the synthetic Gaia survey. The two main factors limiting the sensitivity of deep learning searches for stellar perturbations in phase-space induced by the passage of a DM subhalo are
the low signal-to-noise ratio. Although the synthetic catalogs contain more stars than the real Gaia-DR2(Sanderson et al., 2020), a very limited number of subhalos are contained in the region observed by the survey. Thus resulting in only a handful of halos being usable for the ML optimization. Furthermore, for practicality, we so far focused on the halo stars, neglecting the galactic disk, which is the bulk of the survey.
There is not enough precision in mock observations. On top of the small number (according to the subhalo populations found for the MW-like galaxies in this analysis) of dark matter subhalos within 40 kpc from the Galactic center (GC), those subhalos have a mass below . The velocity change experienced by a star due to the encounter with a subhalo of is roughly , where v is the star’s initial velocity (Feldmann and Spolyar, 2015). Therefore, in order to measure kinematic perturbations the velocity precision must be . This level of precision might be achieved by the 4MOST Milky Way Halo Low-Resolution survey (Helmi et al., 2019).
Another possible limiting factor for deep searches of DM subhalos is the derivation of the synthetic surveys used for training. From each star particle in the Latte simulation, a set of observed stars are derived by sampling from its six-dimensional phase-space distribution (Sanderson et al., 2020). The sampling process introduces a smearing scale which distorts and possibly diminishes the imprint of the passage of a dark subhalo. We checked for this effect by applying the supervised classification approach to two small subsets: one only containing the observed stars whose true position and velocity equal that of the parent star, and another subset with the same fraction of stars that were randomly selected from those that have different true position and velocity with respect to the parent star particle from which they have been generated. In both cases, we obtain that the supervised classification algorithm has no sensitivity to distinguish between background and signal stars and the results are qualitatively the same for both subsets showing that we are limited by the available statistics. Any further study of the impact of the smearing process is left to future investigations.
A number of subsequent improvements to the methodology are possible. In the above analysis, all the observed stars were treated independently of each other. Local correlations, density or clustering were not taken into account, which could potentially limit the sensitivity of the method used so far. As an example, novel approaches based on density-based clustering have been employed for open clusters (Castro-Ginard et al., 2018) and may be interesting to study here for dark subhalos. Clustering can also be combined with unsupervised deep learning for anomaly detection (Mikuni and Canelli, 2021). Another possible approach is the direct search for overdensities by comparing signal and sideband regions, which has been so far demonstrated for stellar streams, but could potentially be studied also for dark subhalos (Shih et al., 2021). Furthermore, in order to understand the potential sensitivity of the method, simulated datasets with a known DM distribution were used. However, the halo distribution in these is fixed, and the number of actual simulated halos in the potentially visible region is limited. Additional simulated datasets with a varying halo distribution could be helpful to establish sensitivity dependence of a potential method on halo mass and distance from the galactic center.
This work was supported by the Estonian Research Council grants PSG700, PRG803, PRG1006, PUTJD907 and MOBTT5, MOBTP187, and by the European Regional Development Fund through the CoE program grant TK133.
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