Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userHow does the grouping scheme affect the Wiener Filter reconstruction of the local Universe?
46
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
High quality reconstructions of the three dimensional velocity and density fields of the local Universe are essential to study the local Large Scale Structure. In this paper, the Wiener Filter reconstruction technique is applied to galaxy radial peculiar velocity catalogs to understand how the Hubble constant (H0) value and the grouping scheme affect the reconstructions. While H0 is used to derive radial peculiar velocities from galaxy distance measurements and total velocities, the grouping scheme serves the purpose of removing non linear motions. Two different grouping schemes (based on the literature and a systematic algorithm) as well as five H0 values ranging from 72 to 76 km/s/Mpc are selected. The Wiener Filter is applied to the resulting catalogs. Whatever grouping scheme is used, the larger H0 is, the larger the infall onto the local Volume is. However, this conclusion has to be strongly mitigated: a bias minimization scheme applied to the catalogs after grouping suppresses this effect. At fixed H0, reconstructions obtained with catalogs grouped with the different schemes exhibit structures at the proper location in both cases but the latter are more contrasted in the less aggressive scheme case: having more constraints permits an infall from both sides onto the structures to reinforce their overdensity. Such findings highlight the importance of a balance between grouping to suppress non linear motions and preserving constraints to produce an infall onto structures expected to be large overdensities. Such an observation is promising to perform constrained simulations of the local Universe including its massive clusters.
rate research
Read More
We present a high performance solution to the Wiener filtering problem via a formulation that is dual to the recently developed messenger technique. This new dual messenger algorithm, like its predecessor, efficiently calculates the Wiener filter solution of large and complex data sets without preconditioning and can account for inhomogeneous noise distributions and arbitrary mask geometries. We demonstrate the capabilities of this scheme in signal reconstruction by applying it on a simulated cosmic microwave background (CMB) temperature data set. The performance of this new method is compared to that of the standard messenger algorithm and the preconditioned conjugate gradient (PCG) approach, using a series of well-known convergence diagnostics and their processing times, for the particular problem under consideration. This variant of the messenger algorithm matches the performance of the PCG method in terms of the effectiveness of reconstruction of the input angular power spectrum and converges smoothly to the final solution. The dual messenger algorithm outperforms the standard messenger and PCG methods in terms of execution time, as it runs to completion around 2 and 3-4 times faster than the respective methods, for the specific problem considered.
Model-Agnostic Meta-Learning (MAML) has become increasingly popular for training models that can quickly adapt to new tasks via one or few stochastic gradient descent steps. However, the MAML objective is significantly more difficult to optimize compared to standard Empirical Risk Minimization (ERM), and little is understood about how much MAML improves over ERM in terms of the fast adaptability of their solutions in various scenarios. We analytically address this issue in a linear regression setting consisting of a mixture of easy and hard tasks, where hardness is related to the condition number of the tasks loss function. Specifically, we prove that in order for MAML to achieve substantial gain over ERM, (i) there must be some discrepancy in hardness among the tasks, and (ii) the optimal solutions of the hard tasks must be closely packed with the center far from the center of the easy tasks optimal solutions. We also give numerical and analytical results suggesting that these insights also apply to two-layer neural networks. Finally, we provide few-shot image classification experiments that support our insights for when MAML should be used and emphasize the importance of training MAML on hard tasks in practice.
Comparison of the ISM properties of a wide range of metal-poor galaxies with normal metal-rich galaxies reveals striking differences. We find that the combination of the low dust abundance and the active star formation results in a very porous ISM filled with hard photons, heating the dust in dwarf galaxies to overall higher temperatures than their metal-rich counterparts. This results in photodissociation of molecular clouds to greater depths, leaving relatively large PDR envelopes and difficult-to-detect CO cores. From detailed modeling of the low-metallicity ISM, we find significant fractions of CO-dark H2 - a reservoir of molecular gas not traced by CO, but present in the [CII] and [CI]-emitting envelopes. Self-consistent analyses of the neutral and ionized gas diagnostics along with the dust SED is the necessary way forward in uncovering the multiphase structure of galaxies
There is a longstanding discrepancy between the observed Galactic classical nova rate of $sim 10$ yr$^{-1}$ and the predicted rate from Galactic models of $sim 30$--50 yr$^{-1}$. One explanation for this discrepancy is that many novae are hidden by interstellar extinction, but the degree to which dust can obscure novae is poorly constrained. We use newly available all-sky three-dimensional dust maps to compare the brightness and spatial distribution of known novae to that predicted from relatively simple models in which novae trace Galactic stellar mass. We find that only half ($sim 48$%) of novae are expected to be easily detectable ($g lesssim 15$) with current all-sky optical surveys such as the All-Sky Automated Survey for Supernovae (ASAS-SN). This fraction is much lower than previously estimated, showing that dust does substantially affect nova detection in the optical. By comparing complementary survey results from ASAS-SN, OGLE-IV, and the Palomar Gattini IR-survey in the context of our modeling, we find a tentative Galactic nova rate of $sim 40$ yr$^{-1}$, though this could decrease to as low as $sim 30$ yr$^{-1}$ depending on the assumed distribution of novae within the Galaxy. These preliminary estimates will be improved in future work through more sophisticated modeling of nova detection in ASAS-SN and other surveys.
Unsupervised domain adaptation (UDA) aims to train a target classifier with labeled samples from the source domain and unlabeled samples from the target domain. Classical UDA learning bounds show that target risk is upper bounded by three terms: source risk, distribution discrepancy, and combined risk. Based on the assumption that the combined risk is a small fixed value, methods based on this bound train a target classifier by only minimizing estimators of the source risk and the distribution discrepancy. However, the combined risk may increase when minimizing both estimators, which makes the target risk uncontrollable. Hence the target classifier cannot achieve ideal performance if we fail to control the combined risk. To control the combined risk, the key challenge takes root in the unavailability of the labeled samples in the target domain. To address this key challenge, we propose a method named E-MixNet. E-MixNet employs enhanced mixup, a generic vicinal distribution, on the labeled source samples and pseudo-labeled target samples to calculate a proxy of the combined risk. Experiments show that the proxy can effectively curb the increase of the combined risk when minimizing the source risk and distribution discrepancy. Furthermore, we show that if the proxy of the combined risk is added into loss functions of four representative UDA methods, their performance is also improved.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
