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Register a new userStochastic Order Redshift Technique (SORT): a simple, efficient and robust method to improve cosmological redshift measurements
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We present a simple, efficient and robust approach to improve cosmological redshift measurements. The method is based on the presence of a reference sample for which a precise redshift number distribution (dN/dz) can be obtained for different pencil-beam-like sub-volumes within the original survey. For each sub-volume we then impose: (i) that the redshift number distribution of the uncertain redshift measurements matches the reference dN/dz corrected by their selection functions; and (ii) the rank order in redshift of the original ensemble of uncertain measurements is preserved. The latter step is motivated by the fact that random variables drawn from Gaussian probability density functions (PDFs) of different means and arbitrarily large standard deviations satisfy stochastic ordering. We then repeat this simple algorithm for multiple arbitrary pencil-beam-like overlapping sub-volumes; in this manner, each uncertain measurement has multiple (non-independent) recovered redshifts which can be used to estimate a new redshift PDF. We refer to this method as the Stochastic Order Redshift Technique (SORT). We have used a state-of-the-art N-body simulation to test the performance of SORT under simple assumptions and found that it can improve the quality of cosmological redshifts in an efficient and robust manner. Particularly, SORT redshifts are able to recover the distinctive features of the cosmic web and can provide unbiased measurement of the two-point correlation function on scales > 4 Mpc/h. Given its simplicity, we envision that a method like SORT can be incorporated into more sophisticated algorithms aimed to exploit the full potential of large extragalactic photometric surveys.
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The purpose of this work is to investigate the prospects of using the future standard siren data without redshift measurements to constrain cosmological parameters. With successful detections of gravitational wave (GW) signals an era of GW astronomy has begun. Unlike the electromagnetic domain, GW signals allow direct measurements of luminosity distances to the sources, while their redshifts remain to be measured by identifying electromagnetic counterparts. This leads to significant technical problems for almost all possible BH-BH systems. It is the major obstacle to cosmological applications of GW standard sirens. In this paper, we introduce the general framework of using luminosity distances alone for cosmological inference. The idea is to use the prior knowledge of the redshift probability distribution for coalescing sources from the intrinsic merger rates assessed with population synthesis codes. Then the posterior probability distributions for cosmological parameters can be calculated. We demonstrate the performance of our method on the simulated mock data and show that the luminosity distance measurement would enable an accurate determination of cosmological parameters up to $20%$ uncertainty level. We also find that in order to infer $H_0$ to 1% level with flat $Lambda$CDM model, we need about $10^5$ events.
Dense retrieval has been shown to be effective for retrieving relevant documents for Open Domain QA, surpassing popular sparse retrieval methods like BM25. REALM (Guu et al., 2020) is an end-to-end dense retrieval system that relies on MLM based pretraining for improved downstream QA efficiency across multiple datasets. We study the finetuning of REALM on various QA tasks and explore the limits of various hyperparameter and supervision choices. We find that REALM was significantly undertrained when finetuning and simple improvements in the training, supervision, and inference setups can significantly benefit QA results and exceed the performance of other models published post it. Our best model, REALM++, incorporates all the best working findings and achieves significant QA accuracy improvements over baselines (~5.5% absolute accuracy) without any model design changes. Additionally, REALM++ matches the performance of large Open Domain QA models which have 3x more parameters demonstrating the efficiency of the setup.
We demonstrate the ability of convolutional neural networks (CNNs) to mitigate systematics in the virial scaling relation and produce dynamical mass estimates of galaxy clusters with remarkably low bias and scatter. We present two models, CNN$_mathrm{1D}$ and CNN$_mathrm{2D}$, which leverage this deep learning tool to infer cluster masses from distributions of member galaxy dynamics. Our first model, CNN$_text{1D}$, infers cluster mass directly from the distribution of member galaxy line-of-sight velocities. Our second model, CNN$_text{2D}$, extends the input space of CNN$_text{1D}$ to learn on the joint distribution of galaxy line-of-sight velocities and projected radial distances. We train each model as a regression over cluster mass using a labeled catalog of realistic mock cluster observations generated from the MultiDark simulation and UniverseMachine catalog. We then evaluate the performance of each model on an independent set of mock observations selected from the same simulated catalog. The CNN models produce cluster mass predictions with lognormal residuals of scatter as low as $0.132$ dex, greater than a factor of 2 improvement over the classical $M$-$sigma$ power-law estimator. Furthermore, the CNN model reduces prediction scatter relative to similar machine learning approaches by up to $17%$ while executing in drastically shorter training and evaluation times (by a factor of 30) and producing considerably more robust mass predictions (improving prediction stability under variations in galaxy sampling rate by $30%$).
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