skip to main content


Search for: All records

Award ID contains: 2019786

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. ABSTRACT

    Examining the properties of subhaloes with strong gravitational lensing images can shed light on the nature of dark matter. From upcoming large-scale surveys, we expect to discover orders of magnitude more strong lens systems that can be used for subhalo studies. To optimally extract information from a large number of strong lensing images, machine learning provides promising avenues for efficient analysis that is unachievable with traditional analysis methods, but application of machine learning techniques to real observations is still limited. We build upon previous work, which uses a neural likelihood-ratio estimator, to constrain the effective density slopes of subhaloes and demonstrate the feasibility of this method on real strong lensing observations. To do this, we implement significant improvements to the forward simulation pipeline and undertake careful model evaluation using simulated images. Ultimately, we use our trained model to predict the effective subhalo density slope from combining a set of strong lensing images taken by the Hubble Space Telescope. We found the subhalo slope measurement of this set of observations to be steeper than the slope predictions of cold dark matter subhaloes. Our result adds to several previous works that also measured high subhalo slopes in observations. Although a possible explanation for this is that subhaloes with steeper slopes are easier to detect due to selection effects and thus contribute to statistical bias, our result nevertheless points to the need for careful analysis of more strong lensing observations from future surveys.

     
    more » « less
  2. A<sc>bstract</sc>

    In this paper, we present a method of embedding physics data manifolds with metric structure into lower dimensional spaces with simpler metrics, such as Euclidean and Hyperbolic spaces. We then demonstrate that it can be a powerful step in the data analysis pipeline for many applications. Using progressively more realistic simulated collisions at the Large Hadron Collider, we show that this embedding approach learns the underlying latent structure. With the notion of volume in Euclidean spaces, we provide for the first time a viable solution to quantifying the true search capability of model agnostic search algorithms in collider physics (i.e. anomaly detection). Finally, we discuss how the ideas presented in this paper can be employed to solve many practical challenges that require the extraction of physically meaningful representations from information in complex high dimensional datasets.

     
    more » « less
    Free, publicly-accessible full text available July 1, 2024
  3. Abstract

    Conservation laws are key theoretical and practical tools for understanding, characterizing, and modeling nonlinear dynamical systems. However, for many complex systems, the corresponding conserved quantities are difficult to identify, making it hard to analyze their dynamics and build stable predictive models. Current approaches for discovering conservation laws often depend on detailed dynamical information or rely on black box parametric deep learning methods. We instead reformulate this task as a manifold learning problem and propose a non-parametric approach for discovering conserved quantities. We test this new approach on a variety of physical systems and demonstrate that our method is able to both identify the number of conserved quantities and extract their values. Using tools from optimal transport theory and manifold learning, our proposed method provides a direct geometric approach to identifying conservation laws that is both robust and interpretable without requiring an explicit model of the system nor accurate time information.

     
    more » « less
  4. Abstract

    The Lipschitz constant of the map between the input and output space represented by a neural network is a natural metric for assessing the robustness of the model. We present a new method to constrain the Lipschitz constant of dense deep learning models that can also be generalized to other architectures. The method relies on a simple weight normalization scheme during training that ensures the Lipschitz constant of every layer is below an upper limit specified by the analyst. A simple monotonic residual connection can then be used to make the model monotonic in any subset of its inputs, which is useful in scenarios where domain knowledge dictates such dependence. Examples can be found in algorithmic fairness requirements or, as presented here, in the classification of the decays of subatomic particles produced at the CERN Large Hadron Collider. Our normalization is minimally constraining and allows the underlying architecture to maintain higher expressiveness compared to other techniques which aim to either control the Lipschitz constant of the model or ensure its monotonicity. We show how the algorithm was used to train a powerful, robust, and interpretable discriminator for heavy-flavor-quark decays, which has been adopted for use as the primary data-selection algorithm in the LHCb real-time data-processing system in the current LHC data-taking period known as Run 3. In addition, our algorithm has also achieved state-of-the-art performance on benchmarks in medicine, finance, and other applications.

     
    more » « less
  5. Abstract

    Cosmological surveys must correct their observations for the reddening of extragalactic objects by Galactic dust. Existing dust maps, however, have been found to have spatial correlations with the large-scale structure of the Universe. Errors in extinction maps can propagate systematic biases into samples of dereddened extragalactic objects and into cosmological measurements such as correlation functions between foreground lenses and background objects and the primordial non-Gaussianity parameterfNL. Emission-based maps are contaminated by the cosmic infrared background, while maps inferred from stellar reddenings suffer from imperfect removal of quasars and galaxies from stellar catalogs. Thus, stellar-reddening-based maps using catalogs without extragalactic objects offer a promising path to making dust maps with minimal correlations with large-scale structure. We present two high-latitude integrated extinction maps based on stellar reddenings, with a point-spread functions of FWHMs 6.′1 and 15′. We employ a strict selection of catalog objects to filter out galaxies and quasars and measure the spatial correlation of our extinction maps with extragalactic structure. Our galactic extinction maps have reduced spatial correlation with large-scale structure relative to most existing stellar-reddening-based and emission-based extinction maps.

     
    more » « less
  6. Abstract

    We present the discovery of the Type II supernova SN 2023ixf in M101 and follow-up photometric and spectroscopic observations, respectively, in the first month and week of its evolution. Our discovery was made within a day of estimated first light, and the following light curve is characterized by a rapid rise (≈5 days) to a luminous peak (MV≈ − 18.2 mag) and plateau (MV≈ − 17.6 mag) extending to 30 days with a fast decline rate of ≈0.03 mag day−1. During the rising phase,UVcolor shows blueward evolution, followed by redward evolution in the plateau phase. Prominent flash features of hydrogen, helium, carbon, and nitrogen dominate the spectra up to ≈5 days after first light, with a transition to a higher ionization state in the first ≈2 days. Both theUVcolor and flash ionization states suggest a rise in the temperature, indicative of a delayed shock breakout inside dense circumstellar material (CSM). From the timescales of CSM interaction, we estimate its compact radial extent of ∼(3–7) × 1014cm. We then construct numerical light-curve models based on both continuous and eruptive mass-loss scenarios shortly before explosion. For the continuous mass-loss scenario, we infer a range of mass-loss history with 0.1–1.0Myr−1in the final 2−1 yr before explosion, with a potentially decreasing mass loss of 0.01–0.1Myr−1in ∼0.7–0.4 yr toward the explosion. For the eruptive mass-loss scenario, we favor eruptions releasing 0.3–1Mof the envelope at about a year before explosion, which result in CSM with mass and extent similar to the continuous scenario. We discuss the implications of the available multiwavelength constraints obtained thus far on the progenitor candidate and SN 2023ixf to our variable CSM models.

     
    more » « less
    Free, publicly-accessible full text available September 1, 2024
  7. Abstract

    We study infinite limits of neural network quantum states (-NNQS), which exhibit representation power through ensemble statistics, and also tractable gradient descent dynamics. Ensemble averages of entanglement entropies are expressed in terms of neural network correlators, and architectures that exhibit volume-law entanglement are presented. The analytic calculations of entanglement entropy bound are tractable because the ensemble statistics are simplified in the Gaussian process limit. A general framework is developed for studying the gradient descent dynamics of neural network quantum states (NNQS), using a quantum state neural tangent kernel (QS-NTK). For-NNQS the training dynamics is simplified, since the QS-NTK becomes deterministic and constant. An analytic solution is derived for quantum state supervised learning, which allows an-NNQS to recover any target wavefunction. Numerical experiments on finite and infinite NNQS in the transverse field Ising model and Fermi Hubbard model demonstrate excellent agreement with theory.-NNQS opens up new opportunities for studying entanglement and training dynamics in other physics applications, such as in finding ground states.

     
    more » « less
    Free, publicly-accessible full text available June 1, 2024
  8. Abstract

    We present an expansion of FLEET, a machine-learning algorithm optimized to select transients that are most likely tidal disruption events (TDEs). FLEET is based on a random forest algorithm trained on both the light curves and host galaxy information of 4779 spectroscopically classified transients. We find that for transients with a probability of being a TDE,P(TDE) > 0.5, we can successfully recover TDEs with ≈40% completeness and ≈30% purity when using their first 20 days of photometry or a similar completeness and ≈50% purity when including 40 days of photometry, an improvement of almost 2 orders of magnitude compared to random selection. Alternatively, we can recover TDEs with a maximum purity of ≈80% and a completeness of ≈30% when considering only transients withP(TDE) > 0.8. We explore the use of FLEET for future time-domain surveys such as the Legacy Survey of Space and Time on the Vera C. Rubin Observatory (Rubin) and the Nancy Grace Roman Space Telescope (Roman). We estimate that ∼104well-observed TDEs could be discovered every year by Rubin and ∼200 TDEs by Roman. Finally, we run FLEET on the TDEs from our Rubin survey simulation and find that we can recover ∼30% of them at redshiftz< 0.5 withP(TDE) > 0.5, or ∼3000 TDEs yr–1that FLEET could uncover from the Rubin stream. We have demonstrated that we will be able to run FLEET on Rubin photometry as soon as this survey begins. FLEET is provided as an open source package on GitHub: https://github.com/gmzsebastian/FLEET.

     
    more » « less
  9. ABSTRACT

    We present a method to reconstruct the initial linear-regime matter density field from the late-time non-linearly evolved density field in which we channel the output of standard first-order reconstruction to a convolutional neural network (CNN). Our method shows dramatic improvement over the reconstruction of either component alone. We show why CNNs are not well-suited for reconstructing the initial density directly from the late-time density: CNNs are local models, but the relationship between initial and late-time density is not local. Our method leverages standard reconstruction as a preprocessing step, which inverts bulk gravitational flows sourced over very large scales, transforming the residual reconstruction problem from long-range to local and making it ideally suited for a CNN. We develop additional techniques to account for redshift distortions, which warp the density fields measured by galaxy surveys. Our method improves the range of scales of high-fidelity reconstruction by a factor of 2 in wavenumber above standard reconstruction, corresponding to a factor of 8 increase in the number of well-reconstructed modes. In addition, our method almost completely eliminates the anisotropy caused by redshift distortions. As galaxy surveys continue to map the Universe in increasingly greater detail, our results demonstrate the opportunity offered by CNNs to untangle the non-linear clustering at intermediate scales more accurately than ever before.

     
    more » « less
  10. ABSTRACT

    Machine learning can play a powerful role in inferring missing line-of-sight velocities from astrometry in surveys such as Gaia. In this paper, we apply a neural network to Gaia Early Data Release 3 (EDR3) and obtain line-of-sight velocities and associated uncertainties for ∼92 million stars. The network, which takes as input a star’s parallax, angular coordinates, and proper motions, is trained and validated on ∼6.4 million stars in Gaia with complete phase-space information. The network’s uncertainty on its velocity prediction is a key aspect of its design; by properly convolving these uncertainties with the inferred velocities, we obtain accurate stellar kinematic distributions. As a first science application, we use the new network-completed catalogue to identify candidate stars that belong to the Milky Way’s most recent major merger, Gaia-Sausage-Enceladus (GSE). We present the kinematic, energy, angular momentum, and spatial distributions of the ∼450 000 GSE candidates in this sample, and also study the chemical abundances of those with cross matches to GALAH and APOGEE. The network’s predictive power will only continue to improve with future Gaia data releases as the training set of stars with complete phase-space information grows. This work provides a first demonstration of how to use machine learning to exploit high-dimensional correlations on data to infer line-of-sight velocities, and offers a template for how to train, validate, and apply such a neural network when complete observational data is not available.

     
    more » « less