Pileup Mitigation with Machine Learning (PUMML)

Pileup involves the contamination of the energy distribution arising from the primary collision of interest (leading vertex) by radiation from soft collisions (pileup). We develop a new technique for removing this contamination using machine learning and convolutional neural networks. The network takes as input the energy distribution of charged leading vertex particles, charged pileup particles, and all neutral particles and outputs the energy distribution of particles coming from leading vertex alone. The PUMML algorithm performs remarkably well at eliminating pileup distortion on a wide range of simple and complex jet observables. We test the robustness of the algorithm in a number of ways and discuss how the network can be trained directly on data.Comment: 20 pages, 8 figures, 2 tables. Updated to JHEP versio

Learning to Classify from Impure Samples with High-Dimensional Data

A persistent challenge in practical classification tasks is that labeled training sets are not always available. In particle physics, this challenge is surmounted by the use of simulations. These simulations accurately reproduce most features of data, but cannot be trusted to capture all of the complex correlations exploitable by modern machine learning methods. Recent work in weakly supervised learning has shown that simple, low-dimensional classifiers can be trained using only the impure mixtures present in data. Here, we demonstrate that complex, high-dimensional classifiers can also be trained on impure mixtures using weak supervision techniques, with performance comparable to what could be achieved with pure samples. Using weak supervision will therefore allow us to avoid relying exclusively on simulations for high-dimensional classification. This work opens the door to a new regime whereby complex models are trained directly on data, providing direct access to probe the underlying physics.Comment: 6 pages, 2 tables, 2 figures. v2: updated to match PRD versio

Deep learning in color: towards automated quark/gluon jet discrimination

Artificial intelligence offers the potential to automate challenging data-processing tasks in collider physics. To establish its prospects, we explore to what extent deep learning with convolutional neural networks can discriminate quark and gluon jets better than observables designed by physicists. Our approach builds upon the paradigm that a jet can be treated as an image, with intensity given by the local calorimeter deposits. We supplement this construction by adding color to the images, with red, green and blue intensities given by the transverse momentum in charged particles, transverse momentum in neutral particles, and pixel-level charged particle counts. Overall, the deep networks match or outperform traditional jet variables. We also find that, while various simulations produce different quark and gluon jets, the neural networks are surprisingly insensitive to these differences, similar to traditional observables. This suggests that the networks can extract robust physical information from imperfect simulations.Massachusetts Institute of Technology. Department of Physic

Panel #2: The Maine-Missouri Crisis and the Politics of Slavery

A panel that included three presentations: African Americans and the Political Consequences of Maine Statehood, Mary T. Freeman Doughface Pioneer: John Holmes of Maine, 1773-1843, Matthew Mason Fire Bell in the Night: The Establishment of a Slave Society in Jefferson\u27s Purchase, Diane Mutti Burk

Normalization of prostate specific antigen in patients treated with intensity modulated radiotherapy for clinically localized prostate cancer

<p>Abstract</p> <p>Background</p> <p>The purpose of this study was to determine the expected time to prostate specific antigen (PSA) normalization with or without neoadjuvant androgen deprivation (NAAD) therapy after treatment with intensity modulated radiotherapy (IMRT) for patients with clinically localized prostate cancer.</p> <p>Methods</p> <p>A retrospective cohort research design was used. A total of 133 patients with clinical stage T1c to T3b prostate cancer (2002 AJCC staging) treated in a community setting between January 2002 and July 2005 were reviewed for time to PSA normalization using 1 ng/mL and 2 ng/mL as criteria. All patients received IMRT as part of their management. Times to PSA normalization were calculated using the Kaplan-Meier method. Significance was assessed at p < 0.05.</p> <p>Results</p> <p>Fifty-six of the 133 patients received NAAD (42.1%). Thirty-one patients (23.8%) received radiation to a limited pelvic field followed by an IMRT boost, while 99 patients received IMRT alone (76.2%). The times to serum PSA normalization < 2 ng/mL when treated with or without NAAD were 298 ± 24 and 302 ± 33 days (mean ± SEM), respectively (p > 0.05), and 303 ± 24 and 405 ± 46 days, respectively, for PSA < 1 ng/mL (p < 0.05). Stage T1 and T2 tumors had significantly increased time to PSA normalization < 1 ng/mL in comparison to Stage T3 tumors. Also, higher Gleason scores were significantly correlated with a faster time to PSA normalization < 1 ng/mL.</p> <p>Conclusions</p> <p>Use of NAAD in conjunction with IMRT leads to a significantly shortened time to normalization of serum PSA < 1 ng/mL in patients with clinically localized prostate cancer.</p

Rapid Circumstellar Disk Evolution and an Accelerating Star Formation Rate in the Infrared Dark Cloud M17 SWex

We present a catalog of 840 X-ray sources and first results from a 100 ks Chandra X-ray Observatory imaging study of the filamentary infrared dark cloud G014.225$-$00.506, which forms the central regions of a larger cloud complex known as the M17 southwest extension (M17 SWex). In addition to the rich population of protostars and young stellar objects with dusty circumstellar disks revealed by Spitzer Space Telescope archival data, we discover a population of X-ray-emitting, intermediate-mass pre--main-sequence stars (IMPS) that lack infrared excess emission from circumstellar disks. We model the infrared spectral energy distributions of this source population to measure its mass function and place new constraints on the inner dust disk destruction timescales for 2-8 $M_{\odot}$ stars. We also place a lower limit on the star formation rate (SFR) and find that it is quite high ($\dot{M}\ge 0.007~M_{\odot}$ yr$^{-1}$), equivalent to several Orion Nebula Clusters in G14.225$-$0.506 alone, and likely accelerating. The cloud complex has not produced a population of massive, O-type stars commensurate with its SFR. This absence of very massive (${\ge}20~M_{\odot}$) stars suggests that either (1) M17 SWex is an example of a distributed mode of star formation that will produce a large OB association dominated by intermediate-mass stars but relatively few massive clusters, or (2) the massive cores are still in the process of accreting sufficient mass to form massive clusters hosting O stars.Comment: 29 pages, 9 figures, accepted to Ap

Durable Near-Complete Response to Anti-PD-1 Checkpoint Immunotherapy in a Refractory Malignant Solitary Fibrous Tumor of the Pleura

Solitary fibrous tumor of the pleura is a rare and usually benign primary neoplasm arising from mesenchymal cells of the submesothelial tissue. We present here the case of a patient diagnosed with CD34-positive advanced malignant solitary fibrous tumor of the pleura whose disease failed to respond to combination cytotoxic chemotherapy agents, but demonstrated a prompt near-complete response to checkpoint blockade treatment using the anti-programmed death (PD)-1 monoclonal antibody pembrolizumab, based on tumor molecular profiling revealing tumoral expression positivity for both programmed death-ligand 1 (PD-L1) and PD-1. The patient experienced minimal adverse effects from the treatment with durable favorable response lasting up to cycle 26