Online classification for time-domain astronomy

The advent of synoptic sky surveys has spurred the development of techniques for real-time classification of astronomical sources in order to ensure timely follow-up with appropriate instruments. Previous work has focused on algorithm selection or improved light curve representations, and naively convert light curves into structured feature sets without regard for the time span or phase of the light curves. In this paper, we highlight the violation of a fundamental machine learning assumption that occurs when archival light curves with long observational time spans are used to train classifiers that are applied to light curves with fewer observations. We propose two solutions to deal with the mismatch in the time spans of training and test light curves. The first is the use of classifier committees where each classifier is trained on light curves of different observational time spans. Only the committee member whose training set matches the test light curve time span is invoked for classification. The second solution uses hierarchical classifiers that are able to predict source types both individually and by sub-group, so that the user can trade-off an earlier, more robust classification with classification granularity. We test both methods using light curves from the MACHO survey, and demonstrate their usefulness in improving performance over similar methods that naively train on all available archival data.Comment: Astroinformatics workshop, IEEE International Conference on Data Mining 201

Collaborative Supervised Learning for Sensor Networks

Collaboration methods for distributed machine-learning algorithms involve the specification of communication protocols for the learners, which can query other learners and/or broadcast their findings preemptively. Each learner incorporates information from its neighbors into its own training set, and they are thereby able to bootstrap each other to higher performance. Each learner resides at a different node in the sensor network and makes observations (collects data) independently of the other learners. After being seeded with an initial labeled training set, each learner proceeds to learn in an iterative fashion. New data is collected and classified. The learner can then either broadcast its most confident classifications for use by other learners, or can query neighbors for their classifications of its least confident items. As such, collaborative learning combines elements of both passive (broadcast) and active (query) learning. It also uses ideas from ensemble learning to combine the multiple responses to a given query into a single useful label. This approach has been evaluated against current non-collaborative alternatives, including training a single classifier and deploying it at all nodes with no further learning possible, and permitting learners to learn from their own most confident judgments, absent interaction with their neighbors. On several data sets, it has been consistently found that active collaboration is the best strategy for a distributed learner network. The main advantages include the ability for learning to take place autonomously by collaboration rather than by requiring intervention from an oracle (usually human), and also the ability to learn in a distributed environment, permitting decisions to be made in situ and to yield faster response time

Characterization of dimensional changes of cement pastes and mortars in fresh state applying an interferometric technique

The effect produced by the incorporation of additives in Portland cement based materials over dimensional changes occurring during the setting process was evaluated employing a fiber optic Fizeau interferometric sensor. The sensor system employed a broadband light source (SLED) centered at 1550 nm, whose spectral emission was modulated by the interferometer formed between the material surface and the end of the optical fiber used to illuminate the sample. An optical spectrum analyzer was used to monitor the variation of the modulated spectrum, while the mentioned process took place. The expansion or contraction experienced by materials with different compositions was observed and quantified. Results obtained point out the accuracy and the potential of the technique.Fil: Mesa Yandy, Angelica Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Centro de Investigaciones Opticas (i); Argentina. Universidad Nacional de La Plata. Facultad de Ingenieria; Argentina. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas; ArgentinaFil: Duchowicz, Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Centro de Investigaciones Opticas (i); Argentina. Universidad Austral. Facultad de Ingeniería; Argentina. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas; ArgentinaFil: Russo, Nelida Araceli. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico La Plata. Centro de Investigaciones Opticas (i); Argentina. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas; ArgentinaFil: Zerbino, Raul Luis. Universidad Nacional de La Plata. Facultad de Ingenieria; Argentina. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas; Argentin

The IPAC Image Subtraction and Discovery Pipeline for the intermediate Palomar Transient Factory

We describe the near real-time transient-source discovery engine for the intermediate Palomar Transient Factory (iPTF), currently in operations at the Infrared Processing and Analysis Center (IPAC), Caltech. We coin this system the IPAC/iPTF Discovery Engine (or IDE). We review the algorithms used for PSF-matching, image subtraction, detection, photometry, and machine-learned (ML) vetting of extracted transient candidates. We also review the performance of our ML classifier. For a limiting signal-to-noise ratio of 4 in relatively unconfused regions, "bogus" candidates from processing artifacts and imperfect image subtractions outnumber real transients by ~ 10:1. This can be considerably higher for image data with inaccurate astrometric and/or PSF-matching solutions. Despite this occasionally high contamination rate, the ML classifier is able to identify real transients with an efficiency (or completeness) of ~ 97% for a maximum tolerable false-positive rate of 1% when classifying raw candidates. All subtraction-image metrics, source features, ML probability-based real-bogus scores, contextual metadata from other surveys, and possible associations with known Solar System objects are stored in a relational database for retrieval by the various science working groups. We review our efforts in mitigating false-positives and our experience in optimizing the overall system in response to the multitude of science projects underway with iPTF.Comment: 66 pages, 21 figures, 7 tables, accepted by PAS

SN 2016hil-- a Type II supernova in the remote outskirts of an elliptical host and its origin

Type II supernovae (SNe) stem from the core collapse of massive ($>8\ M_{\odot}$) stars. Owing to their short lifespan, we expect a very low rate of such events in elliptical host galaxies, where the star-formation rate is low, and which mostly consist of an old stellar population. SN 2016hil (iPTF16hil) is a Type II supernova located in the extreme outskirts of an elliptical galaxy at redshift$z=0.0608$(projected distance$27.2$kpc). It was detected near peak brightness ($M_{r} \approx -17$mag) 9 days after the last nondetection. SN 2016hil has some potentially peculiar properties: while presenting a characteristic spectrum, the event was unusually short lived and declined by$\sim 1.5$mag in$< 40$days, following an apparently double-peaked light curve. Its spectra suggest a low metallicity ($Z<0.4\ Z_{\odot}$). We place a tentative upper limit on the mass of a potential faint host at$\log(M/M_{\odot}) =7.27^{+0.43}_{-0.24}$ using deep Keck optical imaging. In light of this, we discuss the possibility of the progenitor forming locally, and other more exotic formation scenarios such as a merger or common-envelope evolution causing a time-delayed explosion. Further observations of the explosion site in the ultraviolet are needed in order to distinguish between the cases. Regardless of the origin of the transient, observing a population of such seemingly hostless Type II SNe could have many uses, including an estimate the number of faint galaxies in a given volume, and tests of the prediction of a time-delayed population of core-collapse SNe in locations otherwise unfavorable for the detection of such events.Comment: Comments are welcom

Small Near-Earth Asteroids in the Palomar Transient Factory Survey: a Real-Time Streak-detection System

Near-Earth asteroids (NEAs) in the 1–100 meter size range are estimated to be ~1,000 times more numerous than the ~15,000 currently cataloged NEAs, most of which are in the 0.5–10 kilometer size range. Impacts from 10–100 meter size NEAs are not statistically life-threatening, but may cause significant regional damage, while 1–10 meter size NEAs with low velocities relative to Earth are compelling targets for space missions. We describe the implementation and initial results of a real-time NEA-discovery system specialized for the detection of small, high angular rate (visually streaked) NEAs in Palomar Transient Factory (PTF) images. PTF is a 1.2-m aperture, 7.3 deg^2 field of view (FOV) optical survey designed primarily for the discovery of extragalactic transients (e.g., supernovae) in 60-second exposures reaching ~20.5 visual magnitude. Our real-time NEA discovery pipeline uses a machine-learned classifier to filter a large number of false-positive streak detections, permitting a human scanner to efficiently and remotely identify real asteroid streaks during the night. Upon recognition of a streaked NEA detection (typically within an hour of the discovery exposure), the scanner triggers follow-up with the same telescope and posts the observations to the Minor Planet Center for worldwide confirmation. We describe our 11 initial confirmed discoveries, all small NEAs that passed 0.3–15 lunar distances from Earth. Lastly, we derive useful scaling laws for comparing streaked-NEA-detection capabilities of different surveys as a function of their hardware and survey-pattern characteristics. This work most directly informs estimates of the streak-detection capabilities of the Zwicky Transient Facility (ZTF, planned to succeed PTF in 2017), which will apply PTF's current resolution and sensitivity over a 47-deg^2 FOV

EVALUATION AND OPTIMIZATION OF ANTIOXIDANT POTENTIALITY OF XYLARIA FEEJEENSIS HMJAU22039

ABSTRACTObjective: Antioxidants neutralize free radicals generated in the human body and prevent them from causing damage. The present study is aimed toevaluate and optimize the antioxidant activity of Xylaria feejeensis HMJAU22039 an endophyte isolated from Tectona grandis.Methods: The antioxidant potential was measured by 1-1diphenyl-2-picryl-hydrazil (DPPH) radical scavenging assay and nitric oxide (NO) scavengingassay. Total phenolic content (TPC) of the samples was measured by Folin–Ciocalteu reagent based assay, and the values were obtained from theregression equation: y = 0.006141x + 0.157733.Results: Potato dextrose yeast extract broth medium was selected as the basal medium as it reported a high antioxidant activity. The basal mediumwas optimized with 2% dextrose, 0.2% yeast extract, and 200 g/L potato infusion. Dextrose and yeast extract were selected as carbon and nitrogensources, respectively, as they reported high antioxidant activity. The antioxidant activity and phenolic content are highest at static condition(18.14 mg/g). Incubation temperature of 30°C and pH of the initial medium at 6 were found to be optimum conditions for high antioxidant activity. Anincubation period of 20 days reported the highest antioxidant activity and phenolic content. Methanol extract recorded high antioxidant activity witha DPPH (73.86%) followed by ethyl acetate and chloroform fractions.Conclusion: The study highlights the importance of different physiochemical parameters in the production of secondary metabolites havingantioxidant properties. The results reveal a significant positive correlation between DPPH radical scavenging assay, NO scavenging assay, and TPC.Keywords: 1-1diphenyl-2-picryl-hydrazil, Total phenolic content, Endophytic fungus, Tectona grandis

STATISTICAL OPTIMIZATION OF MEDIUM COMPONENTS BY PLACKETT BURMAN DESIGN AND RESPONSE SURFACE METHODOLOGY FOR ENHANCED ANTIOXIDANT ACTIVITY BY XYLARIA FEEJEENSIS HMJAU22039

Objective: To enhance the antioxidant potential of Xylaria feejeensis by statistical optimization. The components of potato dextrose yeast extract (PDYEB) medium: potato infusion, dextrose and yeast extract were investigated to optimize antioxidant activity by Plackett-Burman design (P-B design). The interaction between the critical components of the medium and incubation time was further investigated by Response Surface Method (RSM) and the culture conditions were optimized for enhancing antioxidant activity. Central composite design (CCD) was employed and quadratic response surface model was used to analyze the data using analysis of variance (ANOVA).Methods: The antioxidant potential was measured by 1-1diphenyl-2-picryl-hydrazil (DPPH) radical scavenging assay, nitric oxide (NO) scavenging assay. The antioxidant activity was compared with total phenolic content measured by Folin-Ciocalteau (FC) reagent based assay.Results: Plackett-Burman design revealed dextrose and yeast extract to be the most signiï¬cant components of PDYEB medium (p&lt;.0001). ANOVA analysis showed that the model was highly significant (p&lt;0.0001) for antioxidant activity measured by DPPH, NO scavenging and TPC (total phenolic content). The model was justified by applying the optimized conditions and values of 87.38%, 56.43%, 27.28 mg/g for DPPH, NO scavenging and TPC respectively were obtained. The estimated optimum conditions of the variables for the antioxidant activity and phenolic content are 12.72g of dextrose, 1.67g yeast extract, and incubation period of 23.43 d.Conclusion: The study highlights the importance of various components of the medium and the physiochemical limitations in antioxidant potential and phenol production of the fungal strain.Â

Processing Images from the Zwicky Transient Facility

The Zwicky Transient Facility is a new robotic-observing program, in which a newly engineered 600-MP digital camera with a pioneeringly large field of view, 47~square degrees, will be installed into the 48-inch Samuel Oschin Telescope at the Palomar Observatory. The camera will generate $\sim 1$~petabyte of raw image data over three years of operations. In parallel related work, new hardware and software systems are being developed to process these data in real time and build a long-term archive for the processed products. The first public release of archived products is planned for early 2019, which will include processed images and astronomical-source catalogs of the northern sky in the $g$ and $r$ bands. Source catalogs based on two different methods will be generated for the archive: aperture photometry and point-spread-function fitting.Comment: 6 pages, 4 figures, submitted to RTSRE Proceedings (www.rtsre.org

On Machine-Learned Classification of Variable Stars with Sparse and Noisy Time-Series Data

With the coming data deluge from synoptic surveys, there is a growing need for frameworks that can quickly and automatically produce calibrated classification probabilities for newly-observed variables based on a small number of time-series measurements. In this paper, we introduce a methodology for variable-star classification, drawing from modern machine-learning techniques. We describe how to homogenize the information gleaned from light curves by selection and computation of real-numbered metrics ("feature"), detail methods to robustly estimate periodic light-curve features, introduce tree-ensemble methods for accurate variable star classification, and show how to rigorously evaluate the classification results using cross validation. On a 25-class data set of 1542 well-studied variable stars, we achieve a 22.8% overall classification error using the random forest classifier; this represents a 24% improvement over the best previous classifier on these data. This methodology is effective for identifying samples of specific science classes: for pulsational variables used in Milky Way tomography we obtain a discovery efficiency of 98.2% and for eclipsing systems we find an efficiency of 99.1%, both at 95% purity. We show that the random forest (RF) classifier is superior to other machine-learned methods in terms of accuracy, speed, and relative immunity to features with no useful class information; the RF classifier can also be used to estimate the importance of each feature in classification. Additionally, we present the first astronomical use of hierarchical classification methods to incorporate a known class taxonomy in the classifier, which further reduces the catastrophic error rate to 7.8%. Excluding low-amplitude sources, our overall error rate improves to 14%, with a catastrophic error rate of 3.5%.Comment: 23 pages, 9 figure