The Critical Importance of Data Reduction Calibrations In the Interpretability of S-type Asteroid Spectra Michael J. Gaffey Space Studies Department University.

Slides:



Advertisements
Similar presentations
Error Characterization of the Alpha Residuals Emissivity Extraction Technique Michael C. Baglivio, Dr. John Schott, Scott Brown Center for Imaging Science.
Advertisements

Yu. I. BARANOV and W. J. LAFFERTY Optical Technology Division Optical Technology Division National Institute of Standards and Technology, Gaithersburg,
C. Beichman, Dimitra Touli, Gautam Vasisht, Roger Smith Tom Greene
Echelle Spectroscopy Dr Ray Stathakis, AAO. What is it? n Echelle spectroscopy is used to observe single objects at high spectral detail. n The spectrum.
Spectroscopic Data ASTR 3010 Lecture 15 Textbook Ch.11.
Clouds and the Earth’s Radiant Energy System NASA Langley Research Center / Atmospheric Sciences Methodology to compare GERB- CERES filtered radiances.
832 Karin Shows No Rotational Spectral Variations Clark R. Chapman, B. Enke, W.J. Merline, D. Nesvorný, P. Tamblyn, and E.F. Young Southwest Research Institute.
Sergey Kucheryavski Raman spectroscopy Acquisition, preprocessing and analysis of spectra.
Stellar Continua How do we measure stellar continua? How precisely can we measure them? What are the units? What can we learn from the continuum? –Temperature.
The Earthshine Spectrum in the Near Infrared M. Turnbull 1, W. Traub 2, K. Jucks 3, N. Woolf 4, M. Meyer 4, N. Gorlova 4, M. Skrutskie 5, J. Wilson 5 1.
Active Calibration of Cameras: Theory and Implementation Anup Basu Sung Huh CPSC 643 Individual Presentation II March 4 th,
RHESSI/GOES Observations of the Non-flaring Sun from 2002 to J. McTiernan SSL/UCB.
Observational Astrophysics II: May-June, Observational Astrophysics II (L2)
The Calibration Process
PSF Reconstruction: Part I The PSF “Core” Primary Goal: Derive PSFs for point source detection and PSF fitting photometry. Secondary Goal: Derive PSFs.
Taxonomy of Small Bodies AS3141 Benda Kecil dalam Tata Surya Prodi Astronomi 2007/2008 B. Dermawan.
Naoyuki Tamura (University of Durham) Expected Performance of FMOS ~ Estimation with Spectrum Simulator ~ Introduction of simulators  Examples of calculations.
1/2555 สมศักดิ์ ศิวดำรงพงศ์
0. To first order, the instrument is working very well ! 1.Evolution of the IR detector with time 2.Stability of the L channel 3.Saturation 4.Linearity.
Optical Spectroscopy of Distant Red Galaxies Stijn Wuyts 1, Pieter van Dokkum 2 and Marijn Franx 1 1 Leiden Observatory, P.O. Box 9513, 2300RA Leiden,
Blue: Histogram of normalised deviation from “true” value; Red: Gaussian fit to histogram Presented at ESA Hyperspectral Workshop 2010, March 16-19, Frascati,
SALTLIB Proposal for a Stellar Spectral Library using H. P. Singh, Department of Physics & Astrophysics University of Delhi, Delhi – ,
15 October Observational Astronomy Direct imaging Photometry Kitchin pp ,
Subaru HDS Transmission Spectroscopy of the Transiting Extrasolar Planet HD b The University of Tokyo Norio Narita collaborators Yasushi Suto, Joshua.
Science with the new HST after SM4 WFC3 slitless spectroscopy Harald Kuntschner Martin Kümmel, Jeremy R. Walsh (ST-ECF) WFC3-team at STScI and NASA.
S. Erard et al. — Workshop 3e zone, Nantes, janvier 2007 Analysis of spectral features in TNO and asteroid spectra S. Erard, D. Despan, F. Merlin.
Estimating Water Optical Properties, Water Depth and Bottom Albedo Using High Resolution Satellite Imagery for Coastal Habitat Mapping S. C. Liew #, P.
The Semivariogram in Remote Sensing: An Introduction P. J. Curran, Remote Sensing of Environment 24: (1988). Presented by Dahl Winters Geog 577,
Physics of the Atmosphere II
Data products of GuoShouJing telescope(LAMOST) pipeline and current problems LUO LAMOST Workshop.
1-D Flat Fields for COS G130M and G160M Tom Ake TIPS 17 June 2010.
COS signal to noise capabilities Limitation of COS S/N No good 2-D flat available. Fixed pattern noise dominates COS spectra. An uncalibrated COS spectrum.
MIRI Dither Patterns Christine H Chen. Dithering Goals 1.Mitigate the effect of bad pixels 2.Obtain sub-pixel sampling 3.Self-calibrate data if changing.
Observing Strategies at cm wavelengths Making good decisions Jessica Chapman Synthesis Workshop May 2003.
SNAP Calibration Program Steps to Spectrophotometric Calibration The SNAP (Supernova / Acceleration Probe) mission’s primary science.
TIPS COS Status: SMOV update III STScI/CU COS Team 17 September 2009.
Stellar Continua How do we measure stellar continua?
Extracting binary signals from microarray time-course data Debashis Sahoo 1, David L. Dill 2, Rob Tibshirani 3 and Sylvia K. Plevritis 4 1 Department of.
The University of Tokyo Norio Narita
Multiobject Spectroscopy: Preparing and performing Michael Balogh University of Durham.
MOS Data Reduction Michael Balogh University of Durham.
NICMOS Calibration Challenges in the Ultra Deep Field Rodger Thompson Steward Observatory University of Arizona.
HLA WFPC2 Source List Photometric Quality Checks Version: August 25, 2008 Brad Whitmore 1.Introduction 2.Comparison with Ground-based Stetson Photometry.
Practical session on calibrators Euro Summer School Active Galactic Nuclei at the highest angular resolution: theory and observations Torun, Poland 27.
` Background photo courtesy of NOAO. Taken with LBNL p-channel CCD with extended red sensitivity. Pixel Area Variation in CCDs and Implications for Precision.
Atmospheric extinction Suppose that Earth’s atmosphere has mass absorption coefficient  at wavelength. If f 0 is flux of incoming beam above atmosphere,
NHSC HIFI DP workshop Caltech, 7-9 February page 1 Spurs in HIFI data.
Photometry and Astrometry: Bright Point Sources May 16, 2006 Cullen Blake.
Coronal Hole recognition by He 1083 nm imaging spectroscopy O. Malanushenko (NSO) and H.P.Jones (NASA's GSFC) Tucson, Arizona, USA Solar Image Recognition.
Date of download: 6/22/2016 Copyright © 2016 SPIE. All rights reserved. Glucose sensor architecture. The lamp provides broadband electromagnetic radiation.
Compositional And Physical Characterizations Of NEOs From VNIR Spectroscopy Michael J. Gaffey 1,3 Paul A. Abell 2,3 Paul S. Hardersen 1,3 1 Department.
SINFONI data reduction using the ESO pipeline. Instrument design and its impact on the data (I) integral field spectrometer using mirrors brickwall pattern.
Single Object & Time Series Spectroscopy with JWST NIRCam
Institute of Cosmos Sciences - University of Barcelona
JWST NIRCam Time Series Observations
COS FUV Flat Fields and Signal-to-Noise Characteristics
Data Reduction and Analysis Techniques
ESAC 2017 JWST Workshop JWST User Documentation Hands on experience
MODIS Characterization and Support Team Presented By Truman Wilson
UVIS Calibration Update
UVIS Calibration Update
UVIS Calibration Update
Spectroscopy Workshop
Observational Astronomy
Chapter 9 Hypothesis Testing: Single Population
The University of Tokyo Norio Narita
Fig. 3 Near-infrared spectrum of EM at 4.5 days after merger.
Subaru HDS Ground-based Transmission Spectroscopy
Presentation transcript:

The Critical Importance of Data Reduction Calibrations In the Interpretability of S-type Asteroid Spectra Michael J. Gaffey Space Studies Department University of North Dakota

Only briefly -- Extinction Corrections – –Accurate extinction corrections are essential in the NIR Spectra – –Corrections should depend on objective criteria Avoid using adjustable parameters unless there is are independent objective criteria for determining the adjustment factor. “It looks right” is not an adequate criterion.

Preface This presentation uses examples of asteroid spectra in the recent literature.

Preface This presentation uses examples of asteroid spectra in the recent literature. Some of these are used as examples of data with significant problems which effect their interpretability.

Preface This presentation uses examples of asteroid spectra in the recent literature. Some of these are used as examples of data with significant actual or potential problems which effect their interpretability. No citations or references are given to these particular spectra since the intent is not to attack or insult any individuals. – –In most cases, the asteroid identifications have also been omitted.

Preface This presentation uses examples of asteroid spectra in the recent literature. Some of these are used as examples of data with significant actual or potential problems which effect their interpretability. No citations or references are given to these particular spectra since the intent is not to attack or insult any individuals. – –In most cases, the asteroid identifications have also been omitted. The intent is to show the nature of the problems and to discuss the methods to ameliorate these problems.

Unsmoothed, unedited average spectrum. This should be the current expectation for NIR asteroid spectra. Current State-of-the-Art H2OH2O H2OH2O Hardersen et al. (2004) 1459 MagnyaV mag = 15.7

Spectra with Serious Problems in the 1.4 & 1.9  m Telluric Features V mags = Are these problems due to fainter objects or shorter integrations?

Similar 1.4 & 1.9  m problems for a bright asteroid target (Vesta)

Can these problems be ameliorated by smoothing? Smoothing will only work if the “spikiness” is due random noise in the spectra.

0.05 airmass difference Exposure = 60 sec V Mag = 12.4 What is the nature of these variations? Simple Asteroid / Standard Star Ratio

Effects of Channel Shifts Much of the “noise” is actually an interference pattern due to small offsets in the location of the dispersed spectrum onto the detector array. +1 Channel Offset -1 Channel Offset Raw Flux Data

The Pattern Results from the Fine Structure in the Water Vapor Features At this resolution, slight channel offsets produce an interference pattern in the resulting ratios of raw spectra. At much lower resolution there is no problem. At much higher resolution there is no problem.

Correction for extinction using a SPECPR Starpack Extinction coefficients were calculated from standard star observations without correction for channel offsets

Extinction correction with channel offsets Most of the “noise” in the 1.4 & 1.9  m regions was not random, but due to uncorrected channel offsets

Pattern for 1 channel Offset on SpeX Smoothing does not remove this pattern 37 Pt Smooth ~0.15  m Interval 13 Pt Smooth ~0.05  m Interval 25 Pt Smooth ~0.10  m Interval 51 Pt Smooth - Edited ~0.15  m Interval

Effects of Uncorrected 0.5 Channel Offset -0.5 Chan Chan. No Shift A mall offset produces major deviations in Band II

Implications for Analysis Irrespective of the analysis technique Curve matching Gaussian fitting Parameter extraction Interpretations would differ for these spectra.

Effect on Band II Parameters SpectrumBand II AreaBII Center Properly Corrected 3.16 units 1.93  m Uncorrected +0.5 Channel Shift 3.48 units 10% high 1.95  m Uncorrected Channel Shift 3.89 units 23% high 1.82  m Double Band?

Implications The structure of the 1.4 & 1.9  m “noise” is not random. It’s an interference pattern due to slight channel offsets in the spectra. – –Offsets due to instrument flexure – –Offsets due to position of the object in the slit or aperture Fractional channel shifts produce significant spectral effects. Smoothing will not ameliorate this problem!

Effects of Smoothing Smoothing the uncorrected data introduces significant artifacts in the spectral region of the water vapor absorptions. This problem is particularly significant for S- type asteroids which have relatively weak 2  m features. The S-type asteroid used in this example has a relatively strong 2  m feature. For more weakly featured S-asteroids, the effect on the 2  m feature will be more pronounced.

Correction Process The pattern identifies presence of an offset. Channel offset determined for each set of observations relative to some reference set. Offsets are derived by using the steep edge of the 1.4  m atmospheric water vapor feature. Offsets should be established to ~ pixels. Pixel offset corrections are applied to the raw standard star spectra prior to calculation of the extinction coefficients (or their use in ratios). Pixel offset corrections are applied to the raw object spectra prior to extinction corrections.

Conclusions All medium resolution NIR asteroid spectral data should be corrected for channel offsets as the initial reduction step. – –Preferably the offsets should be determined from the data itself. Extinction should use “objective” criteria. – –Standard extinction coefficients adjusted until the spectrum “looks right” should be avoided. Smoothing of spectra should be avoided unless the previous steps have been accomplished.

The Upside -- Asteroid spectra obtained with medium resolution NIR spectrographs must be routinely checked and corrected for channel offsets. The parameters needed to make the offset correction are derivable from the raw data itself. Routines exist to make the corrections as a regular step in data reduction.

In consequence -- It should no longer be acceptable to publish asteroid spectra which exhibit this correctable problem. As reviewers and editors we should assist our colleagues in identifying this problem and ask that they resubmit their manuscripts with the appropriate correction. This will be good for both our colleagues and for asteroid science by providing the best attainable spectra of our subject bodies.