An invited talk given to an audience interested in using lucky imaging for microlensing studies. I tried to give an overview of where the challenges lie in getting good science data using lucky imaging techniques.
Lucky imaging - Life in the visible after HSTTim Staley
This document discusses using lucky imaging techniques to improve spatial resolution in astronomy. Standard lucky imaging can select the best frames from thousands taken at high speed to achieve near-diffraction limited resolution on ground-based telescopes. When combined with adaptive optics, lucky imaging can further improve resolution and help expand the sky coverage of AO. Potential applications include exoplanet imaging, resolving close binary stars, and probing binarity in globular cluster cores.
MC12K Series Marco lenses for 12K and 16K camerasAlena Verameyeva
MC12K series are macro lenses specifically optimized to work with high resolution line scan cameras with sensor size up to 62 mm. Infinite conjugate lenses, like photographic equipment optics, will offer poor performances when used to observe objects from up close: MC12K series are macro by design, enabling unmatched and uniform optical performances at short working distances.MC12K series are the ideal choice for industrial applications where maximum image resolution is required: solar cells and printed sheets inspection, web inspection or high speed product sorting are just a few examples.
In addition to the standard M72x0.75 mount, MC12K lenses can be easily equipped with any camera mount at no additional cost ensuring wide compatibility with most common linescan cameras.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2021/02/introduction-to-simultaneous-localization-and-mapping-slam-a-presentation-from-skydio/
Gareth Cross, Technical Lead for State Estimation at Skydio, presents the “Introduction to Simultaneous Localization and Mapping (SLAM)” tutorial at the September 2020 Embedded Vision Summit.
This talk provides an introduction to the fundamentals of simultaneous localization and mapping (SLAM). Cross provides foundational knowledge; viewers are not expected to have any prerequisite experience in the field. The talk consists of an introduction to the concept of SLAM, as well as practical design considerations in formulating SLAM problems. Visual inertial odometry is introduced as a motivating example of SLAM, and Cross reviews how the problem is structured and solved.
LAROB: Laser-Guided Underwater Mobile Robot for Reactor Vessel InspectionJobin Jose
The document describes a new laser-guided underwater mobile robot called LAROB that is designed for reactor vessel inspection. LAROB uses lasers and sensors to navigate underwater and inspect reactor vessels in a safer and more efficient manner than previous robots. It is smaller and lighter than prior inspection robots at 40kg, uses magnetic wheels and parallelogram links for movement, and has a long manipulator arm with sensors for inspection. LAROB is controlled automatically by a main computer that generates inspection paths, stores scan data, and produces detailed reports. The new system allows for improved positioning, handling, and results over conventional inspection methods.
ConveyorCAM provides video inspection technology and services to reduce manufacturing costs for clients. It has been in business since 1978 and focuses on scientifically oriented, patented equipment. Services include testing, inspecting, analyzing existing and new conveyor systems. ConveyorCAM uses specialized equipment like video cameras and data loggers to provide detailed inspections of conveyors. The goal is to accurately analyze conveyor status and conditions to prevent breakdowns and production losses.
Laser imageable polymeric film antec 2015 presentation 3-8-2015 (4)Pat Thomas
Laser-imageable polymeric film provides an alternative to conventional printing methods known as "inkless printing". The film contains a thin marking layer with ammonium octamolybdate (AOM) that turns black when exposed to a CO2 laser. Two trials showed that a formulation with 40% AOM, 40% talc, and 9.5% HDPE produced the darkest images with least film distortion. The laser-imageable film has advantages over traditional printing like reduced costs, inventory, and improved efficiency. Future development could reduce costs further and allow for higher speed imaging and thinner films without deformation issues.
This lab report describes testing a cam lobe that was designed virtually using polynomial expansion functions and differential equations, manufactured using CNC machining, and tested on a follower. The cam was tested to evaluate how well it executed the rise-fall and dwell durations. Care was taken to avoid discontinuities in the cam profile. Graphs of the cam's displacement, velocity, and acceleration are presented.
Jcup 3 (2012) Presentation: Lexichem, a new Era. By Ed Cannon Ed Cannon
Lexichem is introducing a new version with improved performance in generating chemical names from structures and vice versa. The new version achieves 98.71% accuracy in "round tripping" structures compared to 60.02% for the previous version. It also offers faster processing speeds. New features include generating names that distinguish stereochemistry, support for ring templates, and the Lexichem Workbench desktop application for structure/name conversion and substructure searching. The performance enhancements and new tools provide chemists with more robust naming and structure conversion capabilities.
Energy Efficient Communications Solutions for Nomadic Nodes within a Wireless...luca-bencini
Presently, wireless sensor networks most challenging applications are related to mobile sinks or even nodes, monitoring and intervening in critical areas. To this end, proper communications protocols design plays a crucial role in fulfilling user requirements. In the following, the benefits of adopting directive antennas, both in terms of energy saving and targets tracking are presented by integrating this feature into a novel MAC protocol (MD-STAR). Simulation results are also deeply provided, underlining higher performance of MD-STAR with respect to existing solutions, for different directive main lobe width and node density values.
Multi-channel Detector Readout Integrated Circuits with ADCs for X-ray and Ga...Gunnar Maehlum
The document describes a family of multi-channel readout integrated circuits (ROICs) developed for X-ray and gamma-ray spectroscopy in space applications. The ROICs called VATAs integrate pre-amplifiers, pulse shaping circuits, discriminators, and 10-bit analog-to-digital converters for each of their 32-64 channels. They deliver digital pulse amplitudes and pixel addresses with low power consumption of 0.2-1.3 mW per channel. The VATAs are being used on several space missions including Astro-H, BepiColombo, and FOXSI to perform radiation detection and spectroscopy of high-energy photons. Test results show the ASICs achieve good energy resolution and low
Realtime pothole detection system using improved CNN Modelsnithinsai2992
The document summarizes work on a real-time pothole detection system using improved CNN models. It discusses using the YOLOv5 model for pothole detection and training YOLOv5m6, YOLOv5s6, and YOLOv5n6 models on a dataset, achieving mAP scores of 80.8%, 82.2%, and 82.5% respectively. It also proposes further improving the system through techniques like better image processing during nighttime and enhancing detection of distant objects.
CMC Laboratories was founded in 2003 and provides technical services focused on advanced materials for the electronics industry. It has over 200 clients ranging from startups to Fortune 100 companies. The document discusses CMC's technical expertise, business focus in materials analysis, electrical/thermal testing, and technology development. It provides examples of recent projects supporting medical device, optoelectronics, telecommunications, and advanced materials manufacturers.
Zuxin Qin has over 10 years of experience as a lead analog designer specializing in analog, mixed-signal, and high voltage integrated circuits. He has a Ph.D. in Electrical and Electronic Engineering and has participated in the design of over 30 ICs that have achieved commercial production. His responsibilities have included project management, system design, circuit design, simulation, and guidance of junior designers. He has extensive experience with various fabrication processes and has designed various circuit blocks for applications such as power management, telecommunications, and RFID.
There are lots of reasons to decommission a data centre.
Perhaps you’re closing down an office? Or saving money by outsourcing your Disaster Recovery? Maybe your hardware is reaching end-of-life and you’re moving to the cloud?
But It’s not an easy project. It can take longer than expected, eating into cost-savings and brings an increased risk of service-interruption.
Key takeaways:
• A checklist for Discovery, Implementation and Disposal stages
• How to create an accurate budget and timetable
• Choosing between a phased or ‘big bang’ approach
Which transient when? - A utility function for transient follow-up schedulingTim Staley
Next-generation astronomical facilities such as the LSST and the SKA will be game-changers, allowing us to observe the entire southern sky and track changing sources in near real-time. Keeping up with their alert-streams represents a significant challenge - how do we make the most of our limited telescope resources to follow up 100000 sources per night?
The biggest problem here is classification - we want to find the really interesting transients and spend our time watching those. However, classification based on the initial survey data can only get you so far - we'll need to use robotic follow-up telescopes for rapid-response observations, to give us more information on the most promising targets. To get the most science done, we need to be smart about scheduling that follow-up.
We're exploring use of active learning algorithms (AKA Bayesian Decision Theory) to solve this problem, building a framework that allows for iterative refinement of a probabilistic classification state. Because there are no algorithms that fit this problem 'out-of-the-box', we've built our own analysis framework using the emcee and PyMultiNest packages to power the underlying Bayesian inference. I'll give an overview of how our proposed system fits into the wider context of an automated astronomy ecosystem, then give a gentle introduction to Bayesian Decision Theory and how it can be applied to this problem.
How to build a TraP: An image-plane transient-discovery toolTim Staley
There are three main points summarized:
1. There are many interesting slow radio transients that could be detected through imaging surveys like accretion flares, orphan gamma-ray bursts, and flare stars.
2. Radio surveys are increasing in sensitivity and field of view by orders of magnitude with instruments like LOFAR, enabling the detection of more rare transient events.
3. TraP is a transient detection pipeline that works by extracting sources from radio images, matching to known sources, identifying new bright sources, analyzing light curves, and making the results accessible through a user-friendly web interface.
From gamma-ray to radio: Multi-wavelength follow-up in the first five minutesTim Staley
This document summarizes recent work on fast radio follow-up of transient sources and multi-wavelength classification. It discusses three key areas: 1) examples of fast radio follow-up of gamma-ray bursts and stellar flares, 2) using radio and optical measurements to classify transients, and 3) the need to automate "transient triage" to efficiently prioritize follow-up observations across different facilities. The presenter argues that distributing information about new transients via VOEvents can help the community automate real-time classification and prioritization of targets.
Tunable algorithms for transient follow-upTim Staley
This document discusses an approach for optimizing follow-up observations of astronomical transients using Bayesian decision theory. The approach uses transient lightcurve models, telescope noise models, and prior information to calculate the information content of potential future observations. Observations are prioritized based on their expected information content to efficiently classify transients. The presenter outlines the necessary components for a software system to implement this approach, including lightcurve generation, data fitting, calculating confusion matrices, and an observation scheduler. Future work involves integrating these components and testing the system in realistic simulations.
A brief introduction to version control systemsTim Staley
This is a lunchtime talk I gave to the Southampton astronomy department. The aim was to make them aware of version control systems and when they might need to use them.
Training your astronomy robots to work as a teamTim Staley
I present a case that the astronomy community is missing a part of the puzzle for the next era of automated big-survey astronomy: we currently have very little published work on target prioritization and optimized observation scheduling. I discuss the sociological issues surrounding the sort of open collaboration needed to make optimal use of globally distributed observatories, and show some preliminary work on generally-applicable classification methods.
Infrastructure for Tracking Information Flow from Social Media to U.S. TV New...Himarsha Jayanetti
This study examines the intersection between social media and mainstream television (TV) news with an aim to understand how social media content amplifies its impact through TV broadcasts. While many studies emphasize social media as a primary platform for information dissemination, they often underestimate its total influence by focusing solely on interactions within the platform. This research examines instances where social media posts gain prominence on TV broadcasts, reaching new audiences and prompting public discourse. By using TV news closed captions, on-screen text recognition, and social media logo detection, we analyze how social media is referenced in TV news.
The human eye is a complex organ responsible for vision, composed of various structures working together to capture and process light into images. The key components include the sclera, cornea, iris, pupil, lens, retina, optic nerve, and various fluids like aqueous and vitreous humor. The eye is divided into three main layers: the fibrous layer (sclera and cornea), the vascular layer (uvea, including the choroid, ciliary body, and iris), and the neural layer (retina).
Here's a more detailed look at the eye's anatomy:
1. Outer Layer (Fibrous Layer):
Sclera:
The tough, white outer layer that provides shape and protection to the eye.
Cornea:
The transparent, clear front part of the eye that helps focus light entering the eye.
2. Middle Layer (Vascular Layer/Uvea):
Choroid:
A layer of blood vessels located between the retina and the sclera, providing oxygen and nourishment to the outer retina.
Ciliary Body:
A ring of tissue behind the iris that produces aqueous humor and controls the shape of the lens for focusing.
Iris:
The colored part of the eye that controls the size of the pupil, regulating the amount of light entering the eye.
Pupil:
The black opening in the center of the iris that allows light to enter the eye.
3. Inner Layer (Neural Layer):
Retina:
The light-sensitive layer at the back of the eye that converts light into electrical signals that are sent to the brain via the optic nerve.
Optic Nerve:
A bundle of nerve fibers that carries visual signals from the retina to the brain.
4. Other Important Structures:
Lens:
A transparent, flexible structure behind the iris that focuses light onto the retina.
Aqueous Humor:
A clear, watery fluid that fills the space between the cornea and the lens, providing nourishment and maintaining eye shape.
Vitreous Humor:
A clear, gel-like substance that fills the space between the lens and the retina, helping maintain eye shape.
Macula:
A small area in the center of the retina responsible for sharp, central vision.
Fovea:
The central part of the macula with the highest concentration of cone cells, providing the sharpest vision.
These structures work together to allow us to see, with the light entering the eye being focused by the cornea and lens onto the retina, where it is converted into electrical signals that are transmitted to the brain for interpretation.
he eye sits in a protective bony socket called the orbit. Six extraocular muscles in the orbit are attached to the eye. These muscles move the eye up and down, side to side, and rotate the eye.
The extraocular muscles are attached to the white part of the eye called the sclera. This is a strong layer of tissue that covers nearly the entire surface of the eyeball.he layers of the tear film keep the front of the eye lubricated.
Tears lubricate the eye and are made up of three layers. These three layers together are called the tear film. The mucous layer is made by the conjunctiva. The watery part of the tears is made by the lacrimal gland
Body temperature_chemical thermogenesis_hypothermia_hypothermiaMetabolic acti...muralinath2
Homeothermic animals, poikilothermic animals, metabolic activities, muscular activities, radiation of heat from environment, shivering, brown fat tissue, temperature, cinduction, convection, radiation, evaporation, panting, chemical thermogenesis, hyper pyrexia, hypothermia, second law of thermodynamics, mild hypothrtmia, moderate hypothermia, severe hypothertmia, low-grade fever, moderate=grade fever, high-grade fever, heat loss center, heat gain center
Energy Efficient Communications Solutions for Nomadic Nodes within a Wireless...luca-bencini
Presently, wireless sensor networks most challenging applications are related to mobile sinks or even nodes, monitoring and intervening in critical areas. To this end, proper communications protocols design plays a crucial role in fulfilling user requirements. In the following, the benefits of adopting directive antennas, both in terms of energy saving and targets tracking are presented by integrating this feature into a novel MAC protocol (MD-STAR). Simulation results are also deeply provided, underlining higher performance of MD-STAR with respect to existing solutions, for different directive main lobe width and node density values.
Multi-channel Detector Readout Integrated Circuits with ADCs for X-ray and Ga...Gunnar Maehlum
The document describes a family of multi-channel readout integrated circuits (ROICs) developed for X-ray and gamma-ray spectroscopy in space applications. The ROICs called VATAs integrate pre-amplifiers, pulse shaping circuits, discriminators, and 10-bit analog-to-digital converters for each of their 32-64 channels. They deliver digital pulse amplitudes and pixel addresses with low power consumption of 0.2-1.3 mW per channel. The VATAs are being used on several space missions including Astro-H, BepiColombo, and FOXSI to perform radiation detection and spectroscopy of high-energy photons. Test results show the ASICs achieve good energy resolution and low
Realtime pothole detection system using improved CNN Modelsnithinsai2992
The document summarizes work on a real-time pothole detection system using improved CNN models. It discusses using the YOLOv5 model for pothole detection and training YOLOv5m6, YOLOv5s6, and YOLOv5n6 models on a dataset, achieving mAP scores of 80.8%, 82.2%, and 82.5% respectively. It also proposes further improving the system through techniques like better image processing during nighttime and enhancing detection of distant objects.
CMC Laboratories was founded in 2003 and provides technical services focused on advanced materials for the electronics industry. It has over 200 clients ranging from startups to Fortune 100 companies. The document discusses CMC's technical expertise, business focus in materials analysis, electrical/thermal testing, and technology development. It provides examples of recent projects supporting medical device, optoelectronics, telecommunications, and advanced materials manufacturers.
Zuxin Qin has over 10 years of experience as a lead analog designer specializing in analog, mixed-signal, and high voltage integrated circuits. He has a Ph.D. in Electrical and Electronic Engineering and has participated in the design of over 30 ICs that have achieved commercial production. His responsibilities have included project management, system design, circuit design, simulation, and guidance of junior designers. He has extensive experience with various fabrication processes and has designed various circuit blocks for applications such as power management, telecommunications, and RFID.
There are lots of reasons to decommission a data centre.
Perhaps you’re closing down an office? Or saving money by outsourcing your Disaster Recovery? Maybe your hardware is reaching end-of-life and you’re moving to the cloud?
But It’s not an easy project. It can take longer than expected, eating into cost-savings and brings an increased risk of service-interruption.
Key takeaways:
• A checklist for Discovery, Implementation and Disposal stages
• How to create an accurate budget and timetable
• Choosing between a phased or ‘big bang’ approach
Which transient when? - A utility function for transient follow-up schedulingTim Staley
Next-generation astronomical facilities such as the LSST and the SKA will be game-changers, allowing us to observe the entire southern sky and track changing sources in near real-time. Keeping up with their alert-streams represents a significant challenge - how do we make the most of our limited telescope resources to follow up 100000 sources per night?
The biggest problem here is classification - we want to find the really interesting transients and spend our time watching those. However, classification based on the initial survey data can only get you so far - we'll need to use robotic follow-up telescopes for rapid-response observations, to give us more information on the most promising targets. To get the most science done, we need to be smart about scheduling that follow-up.
We're exploring use of active learning algorithms (AKA Bayesian Decision Theory) to solve this problem, building a framework that allows for iterative refinement of a probabilistic classification state. Because there are no algorithms that fit this problem 'out-of-the-box', we've built our own analysis framework using the emcee and PyMultiNest packages to power the underlying Bayesian inference. I'll give an overview of how our proposed system fits into the wider context of an automated astronomy ecosystem, then give a gentle introduction to Bayesian Decision Theory and how it can be applied to this problem.
How to build a TraP: An image-plane transient-discovery toolTim Staley
There are three main points summarized:
1. There are many interesting slow radio transients that could be detected through imaging surveys like accretion flares, orphan gamma-ray bursts, and flare stars.
2. Radio surveys are increasing in sensitivity and field of view by orders of magnitude with instruments like LOFAR, enabling the detection of more rare transient events.
3. TraP is a transient detection pipeline that works by extracting sources from radio images, matching to known sources, identifying new bright sources, analyzing light curves, and making the results accessible through a user-friendly web interface.
From gamma-ray to radio: Multi-wavelength follow-up in the first five minutesTim Staley
This document summarizes recent work on fast radio follow-up of transient sources and multi-wavelength classification. It discusses three key areas: 1) examples of fast radio follow-up of gamma-ray bursts and stellar flares, 2) using radio and optical measurements to classify transients, and 3) the need to automate "transient triage" to efficiently prioritize follow-up observations across different facilities. The presenter argues that distributing information about new transients via VOEvents can help the community automate real-time classification and prioritization of targets.
Tunable algorithms for transient follow-upTim Staley
This document discusses an approach for optimizing follow-up observations of astronomical transients using Bayesian decision theory. The approach uses transient lightcurve models, telescope noise models, and prior information to calculate the information content of potential future observations. Observations are prioritized based on their expected information content to efficiently classify transients. The presenter outlines the necessary components for a software system to implement this approach, including lightcurve generation, data fitting, calculating confusion matrices, and an observation scheduler. Future work involves integrating these components and testing the system in realistic simulations.
A brief introduction to version control systemsTim Staley
This is a lunchtime talk I gave to the Southampton astronomy department. The aim was to make them aware of version control systems and when they might need to use them.
Training your astronomy robots to work as a teamTim Staley
I present a case that the astronomy community is missing a part of the puzzle for the next era of automated big-survey astronomy: we currently have very little published work on target prioritization and optimized observation scheduling. I discuss the sociological issues surrounding the sort of open collaboration needed to make optimal use of globally distributed observatories, and show some preliminary work on generally-applicable classification methods.
Infrastructure for Tracking Information Flow from Social Media to U.S. TV New...Himarsha Jayanetti
This study examines the intersection between social media and mainstream television (TV) news with an aim to understand how social media content amplifies its impact through TV broadcasts. While many studies emphasize social media as a primary platform for information dissemination, they often underestimate its total influence by focusing solely on interactions within the platform. This research examines instances where social media posts gain prominence on TV broadcasts, reaching new audiences and prompting public discourse. By using TV news closed captions, on-screen text recognition, and social media logo detection, we analyze how social media is referenced in TV news.
The human eye is a complex organ responsible for vision, composed of various structures working together to capture and process light into images. The key components include the sclera, cornea, iris, pupil, lens, retina, optic nerve, and various fluids like aqueous and vitreous humor. The eye is divided into three main layers: the fibrous layer (sclera and cornea), the vascular layer (uvea, including the choroid, ciliary body, and iris), and the neural layer (retina).
Here's a more detailed look at the eye's anatomy:
1. Outer Layer (Fibrous Layer):
Sclera:
The tough, white outer layer that provides shape and protection to the eye.
Cornea:
The transparent, clear front part of the eye that helps focus light entering the eye.
2. Middle Layer (Vascular Layer/Uvea):
Choroid:
A layer of blood vessels located between the retina and the sclera, providing oxygen and nourishment to the outer retina.
Ciliary Body:
A ring of tissue behind the iris that produces aqueous humor and controls the shape of the lens for focusing.
Iris:
The colored part of the eye that controls the size of the pupil, regulating the amount of light entering the eye.
Pupil:
The black opening in the center of the iris that allows light to enter the eye.
3. Inner Layer (Neural Layer):
Retina:
The light-sensitive layer at the back of the eye that converts light into electrical signals that are sent to the brain via the optic nerve.
Optic Nerve:
A bundle of nerve fibers that carries visual signals from the retina to the brain.
4. Other Important Structures:
Lens:
A transparent, flexible structure behind the iris that focuses light onto the retina.
Aqueous Humor:
A clear, watery fluid that fills the space between the cornea and the lens, providing nourishment and maintaining eye shape.
Vitreous Humor:
A clear, gel-like substance that fills the space between the lens and the retina, helping maintain eye shape.
Macula:
A small area in the center of the retina responsible for sharp, central vision.
Fovea:
The central part of the macula with the highest concentration of cone cells, providing the sharpest vision.
These structures work together to allow us to see, with the light entering the eye being focused by the cornea and lens onto the retina, where it is converted into electrical signals that are transmitted to the brain for interpretation.
he eye sits in a protective bony socket called the orbit. Six extraocular muscles in the orbit are attached to the eye. These muscles move the eye up and down, side to side, and rotate the eye.
The extraocular muscles are attached to the white part of the eye called the sclera. This is a strong layer of tissue that covers nearly the entire surface of the eyeball.he layers of the tear film keep the front of the eye lubricated.
Tears lubricate the eye and are made up of three layers. These three layers together are called the tear film. The mucous layer is made by the conjunctiva. The watery part of the tears is made by the lacrimal gland
Body temperature_chemical thermogenesis_hypothermia_hypothermiaMetabolic acti...muralinath2
Homeothermic animals, poikilothermic animals, metabolic activities, muscular activities, radiation of heat from environment, shivering, brown fat tissue, temperature, cinduction, convection, radiation, evaporation, panting, chemical thermogenesis, hyper pyrexia, hypothermia, second law of thermodynamics, mild hypothrtmia, moderate hypothermia, severe hypothertmia, low-grade fever, moderate=grade fever, high-grade fever, heat loss center, heat gain center
VERMICOMPOSTING A STEP TOWARDS SUSTAINABILITY.pptxhipachi8
Vermicomposting: A sustainable practice converting organic waste into nutrient-rich fertilizer using worms, promoting eco-friendly agriculture, reducing waste, and supporting environmentally conscious gardening and farming practices naturally.
Structure formation with primordial black holes: collisional dynamics, binari...Sérgio Sacani
Primordial black holes (PBHs) could compose the dark matter content of the Universe. We present the first simulations of cosmological structure formation with PBH dark matter that consistently include collisional few-body effects, post-Newtonian orbit corrections, orbital decay due to gravitational wave emission, and black-hole mergers. We carefully construct initial conditions by considering the evolution during radiation domination as well as early-forming binary systems. We identify numerous dynamical effects due to the collisional nature of PBH dark matter, including evolution of the internal structures of PBH halos and the formation of a hot component of PBHs. We also study the properties of the emergent population of PBH binary systems, distinguishing those that form at primordial times from those that form during the nonlinear structure formation process. These results will be crucial to sharpen constraints on the PBH scenario derived from observational constraints on the gravitational wave background. Even under conservative assumptions, the gravitational radiation emitted over the course of the simulation appears to exceed current limits from ground-based experiments, but this depends on the evolution of the gravitational wave spectrum and PBH merger rate toward lower redshifts.
Protective function of skin, protection from mechanical blow, UV rays, regulation of water and electrolyte balance, absorptive activity, secretory activity, excretory activity, storage activity, synthetic activity, sensory activity, role of sweat glands regarding heat loss, cutaneous receptors and stratum corneum
Introduction to Mobile Forensics Part 1.pptxNivya George
Ad
A user's guide to lucky imaging
1. A User’s Guide to Lucky
Imaging
(With a focus on mosaic cameras)
Tim Staley,
Craig Mackay & the Cambridge LI Group
RS Lucky Imaging Meeting
Chicheley Hall, April 2013
WWW: timstaley.co.uk
2. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
OUTLINE
WHY MOSAIC?
LUCKYCAM 2009 & THE NOT
REDUCTION
PERFORMANCE
TRADEOFFS, RECOMMENDATIONS
3. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
OUTLINE
WHY MOSAIC?
LUCKYCAM 2009 & THE NOT
REDUCTION
PERFORMANCE
TRADEOFFS, RECOMMENDATIONS
4. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
WHY MOSAIC?
Spatial sampling
High spatial resolution surveys require
many pixels on sky.
At the frame rates lucky imaging requires,
it becomes difficult to run a 1Kx1K CCD
without getting swamped by readout noise.
Unfortunately, current generation of
EMCCDs cannot be abutted (though this is
changing).
5. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
WHY MOSAIC?
Dynamic range
Independent gain control is a bonus.
Dynamic range on a single EMCCD is
limited to about 10 magnitudes by electron
well depth in multiplication register.
(Disclaimer: back of envelope calculation.)
Can’t guide well on a saturated star.
Ok if GS is Mag ≈ 16. Potentially
problematic if GS is Mag ≈ 10.
6. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
WHY MOSAIC?
But...
Significant hardware challenge – alignment,
synchronisation, multiple control systems...
High data rates.
More calibration.
(See below...)
7. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
OUTLINE
WHY MOSAIC?
LUCKYCAM 2009 & THE NOT
REDUCTION
PERFORMANCE
TRADEOFFS, RECOMMENDATIONS
8. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
A HARDWARE IMPLEMENTATION
9. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
A HARDWARE IMPLEMENTATION
10. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
A HARDWARE IMPLEMENTATION
11. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
A HARDWARE IMPLEMENTATION
12. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
FIELD TEST
13. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
FIELD TEST
Visitor Instrument @ 2.5m Nordic
Optical Telescope
Awarded 8 nights on sky, + technical time
for setup and take-down.
Off we went to La Palma.
14. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
FIELD TEST
15. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
FIELD TEST
16. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
RESULTS?
Minor software
glitches slowed
progress for first
couple of days.
Overall, system
performed very well.
Some good weather.
Now, we hads lots of
data to reduce
(∼ 8TB).
17. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
OUTLINE
WHY MOSAIC?
LUCKYCAM 2009 & THE NOT
REDUCTION
PERFORMANCE
TRADEOFFS, RECOMMENDATIONS
18. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
PHOTOMETRIC CALIBRATION
Specific to EMCCDs:
Accurate on-sky gain calibration via pixel
histograms is desirable (CCDs
independently adjustable).
Histogram based bias map estimation is the
most robust way to separate bias pedestal
and internally generated signal (i.e. dark
current + clock induced charge).
19. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
PHOTOMETRIC CALIBRATION
20. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
PIPELINE OVERVIEW
Custom pipeline written in C++.
A 2 stage process, comprising evaluation
and reduction. Almost always I/O limited
(∼60–80MB/sec).
Make use of thread-friendly STL based
containers and Intel Thread Building Blocks
library to implement a ‘pipeline of filters’
pattern.
21. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
PIPELINE OVERVIEW
First, frame evaluation:
Load frame, debias, gain normalize,
subtract internally generated signal (DC +
CIC).
Interpolate GS region, cross-correlate with
Airy core to estimate tip-tilt and (relative)
Strehl.
Save position and quality estimates to file.
22. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
PIPELINE OVERVIEW
Second, selection and recombination:
Sort frames by estimated quality, selecting
according to user-criteria.
Load and calibrate, optionally applying
thresholding to a copy.
Drizzle to create final image, writing to file
whenever a user-selection criteria is met.
23. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
PIPELINE OVERVIEW
24. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
PIPELINE OVERVIEW
25. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
ASTROMETRIC CALIBRATION
How do you calibrate a high-resolution mosaic
detector?
26. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
ASTROMETRIC CALIBRATION
How do you calibrate a high-resolution mosaic
detector?
Using standard binaries would take at least
(2n-1) pointings per focal lens.
So we observe a crowded field.
27. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
ASTROMETRIC CALIBRATION
How do you calibrate a high-resolution mosaic
detector?
Using standard binaries would take at least
(2n-1) pointings per focal lens.
So we observe a crowded field.
But: standard catalogues (2MASS, USNO)
do not have the resolution (2MASS pixels
are 2 arcseconds width).
28. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
ASTROMETRIC CALIBRATION
How do you calibrate a high-resolution mosaic
detector?
Using standard binaries would take at least
(2n-1) pointings per focal lens.
So we observe a crowded field.
But: standard catalogues (2MASS, USNO)
do not have the resolution (2MASS pixels
are 2 arcseconds width).
Solution: Create own catalogues from
HST/WFPC fields, cross match using
custom code.
29. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
ASTROMETRIC CALIBRATION
30. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
ASTROMETRIC CALIBRATION
31. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
OUTLINE
WHY MOSAIC?
LUCKYCAM 2009 & THE NOT
REDUCTION
PERFORMANCE
TRADEOFFS, RECOMMENDATIONS
32. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
What resolution improvement do we
obtain?
33. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
PSF PROFILES
1.0 0.5 0.0 0.5 1.0
Radius in arcseconds
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0.035
0.040
Strehlratioatpeak
5% selection
100% selection
Seeing profile
How does this vary as we move away from GS?
34. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
FWHM ACROSS THE FIELD
35. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
FWHM ACROSS THE FIELD
0 10 20 30 40 50 60
Distance from guide star [arcsec]
100
200
300
400
500
FWHM[mas]
10% selection
100% selection
Seeing FWHM
36. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
How faint can we go?
37. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
FAINT LIMITS: GUIDE STARS
102 103
Source flux (photo-electrons per exposure)
0.00
0.05
0.10
0.15
0.20
0.25
Strehlratio
Normalised Airy reference CC
Standard Airy reference CC
Brightest pixel
Ideal case (upper horizontal)
Seeing disc (lower horizontal)
(Simulated)
38. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
FAINT LIMITS: GUIDE STARS
A real world example: the Einstein Cross.
(Left: HST. Right: LI. GS Mi ≈ 17)
40. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
FAINT LIMITS: SCIENCE TARGETS
3C 405 (Cygnus-A)
Left: HST. Right: LI.
One hour observation, 50% selection, ∼0.5”
seeing.
Galaxy approx 30” from guide star.
Weakly detect stars of estimated Mi ≥ 23.
Can do better with latest hardware.
41. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
OUTLINE
WHY MOSAIC?
LUCKYCAM 2009 & THE NOT
REDUCTION
PERFORMANCE
TRADEOFFS, RECOMMENDATIONS
42. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
OPTIMAL PIXEL SIZE AND
FRAMERATE TRADE-OFFS
Spatial sampling vs detector noise.
(Old issue with a new twist: Drizzle many
frames with subpixel offsets.)
Longer exposures for faint targets vs
atmospheric blurring above coherence time.
Data rates.
43. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
OBSERVATION PLANNING
Will there be a close / bright enough guide
star?
Will de-saturating my guide star ruin my
faint limit? (Can I position the mosaic to
avoid this?)
What resolution / magnitude depth do I
need to achieve, and what weather
conditions / selection criteria does this
require?
(For very bright sources:) Would I be better
off turning off the EM amplifier altogether?
44. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
ASTROMETRIC CALIBRATION
This is non-trivial!
Worth investigating:
http://www.astromatic.net/
software/scamp
(Cannot read custom catalogues when I last
checked, but this is probably an easy
feature to add.)
45. WHY MOSAIC? LUCKYCAM 2009 & THE NOT REDUCTION PERFORMANCE TRADEOFFS, RECOMMENDATIONS
OPEN QUESTIONS / RESEARCH
PROBLEMS
Use of multiple guide stars.
Crowded field photometry with a slowly
varying PSF.